A Joint Longitudinal Model of a Stepped-Care Intervention’s Effects on the Patient Reported Outcomes of Underserved Cancer Patient-Caregiver Dyads

A Joint Longitudinal Model of a Stepped-Care Intervention’s Effects on the Patient Reported Outcomes of Underserved Cancer Patient-Caregiver Dyads | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Joint Longitudinal Model of a Stepped-Care Intervention’s Effects on the Patient Reported Outcomes of Underserved Cancer Patient-Caregiver Dyads Bissilola Nyonyotsi, Mary Sammel, Elizabeth Juarez-Colunga, Heather Smyth, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5249185/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose. The Stepped-Care study was a randomized controlled trial comparing the effects of a behavioral intervention versus usual care on the patient reported outcomes of underserved patients with lung cancer (LC) and head-and-neck cancer (HNC) and on their caregivers. In this study, we examined multivariate responses from patient-caregiver dyads within a longitudinal Latent Actor-Partner Interdependence Model and whether their interconnectedness was associated with the interventions effect on their patient-reported outcomes. Methods. Exploratory Factor Analysis revealed the strongest correlations among the multivariate responses were within patient and caregiver. A joint (patient and caregiver) mixed model was fitted to assess a stepped-care intervention effects compared to usual care on the latent variables for dyads and to estimate the extent of their correlation. Results. Patient reported outcomes were strongly associated (rho=0.45, p<0.001) with the outcomes of their caregivers, but the outcomes were not different between those in the stepped-care intervention and those in the usual care condition. Conclusions. Our findings provide evidence that LC and HNC patient-caregiver dyads are psychologically interdependent and may benefit from behavioral interventions. However, stepped-care interventions that match each individual’s level of psychological needs may not be effective for delivering behavioral treatment to medically underserved LC and HNC patient-caregiver dyads. Cancer underserved patients caregivers dyads depression anxiety Figures Figure 1 Figure 2 Figure 3 Introduction Head and neck cancers (HNC) are a heterogeneous group of malignancies that arise from the squamous epithelium of the oral cavity and pharynx [ 1 ]. A decrease in survival rate is associated with HNC when they occur in conjunction with a second primary lung cancer (LC) diagnosis [ 2 ]. HNC and LC are also associated with intensive treatment regimens, heavy symptom burden, and high risk for treatment failure and mortality, making these malignancies two of the most psychologically distressing types of cancer that affect quality of life for patients [ 3 – 5 ]. Emotional distress (i.e., depression, anxiety) and lower quality of life are further aggravated for medically underserved patients who lack access to healthcare and are often diagnosed at later stages, leading to worse treatment outcomes [ 6 ]. Due to the nature of the disease and its psychological impact on patients, caregivers of LC and HNC patients also experience significant caregiving burden and emotional distress [ 7 – 8 ]. Unfortunately, caregivers are “hidden patients” as their needs and distress often goes unrecognized [ 9 ]. Distress tends to be similar for caregivers and patients among couples coping with cancer [ 10 ]; however, studies with LC and HNC patients’ caregivers suggest that the caregiver is significantly more distressed than the patient [ 11 , 12 ]. Thus, LC and HNC patients and their caregivers can benefit from psychological support to cope with their distress, poor quality of life, and other patient reported outcomes [PROs] such as stress and caregiver burden [ 13 , 14 ]. Stepped-care interventions are a low-cost option for delivering psychological treatment to underserved cancer patients, applying the least intrusive and costly treatment first, with treatment intensity ‘step-up’ if emotional symptoms increase [ 15 ]. Stepped-care interventions are suitable for LC and HNC patients and their caregivers whose levels of emotional distress vary [ 11 , 12 ] because this step or dose-appropriate approach delivers treatment to meet the individual’s level of emotional need [ 16 ]. While psychological interventions delivered to cancer patient-caregiver dyads improve their emotional distress and quality of life [ 17 , 18 ], there is a lack of evidence on whether stepped-care interventions delivered individually to LC and HNC patients and to their caregivers are as effective [ 19 ]. In this study, we determined whether patients and their caregivers PROs covary based on the evidence that cancer patient-caregiver dyad’s distress levels often covary [ 7 , 10 ], although this may differ for LC and HNC patients and their caregivers [ 11 , 12 ]. We used the Actor-Partner Interdependence model [ 20 ] that frames the strength of dyad members’ psychological interdependence based on the influence that members receive from their partners. We augmented the model using an extension of the Latent Actor-Partner Interdependence Model [ 21 ] by aggregating the multivariate outcomes for both the patients and caregivers into separate latent variables. We then extend this model to accommodate longitudinal data. In this dyadic model we estimate and test for the interconnectedness between dyad members by allowing model random effects to be correlated. In addition to testing for the significance of the dyadic relationship, we were interested in the overall effect of the stepped-care intervention on the PROs of dyads. That is, we examined whether the added dyadic correlation provides evidence that an individual delivered stepped-care intervention has an effect on the patients and caregivers PROs (e.g., depression, anxiety, coping, perceived stress, patients’ quality of life, and caregivers’ burden) due to the dyads’ interdependence. Methods Procedures This study used secondary data from the Stepped-Care randomized controlled trial (RCT) to evaluate the effects of a stepped-care intervention compared to usual care on the PROs of underserved patients with LC and HNC and of their caregivers [ 22 ]. Patients were randomized to condition based on a 1:1 ratio. Randomization was stratified by cancer type, recruitment site, and cancer stage. Caregivers were invited to participate in the RCT and if they agreed, they were assigned to the same study condition that their patient was randomly assigned. In the case where patients had multiple caregivers, we selected only one of the patient’s caregivers at random for the secondary analysis. Intervention We tailored the stepped-care intervention to meet the medical challenges and the psychological needs of patients and their caregivers. Prior and through the duration of the study, we consulted a committee of 8 oncology providers with expertise in these cancers and a patient and caregiver stakeholders (PCS) committee of 9 medically underserved (i.e., low-income, uninsured, underinsured) patients (4 diagnosed with HNC and 5 with LC) and their 9 caregivers. Providers advised us on LC and HNC patients’ unique medical challenges and their potential impact on mental health, while PCS advised us on the unique and common mental health needs that patients and caregivers experience. Moreover, we culturally tailored the intervention for Spanish-speaking patients by consulting these patients (3 patients and 3 caregivers from out PCS), made it accessible vis telehealth delivery, and designed it to address the barriers that medically disadvantaged individuals face. Patients and caregivers assigned to the intervention arm received a baseline survey to assess their level of emotional distress using the Patient-Reported Outcomes Measurement Information System (PROMIS) scales [ 23 ]. Subsequently, each member of the dyad participated in the intervention independently at their individual treatment step, consistent with the aims of the study to meet the patients and caregivers’ individual emotional needs. We use the stepped-care conceptual framework for assigning and progressing participants to a treatment step based on their level of emotional distress [ 24 ]. The four steps that matched levels of distress included watchful waiting (no symptoms, PROMIS score < 50 ) [25, self-help guide (mild symptoms, PROMIS score 50–59 ) [ 25 ], coping skills training (moderate symptoms, PROMIS score 60–69 ) [ 25 ], and eight sessions of cognitive behavioral therapy (CBT, severe symptoms, PROMIS score > 70 ) [ 25 ]. The distribution of treatment received is summarized for both patients and caregivers in Supplemental Information Table S1 and S2. We monitored intervention delivery by videotaping 25% of the sessions and used a rater checklist to achieve 90% intervention-fidelity. The comparator to the intervention was usual care, which was enhanced for standardization across sites with printed mental health resources (e.g., website links), in addition to the usual care mental health resources (e.g., support groups) at the treatment sites. Measures The PROs were measured at four assessment points (time): at baseline, 6 weeks, 12 weeks, and 24 weeks. The outcomes measured for patients included depression, anxiety, coping self-efficacy, quality of life, and perceived stress. The outcomes measured for caregivers included depression, anxiety, coping self-efficacy, perceived stress, and caregiver burden. We measured depression and anxiety symptoms with the 8-item version of the PROMIS for cancer scales [ 25 ]. We administered the Coping Self-Efficacy scale ( a = 0.80-.91) to measure participants’ problem-focused and emotion-focused coping [ 26 ]. The Perceived Stress Scale ( a = 0.80-.89) assessed participants’ appraisal of situations as stressful [ 27 , 28 ]. For patients, we measured quality of life with the Functional Assessment of Cancer Therapy [ 29 ] for LC and HNC ( a = 0.70-.90) [ 30 , 31 ]. For caregivers, we measured caregiver burden with the Zarit Burden Interview (ZBI) using the short version with 12 items ( a = 0.85-.89) [ 32 , 33 ]. Statistical Analysis Data management and statistical analyses including factor analyses were conducted using the R version 2023.06.0. Joint Linear mixed models (JLMM) models were fitted using Proc Mixed in SAS 9.4. We conducted three types of analyses to determine the intervention effects on the patient reported outcomes for dyads and to estimate the extent of their correlation. The analyses included: Exploratory factor analysis (EFA) . EFA was used to identify the dominate correlation structures for the measured outcomes at baseline that could explain the covariation of the patient and caregiver outcomes. We used a combination of the scree plot and the Kaiser Rule [ 34 ], retaining the number of components above the scree (factor) and with an eigenvalue greater than 1. We used the function ‘factanal’ from the ‘psych’ package in R [ 35 ] to fit a common factor model by the method of maximum likelihood to extract the appropriate number of factors. An oblique promax rotation was used to define factor weights, under the assumption of correlated factors. Observed variables were assigned to a specific factor based on their highest factor loading, with cut-off loadings > ± 0.30 [ 36 ]. The variables entered in the analyses loaded onto two factors that we labeled “patient reported outcomes.” Confirmatory Factor Analysis (CFA) . CFA models (separate models for patients and caregivers) were framed based on the results obtained from the EFA model of baseline data. A multi-group CFA with time (assessment point) as the group variable was fit to assess the latent factors for each patient or caregiver at each time allowing us to test the strength of the correlation between the dyads at the next stage of modeling. Goodness of fit indices: Tucker–Lewis index (TLI), comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) were used to assess model fit [ 37 ]. Finally, using ggplot2 package [ 38 ], spaghetti plots were used to visualize within participant time trends of the latent factor scores by participant type (patient or caregiver) and of randomization conditions on the same subjects with the mean summary measures of the factor scores. JLMM . We modeled the covariate effects of interest (condition assignment and time) on the latent variables for the patients and caregivers jointly using a random effects model. The JLMMs were fitted with the latent factor scores estimated for patients and caregivers at each timepoint from the CFA models. Because we used a dyadic approach, we used a joint modeling method for repeated measures on both patients and caregivers jointly allowing for the random effects from each model to be correlated with one another. A random intercept linear mixed model with unstructured covariance matrix was used to estimate correlation coefficient between patient and caregiver factors. Within this modeling framework we will test 2 hypotheses. The first hypothesis tests the strength of the dyadic association between patients and caregivers. This is evaluated by estimating and testing whether the correlation between the patient and caregiver random effects is greater than zero. We also hypothesized a significant group (condition) by time (assessment point) interaction, where the stepped-care intervention would be associated with improvement or worsening of the latent variables compared with the usual care condition for each member of the dyad. JLMM were used to test the hypothesis after accounting for sources of variation and conducting separate testing of fixed effects in patients and caregivers. We tested this interaction through a Wald-type test. The estimation of the regression parameters and covariance parameters was performed using Maximum Likelihood. We investigated the covariance structure using a likelihood ratio test (LRT) that compares the correlated joint model to the uncorrelated joint model (with an independent correlation structure). We assumed a type I, alpha, error rate of 0.05 for significance. Results Patient and Caregiver Characteristics The Consort Diagram in Fig. 1 displays that out of the 286 patients randomized in the original stepped care study, 100 were excluded for the reasons provided in the diagram and 186 patients were paired at random to one caregiver. Table S2 in Supplemental Information compares the characteristics of the caregivers selected with those who were not. This table highlights the differences between the two groups, providing a detailed comparison of their respective characteristics. A statistically significant difference in age between the two groups was observed, p = 0.02. The sample entered in the analysis were 186 patient-caregiver dyads. Table 1 and Table 2 display the demographic characteristics of the sample. A total of 88 (47.3%) patients (and dyads) were randomized to usual care and 98 (52.7%) to the intervention. Patients in the intervention arm were on average 65.4 years old, 58.2% identified as male, 80.6% identified as white, and 41.8% were diagnosed with HNC. Patients in the usual care arm were on average 65.9 years old, 64.8% identified as male, 90.9% as white, and 42% were diagnosed with HNC. Caregivers in the intervention arm were on average 59.1 years old, 74.5% identified as female, and 88.6% as white. Caregiver in the usual care arm were on average 57.7 years old, 81.8% identified as female, and 82.7% as white. Table 1 Baseline Characteristics of Patients Usual Care (N = 88) Intervention (N = 98) Language Preference Spanish 6 (6.8%) 12 (12.2%) English 81 (92.0%) 85 (86.7%) Both 1 (1.1%) 0 (0%) Missing 0 (0%) 1 (1.0%) Gender Male 57 (64.8%) 57 (58.2%) Female 31 (35.2%) 41 (41.8%) Age Mean (SD) 65.9 (13.2) 65.4 (11.2) Median [Min, Max] 68.0 [23.0, 89.0] 67.0 [27.0, 87.0] Race Missing 1 (1.1%) 2 (2.0%) Other 7 (8.0%) 17 (17.3%) White 80 (90.9%) 79 (80.6%) Education College or graduate school 43 (48.9%) 44 (44.9%) High school or lower 45 (51.1%) 53 (54.1%) Missing 0 (0%) 1 (1.0%) Cancer Type Head and Neck 37 (42.0%) 41 (41.8%) Lung 51 (58.0%) 57 (58.2%) * Categorical variables are presented as N(%) and continuous variables are presented as Mean (SD) and Median (IQR). Table 2 Baseline Characteristics of Caregivers Usual Care (N = 88) Intervention (N = 98) Language Preference Spanish 4 (4.5%) 7 (7.1%) English 82 (93.2%) 85 (86.7%) Both 2 (2.3%) 2 (2.0%) Missing 0 (0%) 4 (4.1%) Gender Male 16 (18.2%) 25 (25.5%) Female 72 (81.8%) 73 (74.5%) Age Mean (SD) 57.7 (15.1) 59.1 (13.1) Median [Min, Max] 60.5 [19.0, 85.0] 61.0 [29.0, 87.0] Race Other 10 (11.4%) 17 (17.3%) White 78 (88.6%) 81 (82.7%) Education College or graduate school 58 (65.9%) 53 (54.1%) High school or lower 30 (34.1%) 45 (45.9%) * Categorical variables are presented as N(%) and continuous variables are presented as Mean (SD) and Median (IQR) EFA Results Figure 2 a and Fig. 2 b show an increase or decrease in the mean scores for the patients’ and caregivers’ mental health outcomes by timepoints and study condition. An exploratory factor analysis was performed on baseline outcomes from both patients and caregivers. Two factors were selected based on visualization of combining the scree plot and resulting two eigenvalues > 1 (Supplemental Information Figure S3) . The two factors are presented in the EFA’s diagram (Supplemental Information Figure S4). The model suggests that the baseline outcomes of the caregiver (depression, anxiety, coping self-efficacy, caregiver burden, and perceived stress) load onto the first factor and baseline outcomes measured on the patients (depression, anxiety, perceived stress, quality of life, and coping self-efficacy) load onto the second factor. The patient and caregiver PROs explained 63% of variance, with caregiver outcomes explaining 32% of variance and patient outcomes explained 31% of variance. CFA Results We found two proposed models that were a good fit to the observed data ( Supplemental Information Table S5) . For the patient model, model fit statistics were satisfactory: CFI = 0.939, TLI = 0.944, 3; SRMR = 0.055, and a RMSEA = 0.141. For the caregiver model, model fit statistics were also satisfactory: CFI = 0.971, TLI = 0.974; SRMR = 0.051 and a RMSEA = 0.096. Factor loadings for the two models for patients and caregivers are displayed in Supplemental Information Table S5 . From these models, estimates of the latent patients’ and caregivers’ patients’ reported outcomes at each study assessment time were estimated using regression methods. Figure 3 shows the estimated patient and caregiver patient reported outcomes scores over the assessment timepoints displayed as spaghetti plots. These are faceted by study conditions. All subjects appeared to have factor scores that fluctuate greatly across all four assessment points. The same pattern is observed between subject variations. Overall, it appears that between subject variability was a bit higher than within-subject variability. See Supplemental Material for details on the patient and caregiver CFA model methods. JLMM Results For the joint model, the correlation between patients and caregivers was estimated to be 0.45 and the covariance was significant with a p-value < 0.001. Per the estimates of the regression coefficients of the JLMM in Table 3 , both groups might have had a change in their PROs across timepoints. However, given that the treatment-by-time interaction yielded a p-value of 0.319 for patients and a p-value of 0.296 for caregivers, there was no significant difference over time in patient or caregiver PROs by study condition. Table 3 Correlated Bivariate Linear Mixed Model Patient Model Caregiver Model Term Estimates 95%CI P-value Estimates 95%CI P-value Intercept -0.197 (-1.486,1.091) 0.763 0.569 (-0.725,1.864) 0.388 Group 0.374 (-1.394,2.141) 0.678 -1.026 (-2.801,0.749) 0.256 Time at 6 weeks 1.079 (0.029,2.129) 0.044 2.087 (1.004,3.169) < .001 Time at 12 weeks 1.073 (0.028,2.118) 0.044 1.615 (0.526,2.705) 0.004 Time at 24 weeks 1.804 (0.749,2.859) < .001 1.909 (0.821,2.997) < .001 Group-by-time at 6 weeks -0.965 (-2.449,0.52) 0.202 -0.541 (-2.112,1.030) 0.499 Group-by-time at 12 weeks -1.055 (-2.558,0.449) 0.169 0.395 (-1.208,1.999) 0.628 Group-by-time at 24 weeks -1.565 (-3.090, -0.041) 0.044 -0.808 (-2.416,0.799) 0.324 Group-by-time interaction P-value P-value 0.319 0.296 *CI = Confidence Interval. *Bolding indicates significance, p < .05. Intervention Effects on Patients. As displayed in Table 3 , the mean change of patient PROs for the intervention arm at 24 weeks was − 1.565 units lower than the mean change of the outcomes for those in the control arm (p-value = 0.044). Supplemental Information Figure S9 displays the trends in outcomes over time for the control and intervention patients. There was no overall effect of time or intervention condition found, but there was a condition by time interaction (i.e., the lines cross after baseline). The difference in the intervention means at each time point is different compared to the difference in the control means. That is, patients’ patient reported outcomes in the intervention did not change at each time point compared to the observed increase for patients receiving usual care. Intervention Effects on Caregivers. As Supplemental Information Figure S9 displays the comparison of the mean trends of caregiver patient reported outcomes in the two conditions reveal that the lines are parallel, suggesting that there is no condition by time interaction. Therefore, the main effects of condition and time will be interpreted. The average change of caregiver patient reported outcomes in the intervention condition decreases by 1.026 units compared to the average change of caregiver patient reported outcomes in the control condition (p-value = 0.256 > 0.05, 95%CI: (-2.801,0.749). Caregiver patient reported outcomes increased significantly in both conditions comparing 6 weeks to baseline (p-value < .001), and 12 weeks to baseline (p-value = 0.004, < 0.05) and 24 weeks to baseline (p-value < 0.001). Discussion This study is a contribution to a nascent body of literature on the psychological interdependence of patients and caregivers while patients undergo treatment for LC and HNC, two very emotionally distressing types of cancers. The evidence suggests that LC and HNC patients’ caregivers experience more distress than the patient [ 11 , 12 ], but there is insufficient literature on the efficacy of psychological interventions that meet each individual level of distress. Although studies on dyadic interventions are effective treating members of the dyad [ 17 , 18 ], there is a lack of evidence on whether stepped-care interventions delivered individually to patients and to their caregivers are as effective [ 19 ]. Our study findings demonstrate strong psychological interconnectedness between patients and their caregivers but do not lend evidence that stepped-care interventions influenced their joint PROs as we had hypothesized. To investigate our hypothesis, we implemented a novel extension of the Latent Actor-Partner Interdependence Model [ 34 ] to our longitudinal study to explore the effects of a stepped-care intervention on the PROs of patient-caregiver dyads. Using a joint modeling approach, we reduce into two latent variables the patients’ PROs (i.e., depression, anxiety, coping self-efficacy, quality of life, and perceived stress) and the caregivers’ PROs (i.e., depression, anxiety, coping self-efficacy, perceived stress, and caregiver burden). More specifically, we found a strong dyadic correlation between the patients’ PROs and caregivers’ PROs, but the outcomes were not different between those who received the stepped-care intervention and those who received usual care in this intent-to-treat analysis. Overall, the PROs grouped together as two latent variables did not change over time for patients and caregivers receiving the intervention or usual care. Utilizing joint modeling can be an analytic approach for conceptualizing and analyzing patient-caregiver dyad data when multiple measurements are assessed [ 21 ]. We aimed to increase efficiency when investigating the psychological interdependence of dyads by using fewer parameters [ 21 ], as this approach allows for a summary measure of multiple measurements from each member of the dyad and for testing treatment effects among members. Another benefit of joint modeling is that it provides a straightforward way to estimate and test for a covariate effect, which is more efficient for longitudinal data with multiple outcomes [ 35 ]. Limitations Most studies investigating the effects of psychological interventions on patients and their caregivers have found that dyadic approaches improve the dyad members’ emotional distress and quality of life [ 17 , 18 ], which our study did not pursue. The lack of a dyadic intervention is a possible reason for the lack of significant findings. Instead, we implemented a stepped-care intervention not designed for dyads but for individual members of the dyad, under the hypothesis that they have unique individual levels of emotional distress. Thus, each member of the dyad received the stepped-care intervention targeting their individual level of emotional distress (depression and anxiety symptoms). A separate analysis of the effects of the intervention on PROs yielded an effect of the intervention for 243 patients [ 13 ] but not on the 204 caregivers when their data were analyzed separately (unpublished data). The intervention also aimed to diminish certain patient reported outcomes (i.e., depression, anxiety, perceived stress, caregiver burden) and to improve others (i.e., quality of life, coping self-efficacy), potentially diluting the intervention’s effect on the grouped outcomes. In addition, we used CFA to obtain the factor scores with two separate models to allow us to estimate and test for significant dyadic correlation, however future studies could use a joint model to assess the effects of stepped-care interventions. Second, due to the mismatch of patients and caregivers, we used a subset of the dataset that does not reflect all participants from the trial. Future work can focus on using all the available data and addressing the missing data through appropriate statistical methods. Clinical Implications Our findings provide evidence that LC and HNC patients and their caregivers are psychologically interdependent and may benefit from behavioral interventions targeting the dyad, as the emerging literature suggests [ 24 , 33 ]. Because stepped-care interventions are designed to provide dose-appropriate psychological treatment to match an individual’s level of mental health needs, such interventions may not affect the dyads PROs when these are considered jointly. Other cost-effective dyadic interventions need to be developed and tested to assess if they effectively improve the unique PROs of medically underserved LC and HNC patients and of their caregivers [ 23 ]. Alternatively, stepped-care interventions may be effective if they can be tailored more specifically to the unique needs and mental health challenges that caregivers of LC and HNC patients experience. In the main analysis of the trial, we found that the stepped-care intervention was effective in treating emotional distress, improving coping skills, and quality of life outcomes for patients who received the stepped-care intervention than for usual care patients, but we did not find a significant effect of the intervention on caregivers’ outcomes (unpublished data). Interventions developed for caregivers hold promise to address the unmet needs of these “hidden patients,” particularly for LC and HNC patients’ caregivers as they tend to report high caregiver burden and emotional distress [ 10 ]. Declarations Funding: Research reported in this paper was funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (AD-1511-33395) to Dr. Borrayo (Principal Investigator, PI). Consent for Publication: A consent was obtained fromeach participant in the study . Disclaimer: The statements presented in this paper are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee. Conflict of Interest : The authors report there are no conflict of interest to declare. Author Contributions: Ms.Nyonyotsi contributed to the study’s conceptualization and data analysis. Drs. Juarez-Colunga, Smyth, and Sammel contributed to the study’s data analysis and methodology oversight. Dr. Borrayo contributed to the original study’s conceptualization, funding acquisition, and investigation. All authors have contributed to the interpretation of findings and writing of the manuscript, and review and editing of drafts. Data Availability : Available upon request to the Corresponding Author and PI. ClinicalTrials.gov identifier: NCT03016403 Ethical Approval: The study’s ethics in the conduct of human subjects research was reviewed and approved by the University of Colorado, Denver, Colorado Multiple Institution Review Board (COMIRB, protocol # 16-2621). In accordance with the Declaration of Helsinki, the following norms/standards were observed: -General Principles -Risk, Burden, and Benefits -Vulnerable Groups and Individuals -Scientific Requirements and Research Protocols -Research Ethics Committee -Privacy and Confidentialiy -Informed Consent -Post Trial Provisions -Research Registration and Publication and Dissemination of Results References Pulte D, Brenner H (2010) Changes in survival in head and neck cancers in the late 20th and early 21st century: A period analysis. 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Psychological Methods. https://doi.org/10.1037/met0000531 Borrayo EA, Juarez-Colunga E, Kilbourn K, Waxmonsky J, Jacobson M, Okuyama S, Swaney R, Wamboldt FS, Karam S, Lopez Alvarez S, Jin X, Nguyen J (2023) Stepped-care to improve mental health outcomes among underserved patients with lung and head and neck cancer. Psycho-Oncology 32:1718–1726. https://doi.org/10.1002/pon.6223 Pilkonis PA, Yu L, Dodds NE, Johnston KL, Maihoefer CC, Lawrence SM (2014) Validation of the depression item bank from the Patient-Reported Outcomes Measurement Information System (PROMIS®) in a three-month observational study. Journal of Psychiatric Research 56:112–119. doi: 10.1016/j.jpsychires.2014.05.010 Sobell MB, Sobell LC (2000) Stepped care as a heuristic approach to the treatment of alcohol problems. J Consult Clin Psychol 68:573–589 Cella D, Choi S, Garcia S, et al (2014) Setting standards for severity of common symptoms in oncology using the PROMIS item banks and expert judgment. Qual Life Res 23:2651–2661. https://doi.org/10.1007/s11136‐014‐0732‐6 Chesney MA, Neilands TB, Chambers DB, Taylor JM, Folkman S (2006) A validity and reliability study of the coping self-efficacy scale. British Journal of Health Psychology 11:421–437. doi: 10.1348/135910705X53155 Lee EH (2012) Review of the psychometric evidence of the perceived stress scale. Asian Nursing Research (Korean Society of Nursing Science), 6:121–127. doi: 10.1016/j.anr.2012.08.004 Golden-Kreutz DM, Browne MW, Frierson GM, Andersen BL (2004) Assessing stress in cancer patients: a second-order factor analysis model for the Perceived Stress Scale. Assessment 11:216–223. doi: 10.1177/1073191104267398 Luckett T, King MT, Butow PN, Oguchi M, Rankin N, Price MA, Hackl NA, Heading G (2011) Choosing between the EORTC QLQ-C30 and FACT-G for measuring health-related quality of life in cancer clinical research: issues, evidence and recommendations. Annals of Oncology 22:2179–2190. doi.org/10.1093/annonc/mdq721 Cella DF, Bonomi AE, Lloyd SR, Tulsky DS, Kaplan E, Bonomi P (1995) Reliability and validity of the functional assessment of cancer therapy-lung (FACT-L) quality of life instrument. Lung Cancer 12:199–220. doi: 10.1016/0169-5002(95)00450-f D'Antonio LL, Zimmerman GJ, Cella DF, Long SA (1996) Quality of life and functional status measures in patients with head and neck cancer. JAMA Otolaryngology Head & Neck Surgery,122: 482–487. doi: 10.1001/archotol.1996.01890170018005 Zarit SH, Reever KE, Back-Peterson J (1980) Relatives of the impaired elderly: correlates of feelings of burden. The Gerontologist 20:649–655. https://doi.org/10.1093/geront/20.6.649 Hérbert R, Bravo G, Préville M (2000) Reliability, validity, and reference values of the Zarit Burden Interview for assessing informal caregivers of community-dwelling older persons with dementia. Canadian Journal on Aging 19:494–507. https://doi.org/10.1017/S0714980800012484 William R (2021) Psych: Procedures for Psychological, Psychometric, and Personality Research. https://CRAN.R-project.org/ package=psych.Accessed 15 July 2023 Tabachnick BG, Fidell LS (2007) Using Multivariate Statistics (5th ed.). Boston, Pearson Education Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6:1–55. https://doi.org/10.1080/10705519909540118 Wickham H (2016) GGPLOT2: Elegant graphics for data analysis.Springer DiStefano C, Zhu M, Mindrila D (2019) Understanding and using factor scores: Considerations for the applied researcher. Practical assessment, research, and evaluation 14. https://doi.org/10.7275/da8t-4g52 Estabrook R, Neale M (2013) A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence. Multivariate Behavioral Research 48:1–27. https://doi.org/10.1080/00273171.2012.730072 Fitzmaurice GM (2009) Joint models for continuous and discrete longitudinal data. In: Longitudinal Data Analysis. CRC Press, pp 327–345. Hu Y, Liu T, Li F (2019) Association between dyadic interventions and outcomes in cancer patients: a meta-analysis. Support Care Cancer 27:745–761. Rosseel Y (2012) Lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software 48: 1–36. https://doi:10.18637/jss.v048.i02 Roy J, Lin X (2000) Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes. Biometrics 56:1047–1054. https://doi.org/10.1111/j.0006-341X.2000.01047.x 36.Ulrich GR, Ranby KW, Borrayo E (2023) Underserved head-and-neck and lung cancer patient characteristics are associated with caregiver participation in a clinical trial. Contemporary Clinical Trials Communications 35:101195. https://doi.org/10.1016/j.conctc.2023.101195 37.Widaman KF, Olivera-Aguilar M (2023) Investigating measurement invariance using confirmatory factor analysis. In: Handbook of Structural Equation Modeling, Guilford Press, pp 367–384. Additional Declarations No competing interests reported. Supplementary Files CSupplementalInformationReSubmission.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5249185","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447595414,"identity":"2b0d89f2-0ea4-438a-9166-1826360a0c4e","order_by":0,"name":"Bissilola Nyonyotsi","email":"","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":false,"prefix":"","firstName":"Bissilola","middleName":"","lastName":"Nyonyotsi","suffix":""},{"id":447595415,"identity":"a13e7674-7bb3-4c1a-bad3-6fa5c4402019","order_by":1,"name":"Mary Sammel","email":"","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Sammel","suffix":""},{"id":447595416,"identity":"60569cf2-cef4-4368-b984-0a3370dbf457","order_by":2,"name":"Elizabeth Juarez-Colunga","email":"","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Juarez-Colunga","suffix":""},{"id":447595417,"identity":"654a69aa-32a5-4fef-8391-b06b7e016639","order_by":3,"name":"Heather Smyth","email":"","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"Smyth","suffix":""},{"id":447595418,"identity":"d2c468d2-e001-444e-a696-b17ec9a54af2","order_by":4,"name":"Evelinn Borrayo","email":"data:image/png;base64,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","orcid":"","institution":"University of Colorado Anschutz Medical Campus","correspondingAuthor":true,"prefix":"","firstName":"Evelinn","middleName":"","lastName":"Borrayo","suffix":""}],"badges":[],"createdAt":"2024-10-12 03:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5249185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5249185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82030180,"identity":"b898a19b-ec24-4f97-8a61-691e4492d596","added_by":"auto","created_at":"2025-05-06 07:13:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":451731,"visible":true,"origin":"","legend":"\u003cp\u003eConsort Diagram of patient-caregiver dyad selection\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5249185/v1/598b821439e4adb9fbe30876.jpeg"},{"id":82030974,"identity":"7c07d4f2-ae30-4d80-bc94-218ab30e27cc","added_by":"auto","created_at":"2025-05-06 07:21:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176426,"visible":true,"origin":"","legend":"\u003cp\u003ea: Mean Plots of Patients’ Outcome Measures across timepoints\u003c/p\u003e\n\u003cp\u003eb: Mean Plots of Caregivers’ Outcome Measures across timepoints\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5249185/v1/43093e9d762dc33142689c88.png"},{"id":82030185,"identity":"f1809bae-f333-40ac-b7cd-277a16553f5a","added_by":"auto","created_at":"2025-05-06 07:13:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":309579,"visible":true,"origin":"","legend":"\u003cp\u003eSpaghetti Plot of Patient’s Factor Scores (top) and Spaghetti Plot of Caregiver’s Factor Scores (bottom)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5249185/v1/e6dfba15137d840b26f8eecc.png"},{"id":83085347,"identity":"8233cad5-9532-4a3c-bbba-f8de9cbdc2a4","added_by":"auto","created_at":"2025-05-19 22:31:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5249185/v1/7b924453-3a9f-4514-8bf2-e7487854e283.pdf"},{"id":82032144,"identity":"27409f38-b5ff-46f4-9f52-e4e669558516","added_by":"auto","created_at":"2025-05-06 07:29:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":128503,"visible":true,"origin":"","legend":"","description":"","filename":"CSupplementalInformationReSubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-5249185/v1/6dda3ebfea3c1e16c9e09685.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Joint Longitudinal Model of a Stepped-Care Intervention’s Effects on the Patient Reported Outcomes of Underserved Cancer Patient-Caregiver Dyads\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck cancers (HNC) are a heterogeneous group of malignancies that arise from the squamous epithelium of the oral cavity and pharynx [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A decrease in survival rate is associated with HNC when they occur in conjunction with a second primary lung cancer (LC) diagnosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HNC and LC are also associated with intensive treatment regimens, heavy symptom burden, and high risk for treatment failure and mortality, making these malignancies two of the most psychologically distressing types of cancer that affect quality of life for patients [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Emotional distress (i.e., depression, anxiety) and lower quality of life are further aggravated for medically underserved patients who lack access to healthcare and are often diagnosed at later stages, leading to worse treatment outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to the nature of the disease and its psychological impact on patients, caregivers of LC and HNC patients also experience significant caregiving burden and emotional distress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Unfortunately, caregivers are \u0026ldquo;hidden patients\u0026rdquo; as their needs and distress often goes unrecognized [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Distress tends to be similar for caregivers and patients among couples coping with cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; however, studies with LC and HNC patients\u0026rsquo; caregivers suggest that the caregiver is significantly more distressed than the patient [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, LC and HNC patients and their caregivers can benefit from psychological support to cope with their distress, poor quality of life, and other patient reported outcomes [PROs] such as stress and caregiver burden [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStepped-care interventions are a low-cost option for delivering psychological treatment to underserved cancer patients, applying the least intrusive and costly treatment first, with treatment intensity \u0026lsquo;step-up\u0026rsquo; if emotional symptoms increase [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Stepped-care interventions are suitable for LC and HNC patients and their caregivers whose levels of emotional distress vary [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] because this step or dose-appropriate approach delivers treatment to meet the individual\u0026rsquo;s level of emotional need [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While psychological interventions delivered to cancer patient-caregiver dyads improve their emotional distress and quality of life [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], there is a lack of evidence on whether stepped-care interventions delivered individually to LC and HNC patients and to their caregivers are as effective [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we determined whether patients and their caregivers PROs covary based on the evidence that cancer patient-caregiver dyad\u0026rsquo;s distress levels often covary [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], although this may differ for LC and HNC patients and their caregivers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We used the Actor-Partner Interdependence model [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] that frames the strength of dyad members\u0026rsquo; psychological interdependence based on the influence that members receive from their partners. We augmented the model using an extension of the Latent Actor-Partner Interdependence Model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] by aggregating the multivariate outcomes for both the patients and caregivers into separate latent variables. We then extend this model to accommodate longitudinal data. In this dyadic model we estimate and test for the interconnectedness between dyad members by allowing model random effects to be correlated. In addition to testing for the significance of the dyadic relationship, we were interested in the overall effect of the \u003cem\u003estepped-care intervention\u003c/em\u003e on the PROs of dyads. That is, we examined whether the added dyadic correlation provides evidence that an individual delivered stepped-care intervention has an effect on the patients and caregivers PROs (e.g., depression, anxiety, coping, perceived stress, patients\u0026rsquo; quality of life, and caregivers\u0026rsquo; burden) due to the dyads\u0026rsquo; interdependence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eThis study used secondary data from the Stepped-Care randomized controlled trial (RCT) to evaluate the effects of a \u003cem\u003estepped-care intervention\u003c/em\u003e compared to usual care on the PROs of underserved patients with LC and HNC and of their caregivers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Patients were randomized to condition based on a 1:1 ratio. Randomization was stratified by cancer type, recruitment site, and cancer stage. Caregivers were invited to participate in the RCT and if they agreed, they were assigned to the same study condition that their patient was randomly assigned. In the case where patients had multiple caregivers, we selected only one of the patient\u0026rsquo;s caregivers at random for the secondary analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cp\u003eWe tailored the \u003cem\u003estepped-care intervention\u003c/em\u003e to meet the medical challenges and the psychological needs of patients and their caregivers. Prior and through the duration of the study, we consulted a committee of 8 oncology providers with expertise in these cancers and a patient and caregiver stakeholders (PCS) committee of 9 medically underserved (i.e., low-income, uninsured, underinsured) patients (4 diagnosed with HNC and 5 with LC) and their 9 caregivers. Providers advised us on LC and HNC patients\u0026rsquo; unique medical challenges and their potential impact on mental health, while PCS advised us on the unique and common mental health needs that patients and caregivers experience. Moreover, we culturally tailored the intervention for Spanish-speaking patients by consulting these patients (3 patients and 3 caregivers from out PCS), made it accessible vis telehealth delivery, and designed it to address the barriers that medically disadvantaged individuals face. Patients and caregivers assigned to the intervention arm received a baseline survey to assess their level of emotional distress using the Patient-Reported Outcomes Measurement Information System (PROMIS) scales [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Subsequently, each member of the dyad participated in the intervention independently at their individual treatment step, consistent with the aims of the study to meet the patients and caregivers\u0026rsquo; individual emotional needs. We use the stepped-care conceptual framework for assigning and progressing participants to a treatment step based on their level of emotional distress [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The four steps that matched levels of distress included watchful waiting (no symptoms, \u003cem\u003ePROMIS score\u0026thinsp;\u0026lt;\u0026thinsp;50\u003c/em\u003e) [25, self-help guide (mild symptoms, \u003cem\u003ePROMIS score 50\u0026ndash;59\u003c/em\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], coping skills training (moderate symptoms, \u003cem\u003ePROMIS score 60\u0026ndash;69\u003c/em\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and eight sessions of cognitive behavioral therapy (CBT, severe symptoms, \u003cem\u003ePROMIS score\u0026thinsp;\u0026gt;\u0026thinsp;70\u003c/em\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The distribution of treatment received is summarized for both patients and caregivers in Supplemental Information Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2. We monitored intervention delivery by videotaping 25% of the sessions and used a rater checklist to achieve 90% intervention-fidelity. The comparator to the intervention was usual care, which was enhanced for standardization across sites with printed mental health resources (e.g., website links), in addition to the usual care mental health resources (e.g., support groups) at the treatment sites.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe PROs were measured at four assessment points (time): at baseline, 6 weeks, 12 weeks, and 24 weeks. The outcomes measured for patients included depression, anxiety, coping self-efficacy, quality of life, and perceived stress. The outcomes measured for caregivers included depression, anxiety, coping self-efficacy, perceived stress, and caregiver burden. We measured depression and anxiety symptoms with the 8-item version of the PROMIS for cancer scales [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We administered the Coping Self-Efficacy scale (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80-.91) to measure participants\u0026rsquo; problem-focused and emotion-focused coping [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Perceived Stress Scale (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80-.89) assessed participants\u0026rsquo; appraisal of situations as stressful [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For patients, we measured quality of life with the Functional Assessment of Cancer Therapy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] for LC and HNC (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70-.90) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For caregivers, we measured caregiver burden with the Zarit Burden Interview (ZBI) using the short version with 12 items (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85-.89) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData management and statistical analyses including factor analyses were conducted using the R version 2023.06.0. Joint Linear mixed models (JLMM) models were fitted using Proc Mixed in SAS 9.4. We conducted three types of analyses to determine the intervention effects on the patient reported outcomes for dyads and to estimate the extent of their correlation. The analyses included:\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory factor analysis (EFA)\u003c/b\u003e. EFA was used to identify the dominate correlation structures for the measured outcomes at baseline that could explain the covariation of the patient and caregiver outcomes. We used a combination of the scree plot and the Kaiser Rule [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], retaining the number of components above the scree (factor) and with an eigenvalue greater than 1. We used the function \u0026lsquo;factanal\u0026rsquo; from the \u0026lsquo;psych\u0026rsquo; package in R [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] to fit a common factor model by the method of maximum likelihood to extract the appropriate number of factors. An oblique promax rotation was used to define factor weights, under the assumption of correlated factors. Observed variables were assigned to a specific factor based on their highest factor loading, with cut-off loadings\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The variables entered in the analyses loaded onto two factors that we labeled \u0026ldquo;patient reported outcomes.\u0026rdquo;\u003c/p\u003e \u003cp\u003e\u003cb\u003eConfirmatory Factor Analysis (CFA)\u003c/b\u003e. CFA models (separate models for patients and caregivers) were framed based on the results obtained from the EFA model of baseline data. A multi-group CFA with time (assessment point) as the group variable was fit to assess the latent factors for each patient or caregiver at each time allowing us to test the strength of the correlation between the dyads at the next stage of modeling. Goodness of fit indices: Tucker\u0026ndash;Lewis index (TLI), comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) were used to assess model fit [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Finally, using ggplot2 package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], spaghetti plots were used to visualize within participant time trends of the latent factor scores by participant type (patient or caregiver) and of randomization conditions on the same subjects with the mean summary measures of the factor scores.\u003c/p\u003e \u003cp\u003e\u003cb\u003eJLMM\u003c/b\u003e. We modeled the covariate effects of interest (condition assignment and time) on the latent variables for the patients and caregivers jointly using a random effects model. The JLMMs were fitted with the latent factor scores estimated for patients and caregivers at each timepoint from the CFA models. Because we used a dyadic approach, we used a joint modeling method for repeated measures on both patients and caregivers jointly allowing for the random effects from each model to be correlated with one another. A random intercept linear mixed model with unstructured covariance matrix was used to estimate correlation coefficient between patient and caregiver factors.\u003c/p\u003e \u003cp\u003eWithin this modeling framework we will test 2 hypotheses. The first hypothesis tests the strength of the dyadic association between patients and caregivers. This is evaluated by estimating and testing whether the correlation between the patient and caregiver random effects is greater than zero. We also hypothesized a significant group (condition) by time (assessment point) interaction, where the \u003cem\u003estepped-care intervention\u003c/em\u003e would be associated with improvement or worsening of the latent variables compared with the usual care condition for each member of the dyad. JLMM were used to test the hypothesis after accounting for sources of variation and conducting separate testing of fixed effects in patients and caregivers. We tested this interaction through a Wald-type test. The estimation of the regression parameters and covariance parameters was performed using Maximum Likelihood. We investigated the covariance structure using a likelihood ratio test (LRT) that compares the correlated joint model to the uncorrelated joint model (with an independent correlation structure). We assumed a type I, alpha, error rate of 0.05 for significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient and Caregiver Characteristics\u003c/h2\u003e \u003cp\u003eThe Consort Diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays that out of the 286 patients randomized in the original stepped care study, 100 were excluded for the reasons provided in the diagram and 186 patients were paired at random to one caregiver. \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e in Supplemental Information compares the characteristics of the caregivers selected with those who were not. This table highlights the differences between the two groups, providing a detailed comparison of their respective characteristics. A statistically significant difference in age between the two groups was observed, p\u0026thinsp;=\u0026thinsp;0.02.\u003c/p\u003e \u003cp\u003eThe sample entered in the analysis were 186 patient-caregiver dyads. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e display the demographic characteristics of the sample. A total of 88 (47.3%) patients (and dyads) were randomized to usual care and 98 (52.7%) to the intervention. Patients in the intervention arm were on average 65.4 years old, 58.2% identified as male, 80.6% identified as white, and 41.8% were diagnosed with HNC. Patients in the usual care arm were on average 65.9 years old, 64.8% identified as male, 90.9% as white, and 42% were diagnosed with HNC. Caregivers in the intervention arm were on average 59.1 years old, 74.5% identified as female, and 88.6% as white. Caregiver in the usual care arm were on average 57.7 years old, 81.8% identified as female, and 82.7% as white.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Care\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage Preference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (92.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.4 (11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.0 [23.0, 89.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.0 [27.0, 87.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or graduate school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and Neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* Categorical variables are presented as N(%) and continuous variables are presented as Mean (SD) and Median (IQR).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Caregivers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Care\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage Preference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (93.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (74.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.7 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.1 (13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.5 [19.0, 85.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0 [29.0, 87.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or graduate school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e*\u003cem\u003eCategorical variables are presented as N(%) and continuous variables are presented as Mean (SD) and Median (IQR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEFA Results\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eb show an increase or decrease in the mean scores for the patients\u0026rsquo; and caregivers\u0026rsquo; mental health outcomes by timepoints and study condition. An exploratory factor analysis was performed on baseline outcomes from both patients and caregivers. Two factors were selected based on visualization of combining the scree plot and resulting two eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 \u003cb\u003e(Supplemental Information Figure S3)\u003c/b\u003e. The two factors are presented in the EFA\u0026rsquo;s diagram \u003cb\u003e(Supplemental Information Figure S4).\u003c/b\u003e The model suggests that the baseline outcomes of the caregiver (depression, anxiety, coping self-efficacy, caregiver burden, and perceived stress) load onto the first factor and baseline outcomes measured on the patients (depression, anxiety, perceived stress, quality of life, and coping self-efficacy) load onto the second factor. The patient and caregiver PROs explained 63% of variance, with caregiver outcomes explaining 32% of variance and patient outcomes explained 31% of variance.\u003c/p\u003e\n\u003ch3\u003eCFA Results\u003c/h3\u003e\n\u003cp\u003eWe found two proposed models that were a good fit to the observed data (\u003cb\u003eSupplemental Information Table S5)\u003c/b\u003e. For the patient model, model fit statistics were satisfactory: CFI\u0026thinsp;=\u0026thinsp;0.939, TLI\u0026thinsp;=\u0026thinsp;0.944, 3; SRMR\u0026thinsp;=\u0026thinsp;0.055, and a RMSEA\u0026thinsp;=\u0026thinsp;0.141. For the caregiver model, model fit statistics were also satisfactory: CFI\u0026thinsp;=\u0026thinsp;0.971, TLI\u0026thinsp;=\u0026thinsp;0.974; SRMR\u0026thinsp;=\u0026thinsp;0.051 and a RMSEA\u0026thinsp;=\u0026thinsp;0.096. Factor loadings for the two models for patients and caregivers are displayed in \u003cb\u003eSupplemental Information Table S5\u003c/b\u003e. From these models, estimates of the latent patients\u0026rsquo; and caregivers\u0026rsquo; patients\u0026rsquo; reported outcomes at each study assessment time were estimated using regression methods.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimated patient and caregiver patient reported outcomes scores over the assessment timepoints displayed as spaghetti plots. These are faceted by study conditions. All subjects appeared to have factor scores that fluctuate greatly across all four assessment points. The same pattern is observed between subject variations. Overall, it appears that between subject variability was a bit higher than within-subject variability. See \u003cb\u003eSupplemental Material\u003c/b\u003e for details on the patient and caregiver CFA model methods.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eJLMM Results\u003c/h2\u003e \u003cp\u003eFor the joint model, the correlation between patients and caregivers was estimated to be 0.45 and the covariance was significant with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Per the estimates of the regression coefficients of the JLMM in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, both groups might have had a change in their PROs across timepoints. However, given that the treatment-by-time interaction yielded a p-value of 0.319 for patients and a p-value of 0.296 for caregivers, there was no significant difference over time in patient or caregiver PROs by study condition.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelated Bivariate Linear Mixed Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCaregiver\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.486,1.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.725,1.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.394,2.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.801,0.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime at 6 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.029,2.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.004,3.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime at 12 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.028,2.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.526,2.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime at 24 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.749,2.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.821,2.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup-by-time at 6 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.449,0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.112,1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup-by-time at 12 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.558,0.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.208,1.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup-by-time at 24 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.090, -0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.416,0.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup-by-time interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e*Bolding indicates significance, p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIntervention Effects on Patients.\u003c/b\u003e As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mean change of patient PROs for the intervention arm at 24 weeks was \u0026minus;\u0026thinsp;1.565 units lower than the mean change of the outcomes for those in the control arm (p-value\u0026thinsp;=\u0026thinsp;0.044). \u003cb\u003eSupplemental Information Figure S9\u003c/b\u003e displays the trends in outcomes over time for the control and intervention patients. There was no overall effect of time or intervention condition found, but there was a condition by time interaction (i.e., the lines cross after baseline). The difference in the intervention means at each time point is different compared to the difference in the control means. That is, patients\u0026rsquo; patient reported outcomes in the intervention did not change at each time point compared to the observed increase for patients receiving usual care.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIntervention Effects on Caregivers.\u003c/b\u003e As \u003cb\u003eSupplemental Information Figure S9\u003c/b\u003e displays the comparison of the mean trends of caregiver patient reported outcomes in the two conditions reveal that the lines are parallel, suggesting that there is no condition by time interaction. Therefore, the main effects of condition and time will be interpreted. The average change of caregiver patient reported outcomes in the intervention condition decreases by 1.026 units compared to the average change of caregiver patient reported outcomes in the control condition (p-value\u0026thinsp;=\u0026thinsp;0.256\u0026thinsp;\u0026gt;\u0026thinsp;0.05, 95%CI: (-2.801,0.749). Caregiver patient reported outcomes increased significantly in both conditions comparing 6 weeks to baseline (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), and 12 weeks to baseline (p-value\u0026thinsp;=\u0026thinsp;0.004, \u0026lt; 0.05) and 24 weeks to baseline (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is a contribution to a nascent body of literature on the psychological interdependence of patients and caregivers while patients undergo treatment for LC and HNC, two very emotionally distressing types of cancers. The evidence suggests that LC and HNC patients\u0026rsquo; caregivers experience more distress than the patient [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but there is insufficient literature on the efficacy of psychological interventions that meet each individual level of distress. Although studies on dyadic interventions are effective treating members of the dyad [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], there is a lack of evidence on whether stepped-care interventions delivered individually to patients and to their caregivers are as effective [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study findings demonstrate strong psychological interconnectedness between patients and their caregivers but do not lend evidence that stepped-care interventions influenced their joint PROs as we had hypothesized. To investigate our hypothesis, we implemented a novel extension of the Latent Actor-Partner Interdependence Model [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] to our longitudinal study to explore the effects of a \u003cem\u003estepped-care intervention\u003c/em\u003e on the PROs of patient-caregiver dyads. Using a joint modeling approach, we reduce into two latent variables the patients\u0026rsquo; PROs (i.e., depression, anxiety, coping self-efficacy, quality of life, and perceived stress) and the caregivers\u0026rsquo; PROs (i.e., depression, anxiety, coping self-efficacy, perceived stress, and caregiver burden). More specifically, we found a strong dyadic correlation between the patients\u0026rsquo; PROs and caregivers\u0026rsquo; PROs, but the outcomes were not different between those who received the \u003cem\u003estepped-care intervention\u003c/em\u003e and those who received usual care in this intent-to-treat analysis. Overall, the PROs grouped together as two latent variables did not change over time for patients and caregivers receiving the intervention or usual care.\u003c/p\u003e \u003cp\u003eUtilizing joint modeling can be an analytic approach for conceptualizing and analyzing patient-caregiver dyad data when multiple measurements are assessed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We aimed to increase efficiency when investigating the psychological interdependence of dyads by using fewer parameters [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], as this approach allows for a summary measure of multiple measurements from each member of the dyad and for testing treatment effects among members. Another benefit of joint modeling is that it provides a straightforward way to estimate and test for a covariate effect, which is more efficient for longitudinal data with multiple outcomes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eMost studies investigating the effects of psychological interventions on patients and their caregivers have found that dyadic approaches improve the dyad members\u0026rsquo; emotional distress and quality of life [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which our study did not pursue. The lack of a dyadic intervention is a possible reason for the lack of significant findings. Instead, we implemented a \u003cem\u003estepped-care intervention\u003c/em\u003e not designed for dyads but for individual members of the dyad, under the hypothesis that they have unique individual levels of emotional distress. Thus, each member of the dyad received the \u003cem\u003estepped-care intervention\u003c/em\u003e targeting their individual level of emotional distress (depression and anxiety symptoms). A separate analysis of the effects of the intervention on PROs yielded an effect of the intervention for 243 patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] but not on the 204 caregivers when their data were analyzed separately (unpublished data). The intervention also aimed to diminish certain patient reported outcomes (i.e., depression, anxiety, perceived stress, caregiver burden) and to improve others (i.e., quality of life, coping self-efficacy), potentially diluting the intervention\u0026rsquo;s effect on the grouped outcomes. In addition, we used CFA to obtain the factor scores with two separate models to allow us to estimate and test for significant dyadic correlation, however future studies could use a joint model to assess the effects of stepped-care interventions. Second, due to the mismatch of patients and caregivers, we used a subset of the dataset that does not reflect all participants from the trial. Future work can focus on using all the available data and addressing the missing data through appropriate statistical methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eOur findings provide evidence that LC and HNC patients and their caregivers are psychologically interdependent and may benefit from behavioral interventions targeting the dyad, as the emerging literature suggests [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Because stepped-care interventions are designed to provide dose-appropriate psychological treatment to match an individual\u0026rsquo;s level of mental health needs, such interventions may not affect the dyads PROs when these are considered jointly. Other cost-effective dyadic interventions need to be developed and tested to assess if they effectively improve the unique PROs of medically underserved LC and HNC patients and of their caregivers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Alternatively, stepped-care interventions may be effective if they can be tailored more specifically to the unique needs and mental health challenges that caregivers of LC and HNC patients experience. In the main analysis of the trial, we found that the \u003cem\u003estepped-care intervention\u003c/em\u003e was effective in treating emotional distress, improving coping skills, and quality of life outcomes for patients who received the \u003cem\u003estepped-care intervention\u003c/em\u003e than for usual care patients, but we did not find a significant effect of the intervention on caregivers\u0026rsquo; outcomes (unpublished data). Interventions developed for caregivers hold promise to address the unmet needs of these \u0026ldquo;hidden patients,\u0026rdquo; particularly for LC and HNC patients\u0026rsquo; caregivers as they tend to report high caregiver burden and emotional distress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eResearch reported in this paper was funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (AD-1511-33395) to Dr. Borrayo (Principal Investigator, PI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eA consent was obtained fromeach participant in the study\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u0026nbsp;\u003c/strong\u003eThe statements presented in this paper are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: The authors report there are no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eMs.Nyonyotsi contributed to the study’s conceptualization and data analysis. Drs. Juarez-Colunga, Smyth, and Sammel contributed to the study’s data analysis and methodology oversight. Dr. Borrayo contributed to the original study’s conceptualization, funding acquisition, and investigation. All authors have contributed to the interpretation of findings and writing of the manuscript, and review and editing of drafts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: Available upon request to the Corresponding Author and PI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinicalTrials.gov identifier:\u003c/strong\u003e NCT03016403\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e The study’s ethics in the conduct of human subjects research was reviewed and approved by the University of Colorado, Denver, Colorado Multiple Institution Review Board (COMIRB, protocol # 16-2621). In accordance with the Declaration of Helsinki, the following norms/standards were observed:\u003c/p\u003e\n\u003cp\u003e-General Principles\u003c/p\u003e\n\u003cp\u003e-Risk, Burden, and Benefits\u003c/p\u003e\n\u003cp\u003e-Vulnerable Groups and Individuals\u003c/p\u003e\n\u003cp\u003e-Scientific Requirements and Research Protocols\u003c/p\u003e\n\u003cp\u003e-Research Ethics Committee\u003c/p\u003e\n\u003cp\u003e-Privacy and Confidentialiy\u003c/p\u003e\n\u003cp\u003e-Informed Consent\u003c/p\u003e\n\u003cp\u003e-Post Trial Provisions\u003c/p\u003e\n\u003cp\u003e-Research Registration and Publication and Dissemination of Results\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePulte D, Brenner H (2010) Changes in survival in head and neck cancers in the late 20th and early 21st century: A period analysis. 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In: Handbook of Structural Equation Modeling, Guilford Press, pp 367\u0026ndash;384.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer, underserved, patients, caregivers, dyads, depression, anxiety","lastPublishedDoi":"10.21203/rs.3.rs-5249185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5249185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose.\u003c/strong\u003e The Stepped-Care study was a randomized controlled trial comparing the effects of a behavioral intervention versus usual care on the patient reported outcomes of underserved patients with lung cancer (LC) and head-and-neck cancer (HNC) and on their caregivers. In this study, we examined multivariate responses from patient-caregiver dyads within a longitudinal Latent Actor-Partner Interdependence Model and whether their interconnectedness was associated with the interventions effect on their patient-reported outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e Exploratory Factor Analysis revealed the strongest correlations among the multivariate responses were within patient and caregiver. A joint (patient and caregiver) mixed model was fitted to assess a \u003cem\u003estepped-care intervention\u003c/em\u003eeffects compared to usual care on the latent variables for dyads and to estimate the extent of their correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Patient reported outcomes were strongly associated (rho=0.45, p\u0026lt;0.001) with the outcomes of their caregivers, but the outcomes were not different between those in the \u003cem\u003estepped-care intervention\u003c/em\u003e and those in the usual care condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e Our findings provide evidence that LC and HNC patient-caregiver dyads are psychologically interdependent and may benefit from behavioral interventions. However, stepped-care interventions that match each individual’s level of psychological needs may not be effective for delivering behavioral treatment to medically underserved LC and HNC patient-caregiver dyads.\u003c/p\u003e","manuscriptTitle":"A Joint Longitudinal Model of a Stepped-Care Intervention’s Effects on the Patient Reported Outcomes of Underserved Cancer Patient-Caregiver Dyads","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 07:12:57","doi":"10.21203/rs.3.rs-5249185/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfabd314-860d-497e-abd3-68af81ddbd25","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T22:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 07:12:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5249185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5249185","identity":"rs-5249185","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}