Improving Surrogate Endpoints for Survival Prediction Through Integration of Patient-Reported Outcomes

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Improving Surrogate Endpoints for Survival Prediction Through Integration of Patient-Reported Outcomes He Zhang , Congyu Zhang , Benyam Muluneh , Wenhao Zheng , Huaxiu Yao , Ethan Basch , Jiawei Zhou doi: https://doi.org/10.1101/2025.10.02.25337154 He Zhang 1 Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA 2 National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, China MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Congyu Zhang 1 Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benyam Muluneh 1 Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA PharmD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wenhao Zheng 3 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Huaxiu Yao 3 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA 4 School of Data Science and Society, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ethan Basch 5 Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA MD, MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiawei Zhou 1 Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA 5 Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: zhoujw{at}unc.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Overall survival (OS) remains the gold standard for oncology drug approval, but measuring it requires long follow-up and is impractical in certain oncology settings. To expedite drug evaluation, radiographic endpoints such as progression-free survival (PFS) and overall response rate (ORR) are commonly used as alternatives to OS. However, these measures do not always reflect meaningful survival benefits, limiting their reliability as surrogate endpoints for regulatory decisions. Patient-reported outcomes (PROs), including quality of life (QoL) measures, capture the patient’s perspective on efficacy and toxicity, may enhance prediction of OS when combined with radiographic endpoints. We reviewed FDA oncology New Drug Registry from 2017 to 2023. Trials were included if they reported OS, at least one radiographic endpoint, and patient-reported QoL. We trained machine learning models to predict whether OS is improved in the trial, first using radiographic endpoints alone, and then adding QoL. Model performance was compared between these approaches. Of 387 oncology trials screened, 89 met inclusion criteria. Among these, 57 (64%) showed OS benefit, 76 (85%) reported improvement in the radiographic measures, and 35 (39%) had QoL benefits. Incorporating PROs into the model improved the predictive, with a 20% relative increase in AUC-ROC, indicating substantially better discrimination between trials with and without OS benefit. These findings suggest that incorporating PROs into traditional radiographic endpoints enhances prediction of OS benefit. A composite surrogate endpoint that combines radiographic and QoL measures may provide a more reliable and patient-centered framework for regulatory decision-making and help accelerate drug development. Translational Relevance Overall survival (OS) is the gold standard for evaluating cancer therapies, but its reliance on long-term follow-up delays trial readouts and drug approvals. Commonly used alternatives, such as progression-free survival, provide radiographic signals of tumor progression, but they do not consistently correlate with OS, limiting their value as surrogate endpoints. Patient-reported outcomes (PROs) capture quality of life and symptom burden directly from the patient’s perspective. In this study, we show that incorporating PROs with radiographic endpoints substantially enhances the prediction of OS benefits in clinical trials, with machine learning models demonstrating about 20% better predictive performance compared to using radiographic measures alone. These findings support a novel composite surrogate endpoint that integrates PROs with radiographic measures, providing more patient-centered framework for regulatory decision-making and potentially accelerating oncology drug development. Introduction Overall survival (OS) remains the gold standard for evaluating cancer therapies efficacy and safety, but it requires long follow-up time.( 1 ) This not only makes clinical trials lengthy and costly, but also renders OS impractical in oncology settings such as indolent diseases or highly efficacious therapeutics where patients live for many years.( 2 ) Radiographic endpoints like progression-free survival (PFS) and overall response rate (ORR) can be measured earlier and are widely used to guide trial evaluations and clinical decisions. However, these alternatives often show limited correlation with OS and may fail to capture meaningful survival benefit.( 3 ) The recent FDA draft guidance reaffirmed OS as the primary regulatory endpoint, highlighting an unmet clinical need: the lack of robust surrogate markers that can reliably predict OS earlier in the trial process.( 4 ) Patient-reported outcomes (PROs) have strong potential to fill this gap. PROs are direct reports from patients describing their symptoms, functioning, and quality of life (QoL). Unlike radiographic or biomarker-based measures, PROs provide a unique “host perspective” that complements traditional indicators of disease progression or treatment toxicity.( 5 , 6 ) PROs are non-invasive, low-cost, and can be collected frequently over time, enabling more granular assessment of clinical status over time. Extensive evidence shows that integrating PROs into oncology care improves symptom control, reduces emergency visits, and is even linked to better survival outcomes.( 7 ) In this study, we hypothesized that integrating PROs with radiographic endpoints could enhance their utility as surrogate markers for OS. To test this, we systematically reviewed oncology trial registry from 2017 to 2023 and applied machine learning approaches to evaluate whether the addition of PROs improves the predictive performance of traditional surrogate endpoints for OS. Our findings show that incorporating PROs can enhance the predictive power of radiographic endpoints, allowing better distinct between trials with or without OS benefits. By combining the objective assessment of tumor with the patient’s own experience of symptoms and QoL, we aimed to generate a more comprehensive measure of clinical benefit. Methods We reviewed the FDA New Drug Registry at Drugs@FDA for oncology indications approved between January 2017 and December 2023. Trials were included if study results reported OS, at least one radiographic-based endpoint, and patient-reported QoL. Details of the data inclusion and exclusion process are shown in Figure 1 . Download figure Open in new tab Figure 1. Trial Selection Flow Diagram. Randomized clinical trials of oncology drugs were identified from the FDA registry. Trials were included if they reported overall survival (OS), radiographic endpoints, and quality of life (QoL) outcomes. Clinical trial data were extracted from ClinicalTrials.gov and PubMed. The trial data included endpoint measurements from both the active and control arms, together with point estimates, measures of variance, and sample sizes. When multiple radiographic-based endpoints were reported, the primary surrogate endpoint was used for analysis. For patient-reported QoL, instruments varied across studies. We selected the aggregate-level QoL measure reported and standardized all scales so that higher scores consistently indicated better QoL. In addition, we verified each original trial report to confirm whether OS benefit was reported. All data extraction were independently checked by two researchers to ensure accuracy and consistency. To evaluate endpoint surrogacy, we developed supervised machine learning models using the XGBoost algorithm to classify whether a drug demonstrated OS benefit. The dataset was randomly split into training (80%) and test (20%) cohorts. The models were trained in two stages: first using traditional radiographic endpoints alone, and then retrained with the addition of patient-reported QoL endpoints. Predictive performance on the test cohort was primarily evaluated using the area under the receiver operating characteristic curve (AUC-ROC), which measures the ability to discriminate between drugs with and without OS benefit. AUC-ROC values >0.8 were considered indicative of strong predictive ability. To provide a more comprehensive assessment, we also compared accuracy, precision, sensitivity (recall), specificity, and F1-scores for each model. The machine learning model codes were available at https://github.com/shenmishajing/QoL_prediction . Results Between 2017 and 2023, 387 oncology trials were registered on the FDA website, but only 89 (23%) had publicly available data on OS, radiographic endpoints, and QoL measures. Among these 89 trials, one trial was from a withdrawn indication (atezolizumab for triple-negative breast cancer)( 8 ); while the remaining trials led to regulatory approvals. The data inclusion and exclusion process was presented in Figure 1 . Of these, 57 (64%) trials demonstrated OS benefit, 76 (85%) showed improvement in alternative endpoints, and 35 (39%) reported QoL benefits. ( Figure 2A ) Notably, no drug was approved based solely on QoL improvements, suggesting that PROs are not yet central to regulatory decision-making in oncology.( 9 ) Download figure Open in new tab Figure 2. Added Value of Patient-Reported Quality of Life (QoL) in Predicting Overall Survival (OS) Benefit. (A) Venn diagram illustrating the overlap among oncology trials demonstrating benefits in OS, radiographic-based efficacy endpoints (e.g., progression-free survival or overall response rate), and patient-reported QoL outcomes. The five trials that lacked significant benefits across all three domains but still received approval were non-inferiority trials. (B) The area under the receiver operating characteristic curve (AUC-ROC) compares model performance using radiographic endpoints alone versus the model that also includes QoL data. AUC-ROC reflects how well the model identifies drugs with OS benefit, with values above 0.8 indicating good predictive performance. (C) Feature importance in the machine learning model with both QoL and radiographic endpoints. Among the 32 trials that did not demonstrate an OS benefit, 27 (84.4%) reported better radiographic-based measures, suggesting that radiographic endpoints may yield false-positive signals. Furthermore, 10 trials (11.2%) showed concurrent improvements in both QoL and radiographic endpoints, yet still failed to demonstrate an OS benefit. These findings suggest that the relationships between QoL, radiographic outcomes, and OS are complex and not simply binary or linear. A summary of trial characteristics and endpoint outcomes is provided in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Characteristics of Randomized Clinical Trials Included in the Analysis. We then evaluated the performance of machine learning models trained to predict OS benefit. Using radiographic endpoints alone, the model achieved an AUC-ROC of 0.68, indicating moderate predictivity. After adding QoL data, the model’s performance improved by 20%, achieving an AUC-ROC of 0.88 ( Figure 2B ). The combined model also outperformed the radiographic-only model across accuracy, specificity, precision, and F1 score ( Table 2 ). These findings suggest that adding PROs to traditional surrogate endpoints can substantially enhance the prediction of therapies with an OS benefit. View this table: View inline View popup Download powerpoint Table 2. Comparison of Machine Learning Models Performance With and Without QoL Endpoints. We analyzed feature importance in the machine learning model that incorporated both QoL and radiographic endpoints ( Figure 2C ). Feature importance reflects how strongly each variable contributes to the model’s predictions. Radiographic endpoint benefit remained the most influential predictor, indicating they are still essential in forecasting OS. However, both QoL and QoL variance ranked among the top five features, highlighting the added value of incorporating PROs. Importantly, not only absolute QoL scores but also their variability contributed meaningfully in the prediction model. This is consistent with our prior observation that PROs often exhibit substantial variance, which must be considered when interpreting these measures. ( 10 ) Discussion This study demonstrates that adding PROs to radiographic endpoints can significantly improve survival prediction in oncology trials. By analyzing FDA oncology drug registrations from 2017 to 2023, we assembled trial-level data on patient-reported QoL, radiographic endpoints, and OS. To assess the added value of PROs, we compared the predictive performance of machine learning model developed with radiographic measures alone, with the machine learning model that was trained with both patient-reported QoL and radiographic endpoint data. The model that included PROs achieved approximately 20% improvement in accuracy and overall predictive performance. We did not use traditional regression models because they are limited in capturing the nonlinear, high-dimensional relationships among different endpoints. ( 10 ) Machine learning is better suited to identify these complex associations and predict OS benefits. ( 11 , 12 ) Variability in PROs can also confound statistical analyses and reduce power, and our previous work showed that inconsistencies between PROs and survival often stem from this variance. ( 10 ) By incorporating both QoL measures and their variability, machine learning models can more effectively detect meaningful patterns and improve OS prediction. Despite these promising results, our study has some limitations. We could only include trials that reported both PRO and OS data, which kept the sample size relatively small and limited how broadly the results can be applied. Larger studies are needed to confirm whether these findings hold true in different settings. In addition, PROs were not reported in a consistent way across trials, which may have affected the model’s performance. The next step will be to expand the number of trials included in the analysis to strengthen model power, while also working toward more standardized collection and reporting of PROs. In summary, our approach provides a quantitative framework for generating more reliable surrogate markers for OS, which could accelerate drug approval timelines and enhance regulatory decision-making. Although confirming surrogacy requires patient-level data within specific disease contexts, this study provides foundational evidence supporting composite endpoints that integrate both radiographic and PRO measures. As oncology drug development evolves, integrating PROs into radiographic endpoints in the OS prediction through advanced artificial intelligence and machine learning models may support more holistic, patient-centered regulatory decisions. Data Availability All data produced in the present study are available upon reasonable request to the authors. Acknowledgements H.Z. received funding support from Chinese Academy of Medical Sciences and Peking Union Medical College. We acknowledged the use of Artificial intelligence–based tools to assist with grammar and language editing in the preparation of this manuscript. Footnotes Conflicts of Interest B.M reports personal fees from Novartis outside this submitted work. E.B. receives institutional research funding from the National Cancer Institute and from the Patient-Centered Outcomes Research Institute, and personal fees for scientific advising from Research Triangle Institute, Thyme Care, N-Power Medicine, Resilience Health, Canopy Care, Savor Health, and Navigating Cancer. References 1. ↵ Blumenthal GM , Karuri SW , Zhang H , Zhang L , Khozin S , Kazandjian D , et al. Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non–small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses . Journal of Clinical Oncology 2015 ; 33 ( 9 ): 1008 – 14 . OpenUrl Abstract / FREE Full Text 2. ↵ Banerjee S , Booth CM , Bruera E , Buechler MW , Drilon A , Fry TJ , et al. Two decades of advances in clinical oncology—lessons learned and future directions . Nature Reviews Clinical Oncology 2024 ; 21 ( 11 ): 771 – 80 . OpenUrl PubMed 3. ↵ Rodriguez LR , Gormley NJ , Lu R , Amatya AK , Demetri GD , Flaherty KT , et al. Improving collection and analysis of overall survival data . Clinical Cancer Research 2024 ; 30 ( 18 ): 3974 – 82 . OpenUrl PubMed 4. ↵ FDA . 2025 Approaches to Assessment of Overall Survival in Oncology Clinical Trials Guidance for Industry. Draft. . 5. ↵ Sherry AD , Miller AM , Parlapalli JP , Kupferman GS , Beck EJ , McDonald J , et al. Overall Survival and Quality-of-Life Superiority in Modern Phase 3 Oncology Trials: A Meta-Epidemiological Analysis . JAMA oncology 2025 . 6. ↵ Zettler M , Basch E , Nabhan C. Surrogate end points and patient-reported outcomes for novel oncology drugs approved between 2011 and 2017 . JAMA oncology 2019 ; 5 ( 9 ): 1358 – 9 . OpenUrl PubMed 7. ↵ Colbert JA , Potters L. Overcoming Barriers to Make Patient-Reported Outcome Collection the Standard of Care in Oncology . JAMA oncology 2025 ; 11 ( 3 ): 233 – 4 . OpenUrl PubMed 8. ↵ Schmid P , Adams S , Rugo HS , Schneeweiss A , Barrios CH , Iwata H , et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer . New England journal of medicine 2018 ; 379 ( 22 ): 2108 – 21 . OpenUrl CrossRef PubMed 9. ↵ Bujosa A , Moltó C , Hwang TJ , Tapia JC , Vokinger KN , Templeton AJ , et al. Associations with definitive outcomes and clinical benefit of cancer drugs at the time of marketing approval and in the postmarketing period . Journal of the National Comprehensive Cancer Network 2021 ; 19 ( 13 ): 117 – 25 . OpenUrl 10. ↵ Zhou J , Muluneh B , Li Q , Hughes JH . Revolutionizing patient-reported outcomes analysis for oncology drug development using population models . Clinical Cancer Research 2025 ; 31 ( 9 ): 1580 – 6 . OpenUrl PubMed 11. ↵ Zhou J , Muluneh B , Wang Z , Yao H , Hughes JH . Leveraging Longitudinal Patient-Reported Outcome Trajectories to Predict Survival in Non–Small Cell Lung Cancer . Clinical Cancer Research 2025 ; 31 ( 13 ): 2685 – 94 . OpenUrl PubMed 12. ↵ Dhiman P , Ma J , Andaur Navarro CL , Speich B , Bullock G , Damen JA , et al. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review . BMC medical research methodology 2022 ; 22 ( 1 ): 101 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted October 03, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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