A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period

Keywords synthetic dataset, heart attack, stroke, machine learning, statistics, training, education, data ALL Metrics - Views Downloads How to cite this article Burns D, Richardson K and Driessens C. A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.13651.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Data Note [version 1; peer review: 2 approved with reservations] Dan Burns https://orcid.org/0000-0001-6976-1068 1,2, Kathryn Richardson3, Corine Driessens https://orcid.org/0000-0003-3767-7683 2Dan Burns https://orcid.org/0000-0001-6976-1068 1,2, Kathryn Richardson3, Corine Driessens https://orcid.org/0000-0003-3767-7683 2 PUBLISHED 01 Nov 2024 Author details Author details 1 School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK 2 NIHR Applied Research Collaboration Wessex, University of Southampton, Chilworth, Southampton, SO16 7NP, UK 3 Norwich Medical School, University of East Anglia, Norwich, England, NR4 7TJ, UK 2 NIHR Applied Research Collaboration Wessex, University of Southampton, Chilworth, Southampton, SO16 7NP, UK 3 Norwich Medical School, University of East Anglia, Norwich, England, NR4 7TJ, UK Dan Burns Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Kathryn Richardson Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Corine Driessens Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS Machine learning methodologies are becoming increasingly popular in healthcare research. This shift to integrated data science approaches necessitates professional development of the existing healthcare data analyst workforce. To enhance this smooth transition, educational resources need to be developed. Real healthcare datasets, vital for healthcare data analysis and training purposes, have many barriers, including financial, ethical, and patient confidentiality concerns. Synthetic datasets that mimic real-world complexities offer simple solutions. The presented synthetic dataset mirrors the routinely collected primary care data on heart attacks and strokes among the adult population. Training experiences using this synthetic dataset are elevated as the data incorporate many of the practical challenges encountered in routinely collected primary care systems, such as missing data, informative censoring, interactions, variable irrelevance, and noise. By openly sharing this synthetic dataset, our goal was to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory function, and experience of heart attack and/or stroke. Methods for simulating each variable are detailed to ensure a realistic representation of the patient data. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development in the healthcare and social care research workforce. In healthcare research, computer programs that can learn patterns are becoming increasingly common. These programs are known as machine learning programs. This means that healthcare data analysts must learn about this approach. Analysts must test these methods in real healthcare data. However, it is difficult to access these data. However, these datasets are costly and have limited use. Privacy and ethical concerns also play an important role. There is a need for teaching resources to help analysts learn machine learning. A good solution is to use mock datasets that resemble real world data. This work describes a mock dataset created to resemble real adult patient data. The purpose of the dataset was to predict the risk of a heart attack or stroke. The dataset includes problems that a data analyst encounters and needs to solve. This includes missing data and complex links between the data. It also includes data that are not useful for predicting heart attack or stroke. Finally, some data included recording errors. These problems make the training experience practical and helpful. We aimed to support the growth of machine-learning skills using this mock dataset. We also aimed to provide better support for health and social care research. This study fills this gap in freely available quality healthcare datasets for training. synthetic dataset, heart attack, stroke, machine learning, statistics, training, education, data Corresponding Author(s) Dan Burns ([email protected]) Grant information: This project is funded by the National Institute for Health and Care Research (NIHR) [ARC Wessex (Reference Number NIHR2018870)] and the National Centre for Research Methods. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Dr. Dan Burns and Dr. Corine Driessens were the lead applicants for the funding applications. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Burns D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Burns D, Richardson K and Driessens C. A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.13651.1) First published: 01 Nov 2024, 4:67 (https://doi.org/10.3310/nihropenres.13651.1) Latest published: 01 Nov 2024, 4:67 (https://doi.org/10.3310/nihropenres.13651.1) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. One of the primary objectives of healthcare research is to classify individuals into health status groups to predict the likelihood of developing certain health conditions based on their risk characteristics. Traditionally, this classification is performed using statistical methodologies, with only a few potential risk factors considered simultaneously1. Alongside increased computing capacity to analyse large datasets and increased awareness of the knowledge potential embedded in routinely collected healthcare data, the approach to data analysis and decision-making has evolved from a theoretical to a more data-driven decision-making process2. Machine learning (ML) methodologies have become more popular for classifying healthcare-related projects, and integrated data science approaches should be considered to enhance the ability to extract meaningful insights from the data3,4. The strengths of ML methodologies include the accurate generation of general-purpose learning algorithms to identify patterns of variables related to specified health conditions within large datasets that contain vast collections of different variables5,6. The strengths of statistical methods include the ability to delve deeper and improve understanding of the underlying relationships between the identified risk factors and health conditions in question6. Therefore, in this data-driven era, it is important for modern-day healthcare and social care data analysts to familiarize themselves with a more integrated data science approach7. Organisations such as the National Institute for Health and Care Research (NIHR), whose aim is to improve health and social care through research, must facilitate a smooth transition to this integrated data science approach by developing professional development programmes that highlight the connection between statistical concepts and ML algorithms and similarities in terms of model validation, uncertainty estimation, and feature selection. To reduce the learning curve and enhance the integration of these novel approaches, accessible user-friendly educational resources must be developed8. The 2023 topic of the Routine Data section of the NIHR Statistics Group9 annual workshop is the analysis of classification problems using machine learning and statistical methodologies in routine data. While organising this meeting, we observed a lack of freely available, sophisticated, and comprehensive healthcare datasets with sufficiently large sample sizes for training purposes. Access to healthcare datasets is an essential element of health data science training is access to healthcare datasets10. To protect patient confidentiality, healthcare data are not available for training11. Synthetic datasets can alleviate privacy and ethical concerns associated with accessing real healthcare data for training purposes12. A bespoke synthetic healthcare dataset was created for the annual meeting of the 2023 NIHR Statistics Group Routine Data section. Through meticulous simulation techniques13,14, the synthetic dataset presented here mirrors the complexities and nuances of real-world primary care heart attack or stroke data from adult patients (age 18+ years) within a 10-year follow-up period. The QRISK tool, used in the United Kingdom (UK) to assess the risk of cardiovascular disease in adults, was taken as inspiration for the creation of the synthetic dataset15. Therefore, the synthetic dataset includes factors such as age, sex, smoking status, blood pressure, and medical history. The synthetic dataset has been developed to represent the heterogeneity in heart attack and stroke data commonly encountered in datasets collected from primary care databases, such as the Clinical Practice Research Datalink (CPRD)16, and incorporates the naturally occurring noise of real-world data. To provide an authentic immersive learning experience, the dataset incorporates incomplete information to enhance trainees’ missing data analysis and informative censoring skills. In addition, the synthetic dataset contained variables that did not significantly contribute to the analyses, thereby developing trainees’ variable selection and model optimization skills. By making this synthetic dataset available for open access, we endeavored to produce a transformative asset for professional training in the field of health and social care data analysis that focuses on one of the leading causes of global mortality17. Our philosophy for dataset development is to produce a dataset that sufficiently reflects the realism challenges encountered in practice: missing data, informative censoring, interactions, variable irrelevance, and noise. The purpose of the dataset is to enable training in the handling of these realisms, and knowledge of the exact mechanisms generating these effects in the dataset can help facilitate training in this manner. The aim is not to produce a dataset that represents real life: although the data should have a degree of realism, we do not strive to optimize the accuracy. To maintain some realism in the dataset, we generated covariate values based on the simulated age and sex of each patient based on relationships derived from published studies. This naturally introduces correlations between all variables, while remaining relatively simple to generate. We simulated the data for 100,000 patients using this methodology. A summary of the fields in the dataset is provided in Table 1. A single row per patient was identified using patient_id. The data included age and sex as demographic factors; lifestyle variables such as body mass index (BMI) and smoking status; and comorbidities including hypertension, family history of cardiovascular disease, atrial fibrillation, chronic kidney disease, rheumatoid arthritis, diabetes, and chronic obstructive pulmonary disorder (COPD). It also includes simulated measurements of the systolic blood pressure (SBP) and forced expiratory volume 1 (FEV-1). Finally, the recording of whether a heart attack or stroke occurred was included, with the associated time to event or censoring. We discuss how these variables were simulated in subsequent sections. To simulate age, we utilised 2020-based interim national population projections from the Office of National Statistics18. These data are provided in age groups of 5 years from age 0 to 100 years and are represented in terms of 1000s within each group. For this dataset, we considered only individuals aged 18 – 79 years. We assume that within each 5-year age group, the ages are uniformly distributed, that is, each age is equally likely. With this simplification, we contracted the 15 – 19 age group to an 18–19 age group by replacing the corresponding count with a reduced count by a factor of 2/5, that is, two years out of the five years within that group. To sample an individual’s age, we sampled the age group based on the counts post-modifications. We then sampled an individual’s age from a uniform distribution within that age group. To simulate sex, we used a Bernoulli distribution with p = 0.5 to indicate whether a person is male or female. To simulate BMI, we used the 2021 Health Survey for England dataset on obesity19. This dataset provides aggregated statistics regarding the percentage of individuals in three categories: non-overweight or obese (BMI < 25 kg/m^2), overweight (BMI = 25 kg/m^2), and obese (BMI = 30 kg/m^2). The data were also stratified by sex and the following age groups: 16–24, 25–34, 35–44, 45–54, 55–64, 65–74, and 75+ years. To sample BMI, we assumed that within each age group and sex combination, BMI follows a normal distribution. To fit a normal distribution to the percentage data, we represented each percentage through the cumulative distribution function (CDF) of a normal distribution for that particular group. Based on this assumption, CDF must be equal to the percentage value at each threshold. Let F represent the CDF for a particular group; then, F(25) represents the percentage of individuals in the “not overweight or obese” group, and F(30) represents those in the former group, combined with those in the overweight group. Finally, to include information on obese patients, we set this to F(50) where 50 kg/m^2 is an arbitrarily high upper bound. We then fit the mean and standard deviation of the normal distribution by minimising the least squares between the CDF values and observed percentages: where p25, p30 are the percentages observed for the age group and sex combination. To sample BMI, we used the previously sampled age and sex to match up to the corresponding mean and standard deviation calculated from the above procedure and sample from the corresponding normal distribution. To simulate SBP, we harness the results from Balijepalli et al.20, who calculated the 5th, 25th, 50th, 75th, and 95th percentiles of the SBP distribution as a function of age group. We fitted the percentiles for each age group to a normal distribution in a similar fashion to the case for BMI: minimising the least squares of the CDF values matched with the observed proportions. To sample SBP, we therefore find the age group of the individual and then sample from the corresponding normal distribution. We refer to this SBP as true SBP. SBP as a measurement variable suffers from various sources of noise in routine datasets in practice, such as measurement errors and missingness. We modeled the measurement stochasticity arising from physiological variation and measurement device error by creating a measured version of the true SBP. To achieve this, we sampled from a normal distribution centered on the true SBP. If the true SBP is St, then the measured SBP Sm is distributed as We used σm = 10 mmHg to reflect the median standard deviation observed by Li et al.21, an ambulatory blood pressure monitoring study based in China. We cover this missingness in a later section. Hypertension is known to be undertreated. It is estimated that only approximately 40% of hypertensive patients are treated worldwide, and the primary drivers for this low percentage are a lack of diagnosis and patients refusing treatment22. Furthermore, therapeutic inertia also has an impact23 where cases are explicitly missed; this depends explicitly on the true SBP of the individual. From our simulated true SBPs, approximately 33% of the patients had a true SBP above 135 mmHg, leading to approximately 13% of the patients in the dataset being treated for hypertension. To simulate the effect of an individual being missed as a hypertensive patient, we used a logistic curve to define the probability of treatment. Let ptreated be the probability that a patient is treated. Then, we define where we set pdiag to ensure that the overall prevalence of hypertension treatment was approximately 13%. This leads to pdiag = 0.2 for this case. To simulate whether an individual has a family history, we used the prevalence measured by Chacko et al.24 and used a Bernoulli distribution with p = 0.24 to represent this variable. There are no correlations with the other variables. To simulate comorbidities, we utilised joint distributions between each comorbidity, age, and sex. Each comorbidity is sampled using a Bernoulli distribution, where the probability is governed by the individual’s age group and sex. For chronic kidney disease, we used the study by Kampmann et al., which provides prevalence estimates as a function of age and sex in Southern Denmark25. This study reports the prevalence in three age groups: 18–39, 40–69, and 70+ years for both men and women. For atrial fibrillation, we used Go et al.26, which assessed prevalence as a function of age and sex in the United States in 2001. The age groups are given as 18–54, 55–59 and 5-year age groups, ending with an 85+ age group. For rheumatoid arthritis, we used Symmons et al.27, which stratified the prevalence by age and sex for adults in the UK, with age groups of 16–44, 45–64, 65–74, and 75+ years. For diabetes, we used the Health Survey for England data19, using prevalence estimates for combined diagnosed and undiagnosed diabetes by age groups 16–44, 45–64 and 65+ years. For COPD, we used Ntritsos et al.28, which provides prevalence estimates by age and sex for the global population, with age groups 15–39, 40–69, and 70+. We adjusted the lower age group to 18–39 for simulation and assumed that the prevalence holds for this subgroup. FEV1 differs significantly between patients with COPD and those who do not have the condition. To simulate FEV1 in patients with COPD, we used the study by Le et al.29, which assessed the prognostic ability of GOLD classifications in Norway. The GOLD categories correspond to FEV1 ranges: ≥ 80% for GOLD 1, 50 – 80% for GOLD 2, 30 – 50% for GOLD 3, and <30% for GOLD 4. This study reports the prevalence of GOLD grades 2, 3, and 4 in patients with COPD. We arbitrarily assumed that the prevalence of GOLD 1 is 50% in COPD patients. Therefore, according to the study, the prevalence of GOLD 2, 3, and 4 was 28%, 15%, and 7%, respectively. To sample FEV1 using these data, we first sampled the individual’s GOLD grouping according to the above prevalence. We then sample uniformly from the following ranges: 80 – 82 for GOLD 1, 50 – 80 for GOLD 2, 30 – 50 for GOLD 3, and 20 – 30 for GOLD 4. To measure FEV1 in healthy individuals, we used uniform samples from 90 to 100 irrespective of age and sex. Smoking status prevalence has been assessed in the Health Survey for England study by age group and sex19. The age groups are described in the body mass index section. To simulate smoking status, we extracted the proportion of individuals who currently smoked within each age group and sex combination. We then used a Bernoulli distribution with p equal to the prevalence for that individual’s age and sex. To simulate a heart attack or stroke, we used a modification of the QRISK3 model15 to consider only the variates contained in this synthetic dataset. Baseline survival is taken as a step function with 10 steps, with each step occurring each year. The value of the survival function at the steps forms a linear function, starting at probability 1 at t = 0 years, dropping to 0.977 for males and 0.989 for females at t = 10 years. The step-like nature of the survival function replicates an event measured in discrete intervals. We simplified the QRISK3 algorithm to match our reduced dataset by removing the impact of the majority of variables not present in our dataset. This includes variables corresponding to angina, migraines, systemic lupus erythematosus, severe mental illness, atypical antipsychotic medication, steroid use, erectile dysfunction, ethnicity, and deprivation. For variables within our dataset, QRISK3 had different contributions to the type of diabetes, as well as history of smoking. We set the impact of diabetes on the risk to be equivalent to those with type 2 diabetes and the impact of smoking on the risk as equivalent to light smokers within the QRISK nomenclature. We identified that the cholesterol/HDL ratio and systolic blood pressure standard deviation both had significant impacts on the risk; therefore, we chose to include non-zero values overall. We arbitrarily chose a cholesterol/HDL ratio of 3 and a systolic blood pressure standard deviation of 10 mmHg to align with the median observed standard deviation21. The model was evaluated using the individual’s true SBP, not their measured SBP, to further introduce noise. To determine whether the event occurred, we used the model to calculate the likelihood of an event occurring at 10 years for each individual. We then sample the time at which the event occurred using the individual’s survival curve using inverse transform sampling, that is, we draw a uniformly random variable on (0,1) and match this to the predicted CDF. If the uniformly random variable is larger than the corresponding point for t = 10 years, then the event is censored. For those who did not experience the event, we set their censoring time to 10 years. To simulate study dropout and include informative censoring effects, we used a dropout rate based on whether the event occurred or not. If an individual has an event at t years, then the likelihood of dropout is p = 0.01 * t. After sampling the heart attack or stroke event, we introduced two types of missingness mechanisms among the other variables in the simulated dataset that can occur in routine datasets: Missing at Random (MAR), where the missingness is dependent on other variables in the dataset (e.g., a numerical variable is not explicitly recorded, e.g., BMI), or Missing Completely at Random (MCAR, e.g., a feature of the individual has not been made known to the healthcare system). We modeled missingness in smoking status, family history of cardiovascular disease, SBP, and BMI variables as MCAR30. For smoking status and family history of cardiovascular disease, we converted 1 to 0 with a probability of 30% to obscure the true smoking status and family history. To simulate missingness for the SBP measurements, we sampled from a Bernoulli distribution with p = 0.1 to determine whether the variable value should be dropped. We excluded the measured SBP value based on this indicator. For BMI measurements, we removed those with p = 0.3. For FEV1, missingness is modelled as MAR with missingness less likely if the patient has COPD30. If an individual had COPD, we removed the variable with p = 0.05. If they did not have COPD, we removed the variable with p = 0.75. Finally, we completely excluded the true SBP column, which represents an unknown characteristic of the patient that can only be interpreted through other variables (including the measured SBP). This data note and corresponding dataset was created without patient involvement. Patients were not involved in the design or creation of this synthetic dataset: the dataset’s intended purpose is aimed at healthcare data analysts who wish to develop their machine learning skills. Patients were not invited to contribute to this document. Sophisticated synthetic datasets are becoming an increasingly important resource in health data science. We have provided a freely available and comprehensive synthetic healthcare dataset that mirrors the natural associations within UK primary care data to assess the 10-year risk of two commonly studied health outcomes: heart attack and stroke. We are aware of the CPRD synthetic dataset available for predicting heart attack or stroke using information for 499,344 simulated patients and 21 predictor variables13. Although the simulated data in the CPRD dataset are also based on the QRISK models15, the dataset is generated differently by using a Bayesian Network model trained on real primary care data. This leads to governance issues and, hence, a fee to access and use data. Our synthetic data were built from an a priori modelling framework informed by a literature search. This allows the data to not be generated directly from real patient data, but retains many of the realisms found in such datasets. These synthetic datasets differ in their purpose: ours is for training and learning how to handle such data in practice, whereas the purpose of the CPRD synthetic dataset is to be a faithful representation of CPRD Aurum. The CPRD synthetic dataset only provides a binary outcome variable of heart attack or stroke within five years, whereas ours also provides the follow-up time to censoring or the event, thus additionally enabling classical survival analysis methods to be evaluated on the dataset and the censoring mechanisms to be examined. This was particularly useful in our training event, where we successfully used the synthetic dataset to compare the results of the classical survival analysis with those of machine learning for predicting heart attack or stroke. Our synthetic dataset was strengthened by including sophisticated realistic relationships between predictor variables and outcomes, as informed by literature searches. Missingness patterns, measurement errors, and informative censoring allow data scientists to tailor their classification training to include more advanced methodologies focused on the inclusion of missing data handling techniques (e.g., multiple imputation and ML imputation) and discussion of classification bias due to informative censoring. The additional benefit of spurious variables in the synthetic dataset encourages data scientists to stay up-to-date with the latest variable selection and model optimization techniques. Limitations include the synthetic dataset having less fidelity than synthetic datasets built directly from real primary care data13; however, as mentioned, this enabled us to avoid governance issues and allow the data to be made freely available. Our dataset was limited by not including all the predictive variables within QRISK3. However, we wanted to strike a balance between simulating a reasonably sized realistic dataset for training purposes and covering many types of variables (e.g., continuous, categorical, and various degrees of missingness) without adding unnecessary complexity. In conclusion, by making this realistic simulated dataset on a highly applicable health research topic available for open access, we endeavored to enhance professional training in the field of health and social care data analysis. The data were hosted by ARC Wessex and are openly available on the ARC Wessex website at https://www.arc-wx.nihr.ac.uk/data-sets. The data is also hosted by Zenodo, available at https://doi.org/10.5281/zenodo.1256741631. This project contains following dataset: 1. cvd_synthetic_dataset_v0.2.csv 2. cvd_synthetic_dataset_v0.2_metadata.xlsx Data are available under the terms of the Creative Commons Zero "No rights reserved’ data waiver (CC0 1.0 Public domain dedication). We would like to acknowledge all attendees of the 2023 NIHR Statistics Group – routine data event. Without your keen interest in professional development, this event would not have been possible. We would also like to thank the members of the organizing committee (Dr. Jessica Harris, Dr. Jianhua Wu, Dr. Jacqueline Birks, Dr. Ge Yu, and Dr. David Culliford) for their stimulating discussions that helped conceptualize the creation of the synthetic dataset. Faculty Opinions recommendedReferences - 1. Rothman KJ: Epidemiology: an introduction. Oxford university press, 2012. Reference Source - 2. Krittanawong C, Virk HUH, Bangalore S, et al.: Machine Learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep. 2020; 10(1): 16057. PubMed Abstract | Publisher Full Text | Free Full Text - 3. Alsuliman T, Humaidan D, Sliman L: Machine Learning and Artificial Intelligence in the service of medicine: necessity or potentiality? Curr Res Transl Med. 2020; 68(4): 245–251. 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Burns D, Richardson K, Driessens C: A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10–year follow-up period [Dataset]. Zenodo. 2024; Accessed 27th June 2024. http://www.doi.org/10.5281/zenodo.12567416 Author details Author details 1 School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK 2 NIHR Applied Research Collaboration Wessex, University of Southampton, Chilworth, Southampton, SO16 7NP, UK 3 Norwich Medical School, University of East Anglia, Norwich, England, NR4 7TJ, UK 2 NIHR Applied Research Collaboration Wessex, University of Southampton, Chilworth, Southampton, SO16 7NP, UK 3 Norwich Medical School, University of East Anglia, Norwich, England, NR4 7TJ, UK Dan Burns Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Kathryn Richardson Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Corine Driessens Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This project is funded by the National Institute for Health and Care Research (NIHR) [ARC Wessex (Reference Number NIHR2018870)] and the National Centre for Research Methods. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Dr. Dan Burns and Dr. Corine Driessens were the lead applicants for the funding applications. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright © 2024 Burns D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. metrics VIEWS $counts.viewCount downloads Citations CITE how to cite this article Burns D, Richardson K and Driessens C. A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.13651.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 01 Nov 2024 Views 0 How to cite this report: Waqar M. Reviewer Report For: A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.14821.r39582) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-39582 https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-39582 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 10 Mar 2026 Muhammad Waqar, National University of Modern Languages, Islamabad, Islamabad Capital Territory, Pakistan Approved with Reservations VIEWS 0 - The manuscript presents a synthetic healthcare dataset designed to support training and experimentation with statistical and machine learning models for predicting cardiovascular events within a 10-year follow-up period. The rationale for creating the dataset is clearly explained, - The manuscript presents a synthetic healthcare dataset designed to support training and experimentation with statistical and machine learning models for predicting cardiovascular events within a 10-year follow-up period. The rationale for creating the dataset is clearly explained, particularly the challenge of accessing real healthcare datasets due to privacy and ethical restrictions. - The dataset includes clinically relevant demographic, lifestyle, and medical variables, and it incorporates realistic data challenges such as missing values, noise, irrelevant predictors, and censoring, which increases its usefulness for training in healthcare data science and machine learning. - The dataset appears well structured and usable, and the variables are clearly described, making it accessible for researchers and students who wish to experiment with classification and survival analysis models. - Lack of validation against real-world data: The manuscript does not provide comparisons between the simulated dataset and real healthcare datasets. The authors should include validation showing that the distributions of variables and event rates resemble real clinical data. - Insufficient reproducibility details: The paper does not clearly provide the code or scripts used to generate the synthetic dataset. The authors should release the simulation code or provide a detailed workflow so that other researchers can reproduce the dataset. - Limited description of simulation parameters: Some assumptions and parameter values used in the simulation process are not fully described. Expanding the methodological details would improve transparency. - Absence of demonstration experiments: The manuscript does not include example analyses demonstrating how the dataset can be used with statistical or machine learning models (e.g., logistic regression, Cox models, or classification algorithms). - Lack of data visualization: The paper would benefit from including visual summaries such as variable distribution plots, correlation matrices, or event incidence graphs to better illustrate dataset characteristics. - Is the rationale for creating the dataset(s) clearly described? Yes - Are the protocols appropriate and is the work technically sound? Partly - Are sufficient details of methods and materials provided to allow replication by others? Partly - Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: My area of research is machine learning and its application in health care domain CITE HOW TO CITE THIS REPORT Waqar M. Reviewer Report For: A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.14821.r39582) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-39582 https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-39582 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Views 0 How to cite this report: Sabouri M. Reviewer Report For: A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.14821.r33475) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-33475 https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-33475 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 25 Nov 2024 Maziar Sabouri, The University of British Columbia, Vancouver, British Columbia, Canada Approved with Reservations VIEWS 0 Great work! I have no doubt that this kind of data generation will significantly contribute to the growth of AI in medicine. However, I do have a major concern: why haven’t you trained a model using this ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close I have no doubt that this kind of data generation will significantly contribute to the growth of AI in medicine. However, I do have a major concern: why haven’t you trained a model using this ... Continue reading Great work! I have no doubt that this kind of data generation will significantly contribute to the growth of AI in medicine. However, I do have a major concern: why haven’t you trained a model using this dataset and then evaluated it with external, I mean real-world data? How can we be confident that this dataset is reliable and genuinely beneficial for the training process? You could consider several strategies to validate its effectiveness, such as: I have no doubt that this kind of data generation will significantly contribute to the growth of AI in medicine. However, I do have a major concern: why haven’t you trained a model using this dataset and then evaluated it with external, I mean real-world data? How can we be confident that this dataset is reliable and genuinely beneficial for the training process? You could consider several strategies to validate its effectiveness, such as: - Using only real data. - Using only synthetic data. - Combining real and synthetic data. - Is the rationale for creating the dataset(s) clearly described? Yes - Are the protocols appropriate and is the work technically sound? Yes - Are sufficient details of methods and materials provided to allow replication by others? Yes - Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI in medicine CITE HOW TO CITE THIS REPORT Sabouri M. Reviewer Report For: A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2024, 4:67 (https://doi.org/10.3310/nihropenres.14821.r33475) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-33475 https://openresearch.nihr.ac.uk/articles/4-67/v1#referee-response-33475 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Alongside their report, reviewers assign a status to the article: - Approved - Approved with reservations - Not approved | Invited Reviewers | || |---|---|---| | 1 | 2 | | | Version 1 01 Nov 24 | read | read | - Maziar Sabouri, The University of British Columbia, Vancouver, Canada - Muhammad Waqar, National University of Modern Languages, Islamabad, Pakistan Sign up for content alerts You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' - Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. - You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. - You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). - You work at the same institute as any of the authors. - You hope/expect to benefit (e.g. favour or employment) as a result of your submission. - You are an Editor for the journal in which the article is published. 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