DOI: 10.5281/zenodo.17951643
title: Synthetic Longitudinal Mental Health Dataset (1M Sample; PHQ-9, GAD-7, PCL-5)

description:  

 

This record releases a research-grade synthetic longitudinal mental health dataset designed for methodological research, benchmarking, and education in contexts where access to real mental-health microdata is ethically or legally restricted.

The dataset simulates weekly longitudinal observations for individuals, capturing symptom dynamics related to depression, anxiety, and post-traumatic stress disorder (PTSD) using established psychometric instruments with item-level ordinal response generation:





PHQ-9 (9 items and total score)




GAD-7 (7 items and total score)




PCL-5 (20 items and total score), including PTSD symptom cluster subscales B, C, D, and E



The publicly released archive (mh_10m_sample_1m.zip) contains a 1,000,000-row Parquet sample suitable for open distribution and rapid experimentation. This sample is derived from a larger synthetic simulation comprising 10,617,183 total longitudinal observations, as reported in the accompanying validation summary.



Dataset contents

Each observation includes multiple classes of variables commonly encountered in applied mental-health research:

Psychometric variables





Item-level responses and total scores for PHQ-9, GAD-7, and PCL-5




PTSD cluster-level subscale scores (B/C/D/E)



Demographic and contextual covariates





Age, gender, ethnicity (coded categories)




Socioeconomic status (SES) indicators




Urban/rural indicator




Education level, profession, and marital status



Clinical history and risk proxies





Family mental-health history indicator




Chronic illness indicators (including diabetes, cardiovascular disease, gastrointestinal conditions, and thyroid/hormonal conditions)




Trauma exposure proxy



Behavioral and contextual measures





Sleep, stress, social support, and physical activity measures



Wearable-style proxies





Step counts




Screen time




Heart-rate variability (HRV)



Study design and reporting realism





Recruitment source indicators and sampling weights




Longitudinal attrition indicators (dropout and withdrawal)




Simulated response distortion indicators (e.g., underreporting flags)




Measurement and lab-bias indicators



Treatment and longitudinal dynamics





Time-varying treatment indicator




Treatment adherence intensity




Repeated observations per individual enabling longitudinal prediction and causal analysis





Synthetic generation procedures

The data generation process was explicitly designed to resemble real observational mental-health datasets by modeling the mechanisms that introduce statistical complexity in applied research:





Latent symptom trajectories over timeDepression, anxiety, and PTSD latent states evolve longitudinally with temporal dependence and stochastic perturbations, producing realistic within-individual variability across repeated observations.




Item-level psychometric modelingQuestionnaire items are generated from latent states using graded ordinal response logic rather than sampling aggregate scores directly, preserving scale structure and high internal consistency.




Realistic missingness and response behaviorItem-level nonresponse is simulated at empirically plausible rates, with additional response-behavior mechanisms (e.g., underreporting and response distortion) included to reflect measurement error commonly observed in self-report mental-health data.




Selection, recruitment, and attrition mechanismsParticipation is shaped by recruitment and selection processes, followed by longitudinal attrition, producing incomplete and unbalanced panels typical of real cohort studies.




Time-varying treatment and confoundingTreatment assignment varies over time and is confounded by evolving symptom severity and covariates, enabling evaluation of causal inference methods under realistic non-random treatment assignment.





Validation and benchmarking

Validation and benchmarking were conducted on a random 80,000-row evaluation sample, with results provided in the accompanying files (metrics.csv, DIF outputs):





Total simulated dataset size: 10,617,183 rows




Item-level missingness rates:





PHQ-9 ≈ 8.19%




GAD-7 ≈ 8.11%




PCL-5 ≈ 11.10%






Internal consistency (Cronbach’s α):





PHQ-9 ≈ 0.965




GAD-7 ≈ 0.954




PCL-5 ≈ 0.980






Predictive benchmarking:





Severe depression classification performance: AUC ≈ 0.93






Imputation benchmarking:





Masked PHQ-1 recovery: RMSE ≈ 0.48






Causal benchmarking:





Doubly robust average treatment effect (ATE) estimation




Comparison with a simulated counterfactual subset (reported in metrics.csv)





Measurement bias and invariance screening outputs are included:





Mantel–Haenszel DIF results (DIF_MH_PHQ_gender.csv)




Logistic regression DIF results (DIF_LR_PHQ_gender.csv)





Files included in this record





mh_10m_sample_1m.zip — 1,000,000-row Parquet dataset




metrics.csv — validation and benchmark summary




DIF_MH_PHQ_gender.csv — Mantel–Haenszel DIF results




DIF_LR_PHQ_gender.csv — logistic regression DIF results




eval_log.txt — evaluation log (transparency artifact)





Scientific scope and intended use

This dataset is explicitly intended for research use, including:





Statistical and machine-learning benchmarking




Missing-data and imputation research




Fairness and measurement-bias (DIF) analysis




Longitudinal modeling




Causal inference and treatment-effect estimation




Teaching and demonstration involving sensitive mental-health data structures





Limitations and appropriate use

This dataset is fully synthetic and does not represent real patient populations.It is intended for methods research and analytical benchmarking, not for:





clinical decision-making or diagnosis,




estimation of real-world prevalence, incidence, or risk,




drawing substantive clinical or epidemiological conclusions about real populations.



All statistical relationships are simulated under explicit modeling assumptions and are designed to support evaluation of analytical methods rather than inference about real-world mental health outcomes.