Predicting participant dropout in longitudinal research studies

Overview

Participant dropout is one of the most costly challenges in longitudinal research. This project uses simulated data modeled on realistic lab conditions to explore whether intake-level variables can predict which participants are most at risk of dropping out before it happens.

Background

During two years as a research assistant, I observed firsthand how dropout disrupted longitudinal studies. This project applies data analysis to a problem I watched play out in real time.

Dataset

Simulated dataset of 200 participants with the following features:

• Age
• Commute distance (miles)
• Session length (60 vs 120 minutes)
• Sessions completed (1, 2, or 3)
• Employment status (employed, unemployed, student)

Overall dropout rate: 38.5%

Model

Logistic regression trained on 80% of data, tested on held-out 20%.

Results:

• Accuracy: 75%
• Dropout Precision: 93%
• Dropout Recall: 61%
• F1-score: 0.75

Key finding

Sessions completed was the strongest predictor of dropout (importance: 1.104), followed by employment status (0.381). Unemployed and student participants dropped out at higher rates than employed participants, consistent with published retention literature.

Tools

Python, pandas, numpy, scikit-learn, matplotlib, seaborn

Files

• PT Retention.ipynb — full analysis notebook
• Exploratory_Graph.png — exploratory charts
• Feature_Importance_Graph.png — feature importance visualization

References

• Glomstad, M., Sorby, I. D., Holt, T., & Bjørkly, S. (2023). Client predictors of therapy dropout in a primary care setting: A prospective cohort study. BMC Psychiatry. https://pmc.ncbi.nlm.nih.gov/articles/PMC10207790/
• Gustavson, K., Røysamb, E., von Soest, T., Mathiesen, K. S., & Karevold, E. (2012). Attrition and generalizability in longitudinal studies. BMC Research Notes. https://doi.org/10.1186/1756-0500-5-1
• Melanie McGovern, Joar Øveraas Halvorsen, Marte Svarver Ørstavik, Bård Dyrdal, & Stål Bjørkly. (2024). Who will stay and who will go? Identifying risk factors for psychotherapy dropout. Counselling and Psychotherapy Research. https://doi.org/10.1002/capr.12783
• Ribisl, K. M., Walton, M. A., Mowbray, C. T., Luke, D. A., Davidson, W. S., & Bootsmiller, B. J. (1996). Minimizing participant attrition in panel studies through the use of effective retention and tracking strategies: Review and recommendations. Evaluation and Program Planning, 19(1), 1–25.
• Swift, J. K., & Greenberg, R. P. (2012). Premature discontinuation in adult psychotherapy: A meta-analysis. Journal of Consulting and Clinical Psychology, 80(4), 547–559. https://doi.org/10.1037/a0028226