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Evaluating a Method to Estimate Mediation Effects With Discrete-Time Survival Outcomes
Amanda Jane Fairchild1, Chao Cai1, Heather McDaniel2
1Department of Psychology, University of South Carolina, Columbia, SC, United States.
Frontiers in Psychology
|April 27, 2019
Summary
This study validates a regression-based model for mediation analysis with discrete-time survival data. The model accurately estimates mediation effects, supporting its use in etiological and intervention research.
Area of Science:
- Biostatistics
- Epidemiology
- Psychology
Background:
- Mediation analysis is crucial for understanding event timing mechanisms.
- Appropriate methodology is essential for accurate mediation effect estimation.
- Discrete-time survival outcomes require specialized analytical approaches.
Purpose of the Study:
- To evaluate a regression-based approach for mediation effects with discrete-time survival data.
- To assess the performance of the discrete-time survival mediation model via simulation.
- To compare results with a potential-outcomes framework.
Main Methods:
- Statistical simulation study.
- Regression-based mediation model for discrete-time survival outcomes.
- Empirical evaluation of parameter accuracy, precision, and Type 1 error rates.
Main Results:
- Parameter estimates for mediation were statistically accurate and precise.
- Type 1 error rates were tolerable across examined conditions.
- Medium to large effect sizes were needed for adequate power with binary X and continuous M.
Conclusions:
- The discrete-time survival mediation model is a valid and reliable tool.
- The model's results are functionally equivalent to the potential-outcomes framework.
- The model can strengthen etiological research and inform intervention strategies.
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