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Dynamic path analysis for exploring treatment effect mediation processes in clinical trials with time-to-event
Matthias Kormaksson1, Markus Reiner Lange1, David Demanse1
1Analytics, Development, Novartis Pharma AG, Basel, Switzerland.
Statistics in Medicine
|August 7, 2024
Summary
Dynamic path analysis explains why biomarker improvements don't always lead to survival benefits in cancer treatment. This method decomposes treatment effects, offering insights into drug development for time-to-event outcomes.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Pharmacometrics
Background:
- Longitudinal biomarkers often predict survival but their treatment effects may not improve overall survival.
- This paradox necessitates advanced analytical methods to understand treatment's impact on survival outcomes.
Purpose of the Study:
- To introduce and detail the dynamic path analysis framework for mediation analysis with longitudinal mediators and survival outcomes.
- To decompose the total treatment effect into direct and indirect effects over time.
Main Methods:
- Application of dynamic path analysis, a mediation analysis technique.
- Analysis of longitudinal mediator data and time-to-event survival data.
- Decomposition of treatment effects into direct and indirect components evolving over time.
Main Results:
- Demonstrated the utility of dynamic path analysis in resolving the paradox of biomarker improvement without survival benefit.
- Illustrated the methodology's application using both simulated and real oncology data.
- Provided mechanistic insights into treatment effects through time-dependent effect evolution.
Conclusions:
- Dynamic path analysis offers a robust framework for survival mediation analysis in drug development.
- The methodology enhances understanding of treatment effects when time-to-event is the primary outcome.
- Applicable to various scenarios in clinical research involving longitudinal mediators and survival endpoints.
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