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The challenges of evaluating dose response in flexible-dose trials using marginal structural models
Ilya Lipkovich1, Craig H Mallinckrodt, Douglas E Faries
1Quintiles, Inc., 4820 Emperor Blvd Durham,NC 27703, USA. Ilya.Lipkovich@quintiles.com
Pharmaceutical Statistics
|October 13, 2012
Summary
Assessing dose response in flexible-dose clinical trials is challenging. Marginal structural models with inverse probability of treatment-weighting (IPTW) offer a method to reduce bias, but require careful implementation due to sensitivity to model misspecification.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmacometrics
Background:
- Assessing dose-response relationships in flexible-dose clinical trials presents significant challenges.
- Observed data can obscure or reverse true dose effects due to the relationship between dose and both prior and subsequent outcomes.
Purpose of the Study:
- To introduce and evaluate a methodology for unbiased dose-response assessment in flexible-dose trials.
- To address selection bias inherent in traditional analysis methods.
Main Methods:
- Proposed marginal structural models (MSMs) utilizing inverse probability of treatment-weighting (IPTW).
- Employed weighted pooled repeated measures analysis (generalized estimating equations) to compare potential clinical outcomes across dose groups.
- Dose effect was modeled using current and recent dose history, with weights derived from logistic regression.
Main Results:
- Simulations demonstrated that the IPTW MSM methodology yields nearly unbiased estimates of true dose effects across various scenarios.
- The method showed sensitivity to model misspecification, even with known weights.
- Comparison with unweighted and other weighted analyses highlighted the performance of IPTW MSM.
Conclusions:
- IPTW MSMs provide a promising approach for analyzing dose-response in flexible-dose trials, mitigating selection bias.
- Caution is advised for practitioners due to the methodology's sensitivity to model misspecification when applied to real-world clinical data.
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