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Application of the Marginal Structural Model to Account for Suboptimal Adherence in a Randomized Controlled Trial

James Rochon1, Manjushri Bhapkar2, Carl F Pieper3

  • 1Rho Federal Systems, 6330 Quadrangle Drive, Chapel Hill, NC 27517, USA.

Contemporary Clinical Trials Communications
|December 1, 2016
PubMed
Abstract

Insights

This study introduces a causal modeling approach to adjust for suboptimal adherence in clinical trials. The method predicts intervention effects at full adherence, offering insights beyond traditional intention-to-treat analyses.

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Suboptimal adherence in randomized controlled trials (RCTs) presents analytical challenges.
  • Traditional methods like per-protocol analysis or censoring introduce biases.
  • Causal modeling offers a novel approach to address adherence issues.

Purpose of the Study:

  • To apply causal modeling, specifically marginal structural models with inverse-probability weighting, to adjust for adherence in RCTs.
  • To predict intervention effects under conditions of full adherence.
  • To gain insights into the intrinsic physiological effects of interventions by comparing with intention-to-treat results.

Main Methods:

  • Implementation of a marginal structural model using inverse-probability weighting via a weighted generalized estimating equation model.
  • Development of two ancillary models: stepwise linear regression for percent weight loss and stepwise logistic regression for early discontinuation.
  • Calculation of participant- and time-specific weights based on ancillary models.

Main Results:

  • The developed causal model can effectively predict intervention effects at full adherence.
  • Comparison of model predictions with intention-to-treat results provides deeper understanding of intervention efficacy.
  • The approach allows for nuanced analysis of adherence impact in clinical trials.

Conclusions:

  • The marginal structural model with inverse-probability weighting is a complex but powerful tool for analyzing adherence in RCTs.
  • Careful consideration of model details, including variable selection, interactions, and time-dependent covariates, is crucial for accurate application.
  • This method enhances the ability to interpret intervention effects and understand physiological impacts, complementing traditional trial analyses.
Keywords:
adherenceagingcalorie restrictioncausal modelingmarginal structural modelweighted GEE model

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