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This study introduces a new statistical model to accurately estimate treatment effects in clinical trials, even when participants don't fully follow study rules. The method effectively reduces bias from unobserved factors, improving causal inference for adherence research.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Causal Inference

Background:

  • Variable adherence to randomized clinical trial protocols is a frequent challenge.
  • This impacts the accurate estimation of treatment effects and causal inference.

Purpose of the Study:

  • To propose a generalized modeling framework for estimating the causal effect of variable adherence on outcomes.
  • To adjust for unobserved confounders in longitudinal data structures.
  • To provide a method for estimating local average treatment effects among compliers.

Main Methods:

  • A nonlinear, nonparametric random-coefficients modeling approach was developed.
  • Two techniques, residual inclusion and nonparametric random-coefficients modeling, were combined to address unobserved confounding.
  • This resulted in a 2-stage residual inclusion, instrumental variables model.

Main Results:

  • Simulation studies demonstrated that the proposed estimator significantly reduces bias compared to standard methods.
  • Bias reduction was most pronounced with higher residual variance and time-varying confounders.
  • The method was applied to neurocognitive outcome data from a sleep medicine trial.

Conclusions:

  • The proposed framework is flexible for various outcome distributions and longitudinal data.
  • It effectively reduces bias stemming from unobserved confounders.
  • This enhances the reliability of causal effect estimation in the presence of non-adherence.