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A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making.

Indrabati Bhattacharya1, Brent A Johnson2, William J Artman2

  • 1Department of Statistics, Florida State University, Tallahassee, Florida, USA.

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|April 10, 2023
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Summary
This summary is machine-generated.

This study introduces a novel Bayesian method using principal stratification to accurately estimate treatment effects in trials with low patient compliance. This approach overcomes biases and improves generalizability in complex sequential treatment studies.

Keywords:
Dirichlet process mixtureGaussian copulaSMARTdynamic treatment regimepartial compliance

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Health Services Research

Background:

  • Intention-to-treat (ITT) analyses are standard but can be biased and lack generalizability in studies with significant non-compliance.
  • Substance use disorder trials, like the ENGAGE study, often face low patient compliance, challenging traditional analysis methods.
  • Existing methods struggle to estimate true treatment effects when patient adherence varies significantly.

Purpose of the Study:

  • To develop a method for estimating the mean outcome under a dynamic treatment regime, accounting for patient compliance.
  • To address the limitations of intention-to-treat analyses in multi-stage trials with low compliance.
  • To provide generalizable and reproducible treatment effect estimates in challenging clinical settings.

Main Methods:

  • Proposed a non-parametric Bayesian approach utilizing principal stratification.
  • Employed a Gaussian copula model for potential compliance distributions.
  • Used a Dirichlet process mixture model for treatment sequence-specific outcomes.

Main Results:

  • Simulation studies demonstrated the utility of the proposed approach in multi-stage randomized trials.
  • The method effectively estimates treatment effects conditional on potential compliance strata.
  • The estimator showed robustness in non-linear and non-Gaussian scenarios.

Conclusions:

  • The principal stratification-based Bayesian method offers a robust alternative to intention-to-treat analyses for sequential treatment studies.
  • This approach enhances the accuracy and generalizability of treatment effect estimation in the presence of non-compliance.
  • The findings are particularly relevant for complex trials involving substance use disorders and adaptive treatment strategies.