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Inverse probability weighted estimation of dynamic treatment regimen means in sequential multiple assignment

Jessica Xu1, Robert K Mahar2,3,4, Katherine J Lee3,5

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Summary
This summary is machine-generated.

Multiple imputation (MI) demonstrated minimal bias and slightly lower standard errors compared to complete case analysis (CCA) for estimating dynamic treatment regimen (DTR) means in sequential multiple assignment randomized trials (SMARTs). This finding is crucial for handling missing data in complex clinical trial designs.

Keywords:
Dynamic treatment regimensInverse probability weightingMissing dataMultiple imputationSequential multiple assignment randomised trials

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Services Research

Background:

  • Dynamic treatment regimens (DTRs) personalize sequential treatment decisions in various diseases.
  • Sequential multiple assignment randomized trials (SMARTs) evaluate and optimize DTRs through multi-stage randomization.
  • Inverse probability weighting (IPW) is commonly used to estimate DTR means in SMARTs, but missing data poses a challenge.

Purpose of the Study:

  • To evaluate the performance of complete case analysis (CCA) and multiple imputation (MI) in handling missing data for estimating DTR mean outcomes using IPW within a two-stage SMART.
  • To compare the bias and standard errors of CCA and MI under various missing data scenarios and proportions.

Main Methods:

  • Simulated 1000 datasets with 400 participants each, based on a two-stage SMART design where non-responders are re-randomized.
  • Estimated four DTR means using IPW, assessing performance of CCA and MI under missing data scenarios defined by m-DAGs.
  • Evaluated missing data proportions (20%, 40%) and dependence on stage 1 outcome, stage 2 treatment, and final outcome.

Main Results:

  • MI exhibited minimal bias in most scenarios, except when stage 1 intermediate outcome was missing dependent on baseline variables and stage 1 treatment.
  • CCA showed greater bias than MI when data were missing dependently on other variables (e.g., stage 2 treatment missing based on stage 1 outcome).
  • Empirical standard errors were comparable, with MI generally yielding slightly lower values.

Conclusions:

  • For the studied prototypical SMART design, MI is generally preferred over CCA for estimating DTR mean outcomes via IPW due to minimal bias and slightly lower standard errors.
  • MI proves effective in handling missing data complexities inherent in sequential randomization and intermediate outcome dependencies within SMARTs.