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Optimising dynamic treatment regimens using sequential multiple assignment randomised trials data with missing data.

Jessica Xu1, Anurika P De Silva1, Katherine J Lee2,3

  • 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.

BMC Medical Research Methodology
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

Complete case analysis (CCA) and multiple imputation (MI) were evaluated for handling missing data in sequential multiple assignment randomized trials (SMARTs) using Q-learning. MI showed greater bias and failed to capture treatment effect differences when stage 2 effects varied, especially with missingness dependent on other variables.

Keywords:
Missing dataMultiple imputationQ-learningSequential multiple assignment randomised trials

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Health Research Methods

Background:

  • Dynamic treatment regimens are crucial for chronic conditions.
  • Sequential Multiple Assignment Randomized Trials (SMARTs) optimize these regimens.
  • Missing data poses challenges in SMARTs, particularly with Q-learning analysis.

Purpose of the Study:

  • To evaluate the performance of Complete Case Analysis (CCA) and Multiple Imputation (MI) for estimating Q-learning parameters in a two-stage SMART.
  • To compare CCA and MI under various missing data scenarios and treatment effect conditions.

Main Methods:

  • Simulated 1000 datasets for a two-stage SMART with varying missing data percentages (20%, 40%) and missingness mechanisms (m-DAGs).
  • Assessed bias and empirical standard errors for Q-learning parameter estimation.
  • Validated findings with retrospective data from a smoking cessation SMART.

Main Results:

  • CCA and MI showed similar performance with minimal bias when no treatment effect existed or when stage 2 effects were large and consistent.
  • MI exhibited greater bias and higher standard errors compared to CCA when stage 2 treatment effects varied and data were missing conditionally.
  • MI failed to accurately capture treatment effect differences in a two-stage SMART when stage 2 effects varied among participants.

Conclusions:

  • Complete Case Analysis (CCA) demonstrated more robust performance than Multiple Imputation (MI) in estimating Q-learning parameters within a two-stage SMART under complex missing data scenarios.
  • Multiple Imputation (MI) may introduce bias and mask true treatment effect variations in SMARTs when missingness is dependent on patient history or outcomes.
  • Further research is needed to develop and validate advanced methods for handling missing data in Q-learning analyses of SMARTs.