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Inference in group sequential designs with causal mechanisms: implications for power and mediation analysis.

Kim May Lee1, Richard Emsley2

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Summary
This summary is machine-generated.

Group sequential designs may reduce power for mediation analysis if causal mechanisms aren't considered in sample size calculations. Estimators for mediation pathways can be biased, requiring careful reporting and further research.

Keywords:
Conditional maximum likelihood estimatorDirect effectGroup sequential designsIndirect effectsMediation analysisPenalized maximum likelihood estimator

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Causal Inference

Background:

  • Group sequential designs allow early trial stopping with statistical rigor.
  • Mediation analysis properties are unknown in group sequential designs, despite common use in psychological trials.

Purpose of the Study:

  • Evaluate analysis techniques for mediation in group sequential designs.
  • Assess bias of estimators for total, direct, and indirect effects.
  • Compare maximum likelihood estimators under early stopping scenarios.

Main Methods:

  • Simulation study of a group sequential design with one interim analysis.
  • Analysis of continuous outcomes with a continuous mediating variable.
  • Examination of null hypothesis rejection rates and estimator bias.

Main Results:

  • Reduced power in group sequential designs when sample size ignores causal mechanisms.
  • Maximum likelihood estimator is unbiased only for the mediator-outcome path.
  • Conditional maximum likelihood estimator shows less bias for total and direct effects.

Conclusions:

  • Mediation analysis requires specific consideration in group sequential designs.
  • Sample size calculations must account for causal mechanism variability.
  • Multiple estimators may be needed, acknowledging potential bias in indirect effect estimation.