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Replicable Bandits for Digital Health Interventions.

Kelly W Zhang1, Nowell Closser2, Anna L Trella2

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Summary
This summary is machine-generated.

Adaptive algorithms in digital health trials can lead to unreliable statistical results. This study defines "replicable bandit algorithms" ensuring consistent and accurate causal inference for digital health interventions.

Keywords:
adaptive treatment assignmentbandit algorithmsdigital healthreplicability

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Area of Science:

  • Digital Health
  • Clinical Trials
  • Causal Inference

Background:

  • Adaptive treatment assignment algorithms, like bandit algorithms, are prevalent in digital health clinical trials.
  • Data from these trials often informs intervention refinement and broader deployment decisions.
  • Inference for adaptive algorithm-dependent estimands, such as mean reward, is crucial but challenging.

Purpose of the Study:

  • To investigate the replicability of statistical analyses in trials using adaptive treatment assignment.
  • To identify why standard statistical estimators may fail in these adaptive settings.
  • To introduce a formal definition and framework for replicable adaptive algorithms.

Main Methods:

  • Theoretical analysis of statistical estimators under adaptive algorithms.
  • Introduction and formal definition of "replicable bandit algorithms".
  • Simulation studies using a mobile health oral health self-care intervention.

Main Results:

  • Standard statistical estimators can be inconsistent and non-replicable in adaptive trials, even with large sample sizes.
  • Non-replicability is intrinsically linked to the properties of the adaptive algorithm.
  • Under "replicable bandit algorithms", common estimators are guaranteed to be consistent and asymptotically normal.

Conclusions:

  • Designing adaptive algorithms with replicability is essential for reliable digital health interventions.
  • Replicated evidence is critical for deployment decisions in digital health.
  • Further research is needed on the interplay between adaptive algorithm design, statistical inference, and experimental replicability.