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Addressing selection bias in cluster randomized experiments via weighting.

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Summary
This summary is machine-generated.

Cluster randomized trials with post-randomization recruitment can cause selection bias. This study defines causal estimands and uses inverse probability weighting to estimate treatment effects in recruited populations, addressing bias in cluster randomized experiments.

Keywords:
causal inferencecluster randomized trialprincipal stratificationselection biassensitivity analysisworking propensity score

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Cluster randomized experiments often recruit participants after treatment assignment, leading to potential selection bias.
  • Data availability is typically limited to the recruited sample, creating discrepancies between overall and recruited populations.
  • Post-randomization recruitment can induce systematic differences between intervention and control arms within the recruited sample.

Purpose of the Study:

  • To define causal estimands for both overall and recruited populations in cluster randomized experiments with post-randomization recruitment.
  • To develop methods for estimating treatment effects despite potential selection bias.
  • To identify meaningful subpopulations within the overall population for which treatment effects can be estimated.

Main Methods:

  • Defining causal estimands for overall and recruited populations.
  • Proving consistent estimation of the average treatment effect on the recruited population using inverse probability weighting under ignorable recruitment.
  • Utilizing principal stratification to identify treatment effects on specific subpopulations.
  • Developing an estimation strategy and sensitivity analysis for the ignorable recruitment assumption.
  • Implementing methods in the CRTrecruit R package.

Main Results:

  • Under the assumption of ignorable recruitment, the average treatment effect on the recruited population can be consistently estimated using inverse probability weighting.
  • The average treatment effect on the overall population is generally not identifiable.
  • Treatment effects can be identified for subpopulations: those always recruited and those recruited only under treatment.
  • Application to the ARTEMIS trial showed increased P2Y$_{12}$ inhibitor persistence among the always-recruited population.

Conclusions:

  • Methods are provided to address selection bias in cluster randomized experiments with post-randomization recruitment.
  • The study offers a framework for estimating treatment effects in specific subpopulations when overall population effects are not identifiable.
  • The CRTrecruit R package and sensitivity analysis aid in applying and validating these methods in real-world studies.