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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Assessing Mediation in Cross-Sectional Stepped Wedge Cluster Randomized Trials.

Zhiqiang Cao1, Fan Li2,3,4

  • 1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, P. R. China.

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Summary
This summary is machine-generated.

This study introduces new regression methods for mediation analysis in stepped wedge cluster randomized trials (SW-CRTs) with correlated data. These methods help understand treatment effect mechanisms, even with complex exposure-time variations.

Keywords:
Jackknife variancemediation analysismediation proportionnatural indirect effectstepped wedge cluster randomized trialstime‐dependent treatment effect

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Mediation analysis is crucial for understanding treatment effect mechanisms.
  • Existing methods are limited for correlated data in stepped wedge cluster randomized trials (SW-CRTs).
  • Understanding mediation in SW-CRTs is vital for public health interventions.

Purpose of the Study:

  • To develop and present novel regression-based methods for mediation analysis in SW-CRTs.
  • To extend mediation analysis to handle correlated data structures common in SW-CRTs.
  • To provide tools for estimating natural indirect effects and mediation proportions in complex SW-CRT designs.

Main Methods:

  • Utilized linear and generalized linear mixed models for mediation analysis.
  • Developed estimators for natural indirect effect and mediation proportion.
  • Derived mediation expressions accounting for exposure-time treatment effect heterogeneity.
  • Proposed methods applicable to continuous and binary mediators and outcomes.

Main Results:

  • The developed mediation estimators demonstrate good performance across various data types and treatment effect structures.
  • The methods successfully handle correlated data within SW-CRTs.
  • Effectiveness shown for both typical and complex SW-CRT scenarios with time-varying effects.

Conclusions:

  • The proposed regression-based methods offer a robust framework for mediation analysis in SW-CRTs.
  • These methods enhance the understanding of treatment effect mechanisms in complex trial designs.
  • The accompanying R package, mediateSWCRT, facilitates practical application and implementation.