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Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects.

Xueqi Wang1, Elizabeth L Turner1, Fan Li2

  • 1Department of Biostatistics & Bioinformatics and Duke Global Health Institute, Duke University School of Medicine, Durham, NC, USA.

Contemporary Clinical Trials
|February 5, 2022
PubMed
Summary

In cluster randomized trials (CRTs) with varying cluster sizes, using two distinct weights in generalized estimating equations (GEE) can bias unit average treatment effect (UATE) estimates. An independence working correlation matrix with inverse cluster size weighting ensures valid UATE estimation.

Keywords:
Cluster randomized trialsGeneralized estimating equationsHeterogeneity of treatment effectsUnit average treatment effectWeighting

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Cluster randomized trials (CRTs) involve hierarchical data structures.
  • Variable cluster sizes pose analytical challenges, particularly for generalizing to cluster populations.
  • The unit average treatment effect (UATE) is a key estimand when generalizing CRTs to a population of clusters.

Purpose of the Study:

  • To identify potential biases in estimating the UATE in CRTs with variable cluster sizes using generalized estimating equations (GEE).
  • To evaluate the impact of using multiple weighting schemes within the GEE framework.
  • To propose a valid analytical approach for UATE estimation in such settings.

Main Methods:

  • Theoretical derivations to assess the properties of different analytical approaches.
  • A simulation study to corroborate theoretical findings.
  • Application to real-world data from a colorectal cancer screening CRT.

Main Results:

  • Using both cluster size weights and covariance weights in GEE leads to biased or inefficient UATE estimates.
  • An analytical approach combining an independence working correlation matrix with inverse cluster size weighting provides valid UATE estimates.
  • The combination of two distinct weights in GEE analysis is shown to be problematic for UATE estimation.

Conclusions:

  • Standard GEE approaches with common weighting strategies can yield invalid UATE estimates in CRTs with variable cluster sizes.
  • An independence working correlation matrix and inverse cluster size weighting is the recommended method for valid UATE estimation.
  • Careful consideration of weighting and correlation structures is crucial for accurate analysis of CRTs.