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Random effect misspecification in stepped wedge designs.

Emily C Voldal1, Fan Xia2, Avi Kenny1

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|March 8, 2022
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Summary
This summary is machine-generated.

Misspecifying random effects in stepped wedge cluster randomized trials can lead to inaccurate treatment effect estimates. A correctly specified model is crucial for valid and efficient analysis, especially with fewer than two sequences.

Keywords:
Stepped wedgemodel misspecificationmodel selectionrandom effectsvariance estimation

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Stepped wedge cluster randomized trials (SW-CRTs) are frequently analyzed using linear mixed effects models.
  • These models often incorporate random effects for cluster, time, and/or treatment, but misspecification can impact results.

Purpose of the Study:

  • To investigate the consequences of misspecifying the random effects structure in linear mixed effects models for SW-CRTs.
  • To evaluate the impact on the validity and efficiency of estimated treatment effects.

Main Methods:

  • Examined two misspecification scenarios in cross-sectional SW-CRT models: omitting random time effects when present, and omitting random treatment effects when present.
  • Derived the variance of the estimated treatment effect under misspecification.
  • Defined validity and efficiency as measures of misspecification impact.

Main Results:

  • Validity was generally less than 1.0 (anti-conservative), except in specific cases with two sequences.
  • Efficiency was consistently less than 1, influenced by intracluster correlation, variance component magnitudes, and number of sequences.
  • For classic SW-CRTs with two sequences, random time effects models showed less anti-conservative inference than random treatment effect models.

Conclusions:

  • Misspecification of random effects in SW-CRT analyses can lead to anti-conservative or inefficient treatment effect estimates.
  • Model specification choices significantly impact inferential accuracy.
  • For two-sequence SW-CRTs, prioritizing random time effects in the model may offer more robust inference.