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Estimation of comparable standardized mean differences in cluster randomized trials with covariate adjustment
Juyoung Jung1, Zhijiang Liu1, Ariel M Aloe1
1University of Iowa, Iowa City, Iowa, USA.
Summary
Standardized mean differences (SMDs) in cluster-randomized trials can be inflated by covariate adjustment. We propose a rescaling method using pseudo-R² to correct effect sizes, improving comparability across studies.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Health Research Methods
Background:
- Standardized mean differences (SMDs) are crucial for quantifying treatment effects in cluster-randomized trials.
- Covariate adjustment in hierarchical models can inflate SMDs by reducing residual variance, hindering cross-study comparability.
Purpose of the Study:
- To introduce a unified family of estimators for covariate-adjusted SMDs.
- To ensure effect size estimates are on a common reference scale for improved comparability.
Main Methods:
- Rescaling covariate-adjusted variance components using pseudo-R² indices to recover unadjusted variance.
- Accommodating level-1, level-2, and both-level covariate adjustments.
- Developing method of moments, maximum likelihood, and t-statistic estimators with variance approximations.
Main Results:
- The proposed rescaling method corrects for artificial inflation of SMDs due to covariate adjustment.
- Effect size estimates are placed on a common scale, enhancing comparability across different model specifications.
- An empirical example and simulation study demonstrated the method's utility and the risks of omitting the correction.
Conclusions:
- The proposed unified framework provides accurate and comparable covariate-adjusted SMDs in cluster-randomized trials.
- This approach is essential for reliable meta-analysis and interpretation of treatment effects.
- Implementing these corrections is vital to avoid misleading effect size estimates.
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