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The impact of covariate misclassification using generalized linear regression under covariate-adaptive randomization
Liqiong Fan1, Sharon D Yeatts1, Bethany J Wolf1
11 Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Statistical Methods in Medical Research
|November 25, 2015
Summary
Covariate misclassification in adaptive randomization trials impacts treatment assignment and analysis. Statistical operating characteristics, like power and bias, are negatively affected, differing by outcome type.
Area of Science:
- Clinical Trials
- Biostatistics
- Statistical Modeling
Background:
- Covariate adaptive randomization is used to balance treatment groups.
- Misclassifying covariates can disrupt intended treatment assignments and complicate analysis strategies.
Purpose of the Study:
- To investigate the impact of covariate misclassification on the statistical operating characteristics of clinical trials using covariate adaptive randomization.
- To compare different analysis strategies under covariate misclassification.
Main Methods:
- Simulations were conducted varying misclassification rates and covariate effects.
- Logistic regression (binary outcome) and Poisson regression (count outcome) were used for analysis.
- Models included unadjusted, adjusted for misclassified, and adjusted for corrected covariates.
Main Results:
- For binary outcomes, type I error was maintained with adjusted models, but power decreased with misclassification and covariate effect. Treatment estimates were biased towards the null.
- For count outcomes, misclassification inflated type I error and reduced power in unadjusted and misclassified models.
- The impact of misclassification varied based on the outcome's distribution.
Conclusions:
- Covariate misclassification under adaptive randomization significantly affects trial operating characteristics.
- Appropriate analysis adjustments are crucial, but the specific impact depends on the outcome distribution (binary vs. count).
- Misclassification can lead to biased treatment effect estimates and reduced statistical power.
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