Abstract
Background: In a randomized controlled trial (RCT) with binary outcome the estimate of the marginal treatment effect can be biased by prognostic baseline covariates adjustment. Methods that target the marginal odds ratio, allowing for improved precision and power, have been developed. Methods: The performance of different estimators for the treatment effect in the frequentist (targeted maximum likelihood estimator, inverse-probability-of-treatment weighting, parametric G-computation, and the semiparametric locally efficient estimator) and Bayesian (model averaging), adjustment for confounding, and generalized Bayesian causal effect estimation frameworks are assessed and compared in a simulation study under different scenarios. The use of these estimators is illustrated on an RCT in type II diabetes. Results: Model mis-specification does not increase the bias. The approaches that are not doubly robust have increased standard error (SE) under the scenario of mis-specification of the treatment model. The Bayesian estimators showed a higher type II error than frequentist estimators if noisy covariates are included in the treatment model. Conclusions: Adjusting for prognostic baseline covariates in the analysis of RCTs can have more power than intention-to-treat based tests. However, for some classes of model, when the regression model is mis-specified, inflated type I error and potential bias on treatment effect estimate may arise.
Highlights
Baseline covariates impact the outcome in many randomized controlled trials, and a recent systematic review reported that 84% of the trials present adjusted analysis
It has been shown that models that adjust for baseline covariates can substantially improve the statistical power of the analysis when the covariates are moderately to strongly prognostic
While this is justified for continuous outcomes, for binary outcomes, which require non-linear models, covariate adjustment may change the magnitude of the treatment effect, and the situation is subtler [2]
Summary
Baseline covariates impact the outcome in many randomized controlled trials, and a recent systematic review reported that 84% of the trials present adjusted analysis. It has been shown that models that adjust for baseline covariates can substantially improve the statistical power of the analysis when the covariates are moderately to strongly prognostic. In a randomized controlled trial (RCT) with binary outcome the estimate of the marginal treatment effect can be biased by prognostic baseline covariates adjustment. G-computation, and the semiparametric locally efficient estimator) and Bayesian (model averaging), adjustment for confounding, and generalized Bayesian causal effect estimation frameworks are assessed and compared in a simulation study under different scenarios. The use of these estimators is illustrated on an RCT in type II diabetes. For some classes of model, when the regression model is mis-specified, inflated type I error and potential bias on treatment effect estimate may arise
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