Adjustment of selection bias for clinical trials: a simulation study

Abstract

Clinical trial selection bias is a common issue, as patients are typically not selected randomly from a target population. Various statistical approaches have been proposed to adjust for this bias, including IPW (inverse probability weights), SPS (subclassification with propensity scores), and EVB (external validity bias). However, there has been very little statistical research to compare the performance of these methods in clinical trials. To bridge this gap, we conducted a simulation study using a patient population with seven covariates and a true treatment effect size of 0.5 (Cohen’s d). Next, we assessed the efficacy of the three statistical methods on nonrandom clinical trial samples with varying sizes and covariates. Based on our simulation results, EVB is the most effective method for adjusting clinical trial selection bias when there are seven covariates. SPS is the most effective method for adjusting clinical trial selection bias when there are three and five covariates. However, we observed that IPW's performance was inadequate, indicating that it may not be a suitable option for selection bias adjustment in clinical trials. In summary, our study sheds light on the effectiveness of various statistical methods in mitigating selection bias in clinical trials.