The impact of sample size re-estimation on the type I error rate in the analysis of a continuous end-point

Abstract

Sample size estimation is generally based on assumptions made during the planning stage of a clinical trial. Often, there is limited information available to estimate the initial sample size. This may result in a poor estimate. For instance, an insufficient sample size may not have the capability to produce statistically significant results, while an over-sized study will lead to a waste of resources or even ethical issues in that too many patients are exposed to potentially ineffective treatments. Therefore, an interim analysis in the middle of a trial may be worthwhile to assure that the significance level is at the nominal level and/or the power is adequate to detect a meaningful treatment difference. In this report, the impact of sample size re-estimation on the type I error rate for the continuous end-point in a clinical trial with two treatments is evaluated through a simulation study. Two sample size estimation methods are taken into consideration: blinded and partially unblinded. For the blinded method, all collected data for two groups are used to estimate the variance, while only data from the control group are used to re-estimate the sample size for the partially unblinded method. The simulation study is designed with different combinations of assumed variance, assumed difference in treatment means, and re-estimation methods. The end-point is assumed to follow normal distribution and the variance for both groups are assumed to be identical. In addition, equal sample size is required for each group. According to the simulation results, the type I error rates are preserved for all settings.