Blinded continuous monitoring of nuisance parameters in clinical trials

摘要

Determination of a clinical trial's size is an important task in the planning of any trial because of the direct implications of the sample size on feasibility, costs and timelines. However, sample size calculations are often subject to substantial uncertainty due to limited prior information on the size of nuisance parameters such as variances or event rates. Continuous monitoring of the nuisance parameter in clinical trials has been proposed as a tool to size trials appropriately. With this approach, the nuisance parameter is continuously monitored during the trial. The trial is stopped when the actual estimate for the nuisance parameter and sample size fulfil a stopping criterion. Continuous monitoring can therefore be viewed as a stochastic process with stopping time. We describe the bias that occurs with unblinded continuous monitoring of the variance in clinical trials by means of a simulation study. Then we propose a procedure for blinded continuous monitoring that does not require breaking the treatment code during the on-going study and show that the procedure does not suffer from the same biases as observed in unblinded monitoring. Results on the performance properties of such designs are given and the designs are compared with blinded re-estimation procedures with a single data look. By means of asymptotic theoretical arguments and finite sample size simulations we find that the variability in sample size is smaller with blinded continuous monitoring than with blinded sample size re-estimation whenever the power for both designs is close to the target value. Repeated sample size re-estimation is in between continuous monitoring and sample size re-estimation in this respect. Furthermore, we present a hypertension trial where blinded sample size re-estimation with a single data look was applied and we investigate the properties of blinded continuous monitoring in this setting. Finally we close with a brief discussion.