Flexible sample size considerations using information-based interim monitoring

At the design phase of a clinical trial the total number: of participants needed to detect a clinically important treatment difference with sufficient precision frequently depends on nuisance parameters such as variance, baseline response rate, or regression cofficients other than the main effect. In practical applications, nuisance parameter values are often unreliable guesses founded on little or no available past history. Sample size calculations based on these initial guesses may, there-fore, lead to under- or over-powered studies. In this paper we argue that the precision with which a treatment effect is estimated is directly related to the statistical information in the data. In general, statistical information is a complicated function of sample size and nuisance parameters. However the amount of information necessary to answer the scientific question concerning treatment difference is easily calculated a priori and applies to almost any statistical model for a large variety of endpoints. It is thus possible to, be flexible on sample size but rather continue collecting data until we have achieved the desired information. Such a strategy is well suited to being adopted in conjunction with a group sequential clinical trial where the data are monitored routinely anyway. We present several scenarios and examples of how group sequential information-based design and monitoring can be carried out and demonstrate through simulations that this type of strategy will indeed give us the desired operating characteristics.