An aid to data monitoring in long-term clinical trials

In data monitoring of long-term clinical trials, it is routine to consider the question of whether a trial should be terminated before its scheduled end. Termination may seem reasonable either because it appears that the null hypothesis ( H 0) is likely to be disproved at the end of the trial or, even under optimistic assumptions, it is quite unlikely to be disproved. This idea has recently been formalized and characterized (with respect to Type 1 and Type 2 error) by Lan, Simon, and Halperin [Comm Stat in press]; their results provide a quantitative rationale for using as a monitoring tool, the conditional probability given current results and particular hypotheses that the treatment group will be superior (in the statistical sense) at the designed end of the trial. If the conditional probability under H 0 is sufficiently high, rejection of H 0 is indicated; if the conditional probability under H a is sufficiently low, acceptance of H 0 is indicated. Details of the implementation of this idea are considered for the case where the outcome variable is dichotomous and the statistic comparing the two groups at the trial's end is simply the standardized observed difference in proportions of events. The case of simultaneous entry and no competing risk is considered in detail with subsequent discussion of the alterations required if staggered entry and/or competing risk are relevant; simplifications which are possible in the case of staggered entry are described. Unsurprisingly, our results depend on assumptions made about further experience. Possible approaches to the extrapolation problem thus created are briefly described.