Steven N. Goodman, MD, MHS, PhD
Goodman SN. Stopping at Nothing? Some Dilemmas of Data Monitoring in Clinical Trials. Ann Intern Med. 2007;146:882-887. doi: 10.7326/0003-4819-146-12-200706190-00010
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Published: Ann Intern Med. 2007;146(12):882-887.
This commentary reviews the argument that clinical trials with data monitoring committees that use statistical stopping guidelines should generally not be stopped early for large observed efficacy differences because efficacy estimates may be exaggerated and there is minimal information on treatment harms. Overall, the average of estimates from trials that use these boundaries differs minimally from the true value. Estimates from a given trial that seem implausibly high can be moderated by using Bayesian methods. Data monitoring committees are not ethically required to precisely estimate a large efficacy difference if that difference differs convincingly from zero, and the requirement to detect harms and balance efficacy against harm depends on whether the nature of the harm is known or unknown before the trial.
The trials were designed to have 90% power to detect a 10% mortality benefit (for example, 50% vs. 40%). Each panel corresponds to a different underlying true difference: no difference (top), 10% difference (middle), and 20% difference (bottom). The distribution of results is shown for trials of 2 designs: 1 using a 4-look O'Brien–Fleming stopping rule (“stopping”) and 1 using a fixed sample size (“no stopping”). Median effect size and 2.5% and 97.5% percentiles of each estimate are reported in parentheses. The mean sample size is reported for the “stopping” trial only: n = 1040 for the fixed sample size design.
If reliable estimates are required for each treatment then it seems inevitable that a substantial number of patients must receive the inferior treatment … Then it must be recognized that the risks undertaken by volunteers in the experiment are mainly associated with estimation, rather than the need to discover which of the treatments is superior.
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