Using permutation tests and bootstrap confidence limits to analyze repeated events data from clinical trials.
In clinical trials comparing treatments for superficial bladder cancer, patients are at risk of repeated recurrences of their disease. Statistical methods of analyzing such data are required. This article presents a nonparametric approach. A statistical test to compare the recurrence or tumor rates in two treatment groups, using the randomization distribution, is described. Confidence intervals for the rate ratio are determined from the bootstrap distribution. The implementation of both requires Monte Carlo methods. Computer simulations support the use of these nonparametric methods when there are more than 60 recurrences in each treatment group. An example illustrating their use is given. The strategy adopted for analysis of these data could be applied to other clinical trials where standard methodology is inappropriate.
Freedman
L
L
Biometry Branch, National Cancer Institute, Bethesda, Maryland 20892.
Sylvester
R
R
Byar
D P
DP
D001699:Biometry; D002986:Clinical Trials as Topic / Q000379:methods*; D005500:Follow-Up Studies; D006801:Humans; D008432:Mathematical Computing; D009010:Monte Carlo Method; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis / Q000453:epidemiology*; D009378:Neoplasms, Multiple Primary / Q000175:diagnosis / Q000453:epidemiology; D011897:Random Allocation; D012494:Sampling Studies; D001749:Urinary Bladder Neoplasms / Q000601:surgery