Statistical methods for monitoring clinical trials.

Clinical trials are monitored to determine whether a treatment is safe and effective. If it becomes clear that treatment is superior to control, ethical considerations compel us to stop the study and make the treatment available to control patients. On the other hand, if it becomes clear that the treatment will not be shown superior to control, we would like to stop the study and save valuable resources for more promising agents. But how much evidence is enough to declare benefit, and what criteria do we use to stop for lack of benefit? This article reviews monitoring procedures designed to answer these two questions. The B-value approach of Lan and Wittes (1) and Lan and Zucker (2) is used to unify the monitoring of many different kinds of trials, including those with continuous, dichotomous, or survival outcomes.

[1]  J. Haybittle,et al.  Repeated assessment of results in clinical trials of cancer treatment. , 1971, The British journal of radiology.

[2]  D. Zucker,et al.  Sequential monitoring of clinical trials: the role of information and Brownian motion. , 1993, Statistics in medicine.

[3]  S. Pocock Group sequential methods in the design and analysis of clinical trials , 1977 .

[4]  D. Schoenfeld The asymptotic properties of nonparametric tests for comparing survival distributions , 1981 .

[5]  M Tan,et al.  Clinical trial designs based on sequential conditional probability ratio tests and reverse stochastic curtailing. , 1998, Biometrics.

[6]  T R Fleming,et al.  Designs for group sequential tests. , 1984, Controlled clinical trials.

[7]  K. K. Lan,et al.  Discrete sequential boundaries for clinical trials , 1983 .

[8]  P. O'Brien,et al.  A multiple testing procedure for clinical trials. , 1979, Biometrics.

[9]  K. K. Lan,et al.  Stochastically curtailed tests in long–term clinical trials , 1982 .

[10]  P. Armitage,et al.  Repeated Significance Tests on Accumulating Data , 1969 .

[11]  Michael A. Proschan,et al.  Effects of assumption violations on type I error rate in group sequential monitoring , 1992 .

[12]  Barry R. Davis,et al.  Upper bounds for type I and type II error rates in conditional power calculations , 1990 .

[13]  D. Spiegelhalter,et al.  Monitoring clinical trials: conditional or predictive power? , 1986, Controlled clinical trials.

[14]  K K Lan,et al.  The B-value: a tool for monitoring data. , 1988, Biometrics.