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This paper presents a computer program for use in the design of long-term clinical trials with multiple treatment arms in which the primary outcome variables are censored survival times. The treatment arms may be structured as a one-way or multi-way factorial design. It is assumed that patients are entered and randomized to a treatment arm during an accrual period. The patients are then followed for a fixed period during which there may be dropouts. Various distributional assumptions can be used to model the survival times. These include an option in which there is an effect of treatment duly after a lag or delay time. The program then computes the power of various statistical tests of hypotheses concerning treatment differences, interactions and trends. The power computations are "exact" in that they use the Monte Carlo method to obtain Type I and II error probabilities. However the program also outputs the normal approximations for comparison, although they are typically not accurate in these situations. Fisher's LSD method is used to adjust for the multiple comparisons. By comparing the power for various sets of design parameters, such as sample size, numbers of factor levels, patient accrual rate, and length of follow-up, an appropriate design can be constructed. Two examples are provided. The first is a simple one-way layout with multiple treatment arms; the second a two-way factorial design for a proposed large scale cancer chemoprevention trial.

This paper presents a computer program for fitting mixed effects regression models to repeated events data. The method has been described by Abu-Libdeh, Turnbull and Clark (Biometrics 46 (1990) 1017-1034). Such data can occur in longitudinal studies where subjects experience repeated events over time. The program allows the stepwise construction of a series of regression models which can be used to examine and test the influence of the various measured covariates upon the event rates. Two examples are provided. The first is a simple example involving the incidence of mammary tumors in rats. The second involves a very large complex data set from a clinical trial for the prevention of recurrent skin tumors.