Studies on event occurrence aim to investigate if and when subjects experience a particular event. The timing of events may be measured continuously using thin precise units or discretely using time periods. The latter metric of time is often used in social science research and the generalized linear model (GLM) is an appropriate model for data analysis. While the design of trials with continuous-time survival endpoints has been extensively studied, hardly any guidelines are available for trials with discrete-time survival endpoints. This article studies the relationship between sample size and power to detect a treatment effect in a trial with two treatment conditions. The authors use the exponential and the Weibull survival functions to represent constant and varying hazard rates. Furthermore, logit and complementary log-log link functions are used. For constant hazard rates, the power depends on the event proportions at the end of the trial in both treatment arms and on the number of time periods. For varying hazard rates, the power also depends on the shape of the survival functions and different power levels are observed in each time period in case the logit link function is used. For any survival function, power decreases if attrition is present. The authors provide R code to perform the power calculations. (Contains 6 tables and 7 figures.)