Semiparametric transformation models, which include the Cox proportional hazards and proportional odds models as special cases, are popular in current practice of survival analysis owing to that, in contrast to parametric models, no assumption on the baseline distribution is required. Although sample size calculations for semiparametric survival analysis with right-censored data are available, no such calculation exits in literature for semiparametric analysis with current status data, where only an examination time and whether the event occurs prior to the examination are observable. We develop sample size calculation for semiparametric two-group comparison or regression analysis with current status data. The proposed formula can be readily implemented with given effect size, power level, covariate group proportions, covariate-specific examination (censoring) time distributions, and proportions of events observed in the control group at a few knot points in the study period. Simulation results show that the proposed sample size calculation is adequate in the sense that it leads to studies with empirical power very close to the planned power level. We illustrate practical applications of the proposal through examples from an animal tumorigenicity study and a cross-sectional survey on osteoporosis status in the elderly.
Keywords: Cox proportional hazards model; power; sample size calculation; study design; transformation models.