Power analysis and sample-size determination are important components of a study design. In survival analysis, the power is directly related to the number of events observed in the study. The required sample size is therefore determined by the observed number of events. Survival data are commonly analyzed using the log-rank test or the Cox proportional hazards model. Stata 10’s new stpower command provides sample-size and power calculations for survival studies that use the log-rank test, the Cox proportional hazards model, and the parametric test comparing exponential hazard rates. It reports the number of events that must be observed in the study and accommodates unequal subject allocation between groups, nonuniform subject entry, and exponential losses to follow-up. This talk will demonstrate power, sample-size, and effect-size computations for different methods used to analyze survival data and for designs with recruitment periods and random censoring (administrative and loss to follow-up). It will also discuss building customized tables and producing graphs of power curves.
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