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In epidemiological studies of time-to-event data, a quantity of interest tothe clinician and the patient is the risk of an event given a covariateprofile. However, methods relying on time matching or risk-set sampling(including Cox regression) eliminate the baseline hazard from the likelihoodexpression or the estimating function. The baseline hazard then needs to beestimated separately using a non-parametric approach. This leads to step-wiseestimates of the cumulative incidence that are difficult to interpret. Usingcase-base sampling, Hanley & Miettinen (2009) explained how the parametrichazard functions can be estimated using logistic regression. Their approachnaturally leads to estimates of the cumulative incidence that aresmooth-in-time. In this paper, we present the casebase R package, acomprehensive and flexible toolkit for parametric survival analysis. Wedescribe how the case-base framework can also be used in more complex settings:competing risks, time-varying exposure, and variable selection. Our packagealso includes an extensive array of visualization tools to complement theanalysis of time-to-event data. We illustrate all these features through fourdifferent case studies. *SRB and MT contributed equally to this work.

casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates