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