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

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 exp...

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Bibliographic Details
Published in:arXiv.org 2020-09
Main Authors: Sahir Rai Bhatnagar, Turgeon, Maxime, Islam, Jesse, Hanley, James A, Saarela, Olli
Format: Article
Language:English
Subjects:
Regression analysis
Sampling
Survival
Survival analysis
Toolkits
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Summary: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.
ISSN:2331-8422