Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
|Author||Sahir Bhatnagar [aut, cre] (http://sahirbhatnagar.com/), Maxime Turgeon [aut] (<https://orcid.org/0000-0003-4863-6035>), Jesse Islam [aut] (https://www.jesseislam.com/), Olli Saarela [aut] (http://individual.utoronto.ca/osaarela/), James Hanley [aut] (http://www.medicine.mcgill.ca/epidemiology/hanley/)|
|Maintainer||Sahir Bhatnagar <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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