c(
person("Sahir", "Bhatnagar", email = "sahir.bhatnagar@gmail.com",
role = c("aut", "cre"), comment = "http://sahirbhatnagar.com/"),
person("Maxime", "Turgeon", email = "max.turgeon@umanitoba.ca",
role = c("aut"), comment = c(ORCID = "0000-0003-4863-6035")),
person("Jesse", "Islam", email = "jesse.islam@mail.mcgill.ca",
role = c("aut"), comment = "https://www.jesseislam.com/"),
person("Olli", "Saarela", email = "olli.saarela@utoronto.ca",
role = c("aut"), comment = "http://individual.utoronto.ca/osaarela/"),
person("James", "Hanley", email = "james.hanley@mcgill.ca",
role = c("aut"), comment = "http://www.medicine.mcgill.ca/epidemiology/hanley/")
)
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>.
data.table, ggplot2, methods, mgcv, stats, survival, VGAM
colorspace, eha, glmnet, knitr, progress, rmarkdown, splines,
testthat (>= 3.0.0), visreg
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/)