Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
This is a proposal to further simplify test.linear.assumptions(). It is an attempt to streamline the code in order to make it easier to understand and maintain. In addition the code for Gompertz models has been replaced by a placeholder: this is a stopgap measure for issue #9 and also fixes one of the warnings during build.
Toy example:
mle <- fit.models(formula=Surv(recyrs,censrec) ~ group,
data=bc,
distr=c("exp", "weibull", "llogis", "lognormal", "gompertz"),
method="mle")
par(mfrow = c(2, 3))
for (i in 1:5) test.linear.assumptions(mle, mod = i, label_plot = TRUE)
par(mfrow = c(1, 1))
This is a proposal to further simplify test.linear.assumptions(). It is an attempt to streamline the code in order to make it easier to understand and maintain. In addition the code for Gompertz models has been replaced by a placeholder: this is a stopgap measure for issue #9 and also fixes one of the warnings during build.
Toy example: