Smooth Survival Models, Including Generalized Survival Models
Class "stpm2" ~~~
Parametric accelerated failure time model with smooth time functions
Placemarker function for a baseline hazard function.
Generic method to update the coef in an object.
Test for a time-varying effect in the coxph model
S3 method for to provide exponentiated coefficents with confidence int...
gradient function (internal function)
Extract design information from an stpm2/gsm object and newdata for us...
Defaults for the gsm call
Parametric and penalised generalised survival models
Utility that returns a function to increment a variable in a data-fram...
S3 methods for lines
Predictions for continuous time, nonhomogeneous Markov multi-state mod...
Predictions for continuous time, nonhomogeneous Markov multi-state mod...
Generate a Basis Matrix for Natural Cubic Splines (with eXtensions)
Generate a Basis Matrix for the first derivative of Natural Cubic Spli...
Calculate numerical delta method for non-linear predictions.
plots for an stpm2 fit
Predicted values for an stpm2 or pstpm2 fit
Evaluate a Spline Basis
~~ Methods for Function predictnl ~~
Estimation of standard errors using the numerical delta method.
Class "pstpm2"
Residual values for an stpm2 or pstpm2 fit
Internal functions for the rstpm2 package.
Simulate values from an stpm2 or pstpm2 fit
Utility to use a smooth function in markov_msm based on piece-wise con...
Class "stpm2" ~~~
Class "tvcCoxph"
Methods for Function update
Vectorised One Dimensional Optimization
Vectorised One Dimensional Root (Zero) Finding
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.