Function reference • surtvep

Main Fitting Functions The main function to fit the Cox-non proportional hazards model
`coxtv()`	fit a Cox non-proportional hazards model
`coxtp()`	fit a Cox non-proportional hazards model with P-spline or Smoothing-spline, with penalization tuning parameter chosen by information criteria or cross-validation
`cv.coxtp()`	fit a cross-validated Cox non-proportional hazards model with P-spline or Smoothing-spline where penalization tuning parameter is provided by cross-validation
Information Criteria Calculating the Information Criteria to select the tunning paratmeter for the penalized methods
`IC()`	calculating information criteria from a `coxtp` object
Tests of Cox-non proportional hazards model
`tvef.ph()`	testing the proportional hazards assumption from a `coxtv` or `coxtp` object
`tvef.zero()`	testing the significance of the covariates from a `coxtv` or `coxtp` object
`tvef.zero.time()`	testing the significance of the covariates from a `coxtv` or `coxtp` object using a Wald test statistic
Helper functions
`get.tvcoef()`	helper function to get time-varying coefficients
`confint(<coxtp>)` `confint(<coxtv>)`	get confidence intervals of time-varying coefficients from a fitted object
`plot(<coxtp>)`	plotting results from a fitted `coxtp` object
`plot(<coxtv>)`	plotting results from a fitted `coxtv` object
`baseline()`	calculating baseline hazard and baseline cumulative hazard using the result from a `coxtv` or `coxtp` object
`plot(<baseline>)`	plotting the baseline hazard
Package data sets
`ExampleData`	example data with 2000 observations of 2 continuous variables
`ExampleDataBinary`	example data with 2000 observations of 2 binary variables
`StrataExample`	example data for stratified model illustration
`support`	Study to Understand Prognoses Preferences Outcomes and Risks of Treatment

Reference

Main Fitting Functions

Information Criteria

Tests of Cox-non proportional hazards model

Helper functions

Package data sets