ptc: Prepares and fits a PTC Cox model
In trooper197/tccox: Treatment Choice Cox Model

Description Usage Arguments Value Author(s) References Examples

Calls treatinit() to prepare dataset

Calls addtc() to create TC intervals

Calls ptcfitter() to fit PTC model

1	ptc(dataset, ncov, cov_names, maxfollow, nmaxint, interval_width, min_future_events)

`dataset`	data.frame organized as expected by tc()
`ncov`	number of baseline covariates (including treatment) to be included in model
`cov_names`	vector of baseline covariate names (including treatment)
`maxfollow`	maximum followup for any subject in dataset
`nmaxint`	maximum number of TC intervals allowed
`interval_width`	width of the TC intervals
`min_future_events`	minimum number of events expected of future starters(stoppers) of treatment for determining upper bound on starting(stopping) TC intervals

`fit_ptc`	fit of PTC model
`nstartint`	number of TC starting intervals
`startint`	vector containing the TC starting interval endpoints
`nstopint`	number of TC stopping intervals
`stopint`	vector containing the TC stopping interval endpoints
`cov_names1`	vector containing the covariate names of the model
`nperson`	number of subjects in dataset
`numevents`	number of events in dataset
`medianfollowup`	median followup for subjects in dataset

James F. Troendle

Troendle, JF, Leifer, E, Zhang Z, Yang, S, and Tewes H (2017) How to Control for Unmeasured Confounding in an Observational Time-To-Event Study With Exposure Incidence Information: the Treatment Choice Cox Model. Statistics in Medicine 36: 3654-3669.

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x)
{
  }