addtc: Determines TC intervals and breaks up dataset into TC...
In trooper197/tccox: Treatment Choice Cox Model

Description Usage Arguments Value Author(s) References Examples

First the starting and stopping TC intervals are determined according to the expected event criteria; ITC intervals are truncated if necessary or dropped if meaningless. Next the dataset is broken up according to the TC intervals.Finally, the dataset is compressed to only the meaningful intervals.

1
2
3

addtc(dataset, ncov, maxfollow, start_times, stop_times, min_future_events, numevents,
 nperson, nmaxint, maxobs, interval_width, nitc_start, itc_start_endpoint, nitc_stop,
 itc_stop_endpoint, tti, tts, followup)

`dataset`	data.frame organized as expected by tc()
`ncov`	number of baseline covariates (including treatment) to be included in model
`maxfollow`	maximum followup for any subject in dataset
`start_times`	vector of ordered times when starting of treatment occurs in dataset
`stop_times`	vector of ordered times when stopping of treatment occurs in dataset
`min_future_events`	minimum number of events expected of future starters(stoppers) of treatment for determining upper bound on starting(stopping) TC intervals
`numevents`	number of events in dataset
`nperson`	number of subjects in dataset
`nmaxint`	maximum number of TC intervals allowed
`maxobs`	maximum number of observations (intervals of time) allowed for dataset
`interval_width`	width of the TC intervals
`nitc_start`	number of ITC starting intervals
`itc_start_endpoint`	vector containing the endpoints of the ITC starting intervals
`nitc_stop`	number of ITC stopping intervals
`itc_stop_endpoint`	Vector containing the endpoints of the ITC starting intervals
`tti`	vector of same length as dataset containing times when starting occurs or 0 if subject does not start
`tts`	vector of same length as dataset containing times when stopping occurs or 0 if subject does not stop
`followup`	vector of same length as dataset containing followup times

`dataset`	data.frame with dataset broken up according to TC intervals
`tstartp`	matrix whose columns are the ITC starting covariate values
`tstopp`	matrix whose columns are the ITC stopping covariate values
`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
`nitc_start`	number of ITC starting intervals
`itc_start_endpoint`	vector containing the ITC starting interval endpoints
`nitc_stop`	number of ITC stopping intervals
`itc_stop_endpoint`	vector containing the ITC stopping interval endpoints

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)
{
  }