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.
1 2 3 4 5 6 7 8 | ##---- 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)
{
}
|
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