Description Usage Arguments Value Author(s) References Examples
Calls treatinit() to prepare dataset
Calls itcadd() to create ITC intervals
Calls addtc() to create TC intervals
Calls itcfitter() to fit ITC model
1 2 3 | itc(dataset, ncov, cov_names, maxfollow, nmaxint, interval_width, min_exp_events,
min_future_events, nitc_fixed, n_start_fixed, n_stop_fixed,
interval_stop_beginning)
|
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_exp_events |
minimum number of events expected of subjects in each cell for determining ITC intervals |
min_future_events |
minimum number of events expected of future starters(stoppers) of treatment for determining upper bound on starting(stopping) TC intervals |
nitc_fixed |
indicator (0 or 1) that potential ITC intervals are fixed |
n_start_fixed |
number of fixed ITC starting intervals (only applicable if nitc_fixed=1) |
n_stop_fixed |
number of fixed ITC stopping intervals (only applicable if nitc_fixed=1) |
interval_stop_beginning |
smallest ITC stopping interval endpoint (only applicable if nitc_fixed=1) |
fit_itc |
fit of ITC model |
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 |
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_names3 |
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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