The SFTM assumes that the potential failure time U had the individual
never received treatment and the observed failure time T follow
$$U \sim \int_0^T e^{\psi A_u}d u, $$
where ~ means "has the same distribution as", and \(A_u\) is the
treatment indicator at time \(u\).
We assume that the individual continuously received treatment until
time \(V\). The observed failure time can be censored assuming the
censoring time is independent of the failure time given the treatment and
covariate history (the so-called ignorable censoring). The current
function allows for multi-dimensional baseline covariates and/or
multi-dimensional time-dependent covariate.
Variance estimates should be implemented by delete-one-group jackknifing
and recalling ctSFTM.
If only time-independent covariates are included, the data.frame must
contain the following columns:
- id:
A unique participant identifier.
- U:
The time to the clinical event or censoring.
- deltaU:
The clinical event indicator (1 if U is the event time;
0 otherwise.
- V:
The time to optional treatment discontinuation, a clinical
event, censoring, or a treatment-terminating event.
- deltaV:
The indicator of optional treatment discontinuation
(1 if treatment discontinuation was optional; 0 if
treatment discontinuation was due to a clinical event,
censoring or a treatment-terminating event.
If time-dependent covariates are to be included, the data.frame must be
a time-dependent dataset as described by package survival. Specifically,
the time-dependent data must be specified for an interval (lower,upper]
and the data must include the following additional columns:
- start:
The lower boundary of the time interval to which the
data pertain.
- stop:
The upper boundary of the time interval to which the
data pertain.