LDTestimate {DTR} | R Documentation |
The function computes the survival estimates and estimated standard errors for dynamic treatment regimes (DTRs) at the observed event times as proposed in Lunceford, Davidian and Tsiatis (2002) Equation (3) and Equation (10).
LDTestimate(data, L = .Machine$double.xmax)
data |
a data frame (X, R, Z, U, delta) representing the data from a two-stage randomization design with therapies A1 and A2 available at the first stage, and B1 and B2 available at the second stage. |
L |
restricted survival time. Default is .Machine$double.xmax, which is the largest double value of R. Set L to a numeric number smaller than the maximum follow-up time if restricted follow-up time up to L is considered. |
In sequentially randomized designs, there could be more than two therapies available at each stage. For simplicity, and to maintain similarity to the most common sequentially randomized clinical trials, a two-stage randomization design allowing two treatment options at each stage is used in the current version of the package. In detail, patients are initially randomized to either A1 or A2 at the first stage. Based on their response status, they are then randomized to either B1 or B2 at the second stage. Therefore, there are a total of four DTRs: A1B1, A1B2, A2B1, and A2B2.
The function returns an object of class DTR
. See DTR.object
for details.
Lunceford JK, Davidian M, Tsiatis AA: Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials. Biometrics 58:48-57, 2002
simLDTdata
, DTR.object
, print.DTR
,
summary.DTR
, print.summary.DTR
, plot.DTR
## Not run:
data("LDTdata")
est <- LDTestimate(data=LDTdata)
est
## End(Not run)