DTR (version 1.7)

CHRestimate: Function for calculating cumulative hazard ratio (CHR) estimates

Description

This function computes the estimates for the cumulative hazard ratios (CHRs) between two different dynamic treatment regimes (DTRs) and their variance estimates at observed event times as proposed in Tang and Wahed (2013) [Epub ahead of print].

Usage

CHRestimate(data, covar=names(data)[!names(data) 
%in% c("X", "R", "Z", "U", "delta")])

Arguments

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. X: first-stage indicator, X=0 if assigned to A1, and X=1

covar

covariate(s) to be adjusted. The default uses all the variables in the data other than X, R, Z, U and delta

Value

• The function returns an object of class CHR. See CHR.object for details.

Details

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 simple 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. Based on four DTRs, six different cumulative hazard ratios (CHRs) are computed: CHR(A1B2 vs. A1B1), CHR(A2B1 vs. A1B1), CHR(A2B2 vs. A1B1), CHR(A2B1 vs. A1B2), CHR(A2B2 vs. A2B1), and CHR(A2B2 vs. A2B1). The natural logarithms of the CHRs are also computed.

References

Tang X, Wahed AS: Cumulative hazard ratio estimation for treatment regimes in sequentially randomized clinical trials. Statistics in Biosciences, 2013 [Epub ahead of print]

See Also

simCHRdata, CHR.object, print.CHR, summary.CHR, print.summary.CHR, plot.CHR

Examples

data("CHRdata")
est <- CHRestimate(data=CHRdata, covar="V1")
est