DTR-package: Estimation and comparison of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials

Description

This is a package for the estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials. In a sequentially randomized design, patients are initially randomized to one of the first-stage therapies. Based on their responses to the first-stage therapy, they are then randomized to one of the second-stage therapies. The second-stage therapy could be a rescue therapy if the response is not favorable, or maintenance therapy if favorable response is achieved. There are treatment sequences resulted from such designs: first-stage therapy -> response -> second-stage therapy. The treatment sequences are also referred to as dynamic treatment regimes (DTRs) or adaptive treatment strategies in the literature. The estimation functions include LDT.estimator, LDT.mean.estimator, WRSE.estimator, and CHR.estimator. The comparisons functions include DTR.Wald.test, DTR.Cox.test, CHR.Wald.test, and DTR.Logrank.test. The plotting functions include DTR.surv.plot and DTR.CHR.plot. The functions for data simulation include sim.LDT.data, sim.WRSE.data, sim.Cox.data, sim.CHR.data, and sim.Logrank.data.

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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. The function sim.LDT.data generates data sets from sequentially randomized clinical trials as described in the simulation study of Lunceford, Davidian and Tsiatis (2002). The function LDT.estimator computes the estimates of the survival function and their estimated standard errors for DTRs at given time points as proposed in Lunceford, Davidian and Tsiatis (2002) Equation (3) and Equation (10). The function LDT.mean.estimator computes the mean restricted survival estimates and their standard errors for DTRs as proposed in Lunceford, Davidian and Tsiatis (2002). The function sim.WRSE.data generates data sets from sequentially randomized clinical trials as described in the simulation study of Guo and Tsiatis (2005). The function WRSE.estimator computes the weighted risk set estimator (WRSE) of the survival function and their estimated standard errors for DTRs at given time points as proposed in Guo and Tsiatis (2002) Equation (3) and Equation (16). The function DTR.Wald.test compares the survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials based on the LDT estimator proposed in Lunceford, Davidian and Tsiatis (2002) or the WRSE estimator proposed in Guo and Tsiatis (2005) using the Wald-type tests. The function DTR.surv.plot plots the survival functions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials based on the LDT estimator proposed in Lunceford, Davidian and Tsiatis (2002) or the WRSE estimator proposed in Guo and Tsiatis (2005). The function sim.Cox.data generates a data set from sequentially randomized clinical trials as described in the simulation study of Tang and Wahed (2011). The function DTR.Cox.test compares the survival distributions (i.e. hazard functions) of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials after adjustment for covariates as proposed in Tang and Wahed (2011). The function sim.CHR.data generates a data set from sequentially randomized clinical trials as described in the simulation study of Tang and Wahed (2013) [Epub ahead of print]. The function CHR.estimator computes the estimates for the cumulative hazard ratios (CHRs) between two different dynamic treatment regimes (DTRs) and their variance estimates at given time points as proposed in Tang and Wahed (2013) [Epub ahead of print]. The function CHR.Wald.test compares the cumulative hazard functions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials by calculating the natural logarithms of cumulative hazard ratios (CHRs) and performing the Wald-type tests based on natural logarithms of CHRs as proposed in Tang and Wahed (2013) [Epub ahead of print]. The function DTR.CHR.plot plots the estimated cumulative hazard ratios (CHR) or the natural logarithms of the estimated CHRs between two different dynamic treatment regimes (DTRs) from sequentially randomized clinical trials using the CHR estimates proposed in Tang and Wahed (2013) [Epub ahead of print]. The function sim.Logrank.data generates a data set from sequentially randomized clinical trials as described in the simulation study of Kidwell and Wahed (2013). The function DTR.Logrank.test compares the survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials using the weighted logrank tests proposed in an unpublished 2005 PhD thesis from North Carolina State University by X. Guo, Feng and Wahed (2008), and Kidwell and Wahed (2013). ll{ Package: DTR Type: Package Version: 1.2 Date: 2013-08-01 License: GPL (>=2) }

References

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 Guo X: Statistical analysis in two-stage randomization designs in clinical trials. PhD thesis, Department of Statistics, North Carolina State University, 2005 Guo X, Tsiatis AA: A weighted risk set estimator for survival distributions in two-stage randomization designs with censored survival data. Int. J. Biostatistics 1:1-15, 2005 Feng W, Wahed AS: Supremum weighted log-rank test and sample size for comparing two-stage adaptive treatment strategies. Biometrika 95:695-707, 2008 Tang X, Wahed AS: Comparison of treatment regimes with adjustment for auxiliary variables. Journal of Applied Statistics 38(12):2925-2938, 2011 Kidwell KM, Wahed AS: Weighted log-rank statistic to compare shared-path adaptive treatment strategies. Biostatistics, 14(2):299-312, 2013 Tang X, Wahed AS: Cumulative hazard ratio estimation for treatment regimes in sequentially randomized clinical trials. Statistics in Biosciences, 2013 [Epub ahead of print]

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