Software

  • Censored Rank Independence Screening

The R codes were developed for censored rank independence screening for high-dimensional right censored data. The R codes include all functions called can be found here. The original paper is appeared in Biometrika (2014).

  • Varying-coefficient Independence Screening for High-dimensional data

The R codes were developed for vary-coefficient independence screening for high-dimensional longitudinal data. The R codes include all functions called can be found here. The original paper is appeared in Statistica SInica (2015).

  • Penalized Q-learning for dynamic treatment regimes

The R codes were developed for statistical inference for penalized Q-learning in dynamic treatment regimes. The R codes include all functions called and a simulation setting can be found here. The original paper is appeared at Statistica Sinica (2014).

  • Subgroup detection and sample size calculation

The R codes were developed for testing the existence of a subgroup with enhanced treatment effect, and associated sample size calculation procedure for the subgroup detection test. An R package, named “subdetect” has been uploaded to CRAN. The codes were developed by Ailin Fan, Shannon Holloway, Wenbin Lu and Rui Song. The original paper is accepted for publication in Journal of American Statistical Association (2016).

  • Doubly robust estimation of optimal treatment regimes for maximizing t-year survival probabilities

The R codes were developed for implementing the doubly robust estimation methods proposed in the paper “On estimation of optimal for maximizing t-year survival probabilities” by Runchao Jiang, Wenbin Lu, Rui Song and Yong Marie Davidian (JRSSB, 2017, in press). The R codes (including a readme.txt file for a detailed description) and the AIDS data (ACTG175) used in the paper can be downloaded from here.

  • Concordance assisted learning (CAL) for estimating optimal individualized treatment regimes

The R codes were developed for implementing the CAL methods proposed in the paper “Concordance assisted learning for estimating optimal individualized treatment regimes” by Caiyun Fan, Wenbin Lu, Rui Song and Yong Zhou (JRSSB, 2017, in press). The R codes (including a readme.txt file for a detailed description) and the AIDS data (ACTG175) used in the paper can be downloaded from here.

  • Variable  selection for optimal dynamic treatment regime

The R codes were developed for selecting important predictors in optimal dynamic treatment decision (i.e. those with qualitative interactions with treatments) based on two methods: the sequential advantage selection (SAS, Fan, Lu and Song, 2016, Annals of Applied Statistics) and high-dimensional A-learning (Shi, Fan, Song and Lu, 2018, Annals of Statistics, in press). An R package, named “ITRSelect” has been uploaded to CRAN.

  • Inference for quantile-adaptive dynamic treatment regime

The R codes were developed for estimation and inference for quantile-adaptive dynamic treatment regime An R package, named “quantoptr” has been uploaded to CRAN.