• Weighted estimating equations for linear transformation models with case-cohort sampling

The computation codes, written in the C language, solve the inverse selected probability weighted (ISPW) martingale-based estimating equations for a general class of linear transformation models with case-cohort sampling, which generalizes Prentice (1986) and Self and Prentice (1988)'s maximum pseudo-likelihood estimation method for the proportional hazards model. The codes were developed by Wenbin Lu. The original paper is published in Biometrika (2006, 93, 207-214). The codes are for the family of linear transformation models considered by Dabrowska and Doksum (1988). In this family, gama = 0 gives the partial linear proportional hazards model while gama = 1 gives the proportional odds model. Download the C code here for a simulation study with gama = 1 (PO model) and covariate-dependent censoring.

• Adaptive-Lasso for Cox's proportional hazards model (based on penalized partial likelihood)

The computation codes, written in the R language, conduct variable selection and model fitting simultaneously in the proportional hazards model (Cox 1972). The adaptive-lasso estimate is obtained using the penalized partial likelihood method with weighted L-1 penalty. The algorithm iteratively solves least square problems with weighted L-1 penalty via a modified shooting algorithm of Fu (1998). The codes were developed by Wenbin Lu and Hao Helen Zhang. The original paper is published in Biometrika (2007, 94, 1-13). Download the R code here for a simulation study (without tuning). PBC liver data example: R code (with tuning using GCV).

• Adaptive-Lasso for proportional odds model (based on penalized marginal likelihood)

The computation codes, written in the R language, conduct variable selection and model fitting simultaneously in the proportional odds model (Bennett 1983). The adaptive-lasso estimate is obtained using the penalized marginal likelihood method with weighted L-1 penalty. The algorithm iteratively solves least square problems with weighted L-1 penalty via a modified shooting algorithm of Fu (1998). The codes were developed by Wenbin Lu and Hao Helen Zhang. The original paper is published in Statistics in Medicine (2007, 26, 3771-3781). Download the R code here for a simulation study (without tuning).

• The marginal likelihood based boosting algorithm for nonlinear transformation models

The computation codes, written in the R language, fit the nonlinear transformation models using a smoothing spline based boosting learning algorithm (e.g. Buhlmann and Yu, 2003; Li and Luan, 2005). We first compute the log marginal likelihood function and its gradient using importance sampling. Then we use the negative gradients as responses and fit the model using component-wise cubic smoothing spline as the base learner. The codes were developed by Wenbin Lu and Lexin Li. The original paper is published in Biostatistics (2008, 9, 658-667). Download the R code here.

• Estimating equations based method for longitudinal data with informative observation times

In this work, we propose joint modeling and analysis of longitudinal data with possibly informative observation times via latent variables. A two-step estimation procedure is developed for parameter estimation. The computation codes, written in the C language, solve the set of proposed asymptotically unbiased estimating. Both gamma and log-normal frailties have been implemented. The codes were developed by Yu Liang and Wenbin Lu. The original paper is published in Biometrics (2009, 65, 377-384). Download the C code here for a simulation study with gamma frailty.

• Efficient kernel estimation for accelerated failure time cure model

The computation codes, written in the R language, fit the accelerated failure time cure model via kernel-based nonparametric maximum likelihood estimation. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error density, in which a kernel-smoothed conditional profile likelihood is maximized in the M-step. The variance of the resulting estimators are obtained using an EM-aided numerical differentiation method. The codes were developed by Wenbin Lu. The original paper is published in Statistica Sinica (2010, 20, 661-674). Download the R code here for analysis of a breast cancer data (Farewell, 1986).

• Global and local estimating equations for partially linear transformation model

The computation codes, written in the C language, solve the martingale-based global and local estimating equations for a general class of partially linear transformation models. A resampling method is used to estimate the variance of the proposed estimators. The codes were developed by Wenbin Lu and Hao Helen Zhang. The original paper is published in Journal of American Statistical Association (2010, 105, 683-691). Download the C code here for analysis of a lung cancer data (Kalbfleisch and Prentice, 2002). In the provided codes, gama = 1, which gives the partial linear proportional odds model. But setting gama = 0 gives the partial linear proportional hazards model. In general, for gama >= 0, it gives a family of survival models considered by Dabrowska and Doksum (1988).

• Martingale-based estimating equations for linear transformation models

The R codes solve the martingale-based estimating equations for a class of linear transformation models proposed by Chen et al. (2002, Biometrika). In particular, we consider linear transformation models a specified error distribution indexed by a parameter r. Here, r = 0 gives the PH model, r = 1 gives the PO model and r > 0 gives a family of survival models proposed by Dabrowska and Doksum (1988). An illustration via simulation studies is also provided. An R package, named "TransModel" has also been created and uploaded to CRAN.

• Weighted estimating equations for linear transformation models with nested case-control sampling

The computation codes, written in the C language, solve the inverse selected probability weighted (ISPW) martingale-based estimating equations for a general class of linear transformation models with nested case-control sampling, which generalizes Samuelsen's maximum pseudo-likelihood estimation method (Samuelsen, 1997) for the proportional hazards model. The codes were developed by Wenbin Lu and Mengling Liu. The original paper is published in Lifetime Data Analysis (2012, 18, 80-93). Download the C code here for analysis of a data from the Wilms's Tumor Study (D'Angio et al. 1989; Green et al. 1998). We conducted nested case-control sampling within the full cohort with the control size = 1 or 2. In the provided codes, size =2 and gama = 2, which gives a model from the family of survival models considered by Dabrowska and Doksum (1988). In this family, gama = 0 gives the partial linear proportional hazards model while gama = 1 gives the proportional odds model.

• Sample size and power calculation for the proportional hazards cure model

The R codes were developed for sample size and power calculation under the proportional hazards cure model. An R package, named "NPHMC" has been uploaded to CRAN. The codes were developed by Chao Cai, Songfeng Wang, Wenbin Lu and Jiajia Zhang. The original paper is published in Statistics in Medicine (2012, 31, 3959-3971).

• Variable  selection for optimal treatment decision

The R codes were developed for a penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables, and is appropriate for either censored or uncensored continuous response. An R package, named "OTRselect" has been uploaded to CRAN. The codes were developed by Yuan Geng, Shannon Holloway, and Wenbin Lu. The original papers are published in Statistical Methods in Medical Research (2013, 22, 493-504) and Statistics in Medicine (2015, 34, 1169-1184).

• 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 (2017, 112, 769-778).

• 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 Jiang, Lu, Song and Davidian (JRSSB, 2017, 79, 1165-1185). 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 Fan, Lu, Song and Zhou (JRSSB, 2017, 79, 1565-1582). 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.

• An EM algorithm for fitting the generalized odds-rate model with interval censored data

The R codes were developed for fitting the generalized odds-rate model with interval censored data. An R package, named "ICGOR" has been uploaded to CRAN. The codes were developed by Jie Zhou, Jiajia Zhang and Wenbin Lu. The original paper is published in Statistics in Medicine (2017, 36, 1157-1171).

• 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). An R package, named "ITRSelect" has been uploaded to CRAN.

• Estimation of the optimal treatment regime with heterogeneous individualized treatment effects

The R codes were developed for the maximin projection learning method proposed in the paper "Maximin projection learning for optimal treatment decisions with heterogeneous treatment effects" by Shi, Song, Lu, and Fu (JRSSB, 2018+). An R package, named "ITRLearn" has been uploaded to CRAN.