|Grant Number:||5R01CA140632-03 Interpret this number|
|Primary Investigator:||Lu, Wenbin|
|Organization:||North Carolina State University Raleigh|
|Project Title:||Flexible Statistical Methods for Complex Survival Data in Biomedical Studies|
DESCRIPTION (provided by applicant): The broad, long-term objectives of this research are the developments of new statistical methodology for the analysis of survival data from both epidemiological studies and clinical trials. Significant progress has been made in statistical modeling and inference in survival data analysis; however, there are still many open questions and emerging challenges posed by new study designs, advanced technologies, as well as the growing scale and complexity of medical studies. In this proposed research, we will explore two general classes of semiparametric models, the transformation model and the accelerated failure time model, for analyzing complex survival data. These models not only are complements to Cox's proportional hazards model, but also provide general regression frameworks and possibly better strategies for modeling survival data. Thus, they play important roles in many biomedical applications by offering comprehensive survival analysis. We seek to develop statistically sound methods that not only make proper use of data information and structure but also are powerful and computationally efficient. Motivated by problems arising from the investigators' collaborative work on the New York University Women's Health Study (NYUWHS) and the Health Effects of Arsenic Longitudinal Study (HEALS), our methodology developments include the following four specific aims: (1.) To explore a broad class of linear transformation models in nested case-control (NCC) studies; (2.) To investigate efficient estimation of the accelerated failure time (AFT) model in case-cohort (CC) and nested case-control studies through a unified likelihood-based approach; (3.) To develop semiparametric Bayesian inference methods for the AFT cure model for the analysis of survival data from cohort studies or clinical trials in an admixture population with susceptible and non-susceptible (cured) subjects; (4.) To study partially linear regression modeling and the associated inference procedures for censored survival data from cohort studies or clinical trials. Results from the proposed project will be relevant and applicable to many biomedical studies. In all the specific aims, we will study the theoretical properties of the proposed estimators, and develop reliable numerical algorithms for implementing the proposed estimation methods. Special effort will also be devoted to developing and disseminating software for practitioners. We will carry out extensive simulation studies to evaluate relevance of the theory and the finite sample performance of the proposed estimators. We will also investigate the performance of the proposed methods on published datasets, compare them with existing approaches and demonstrate their applications in major clinical and epidemiological studies, including the NYUWHS and the HEALS. 1 PUBLIC HEALTH RELEVANCE: The proposed research aims to develop novel statistical approaches for analyzing survival data under various study designs, from admixed populations, and with complex covariates effects. The completion of our proposed research will provide reliable and efficient statistical methods for complex survival data that are commonly encountered in clinical and epidemiological studies. These methods can facilitate scientists' understanding of etiology of complex diseases and eventually lead to better design of disease prevention, prognosis and treatment strategies to improve human health. 1
Censored Rank Independence Screening for High-dimensional Survival Data.
Authors: Song R, Lu W, Ma S, Jeng XJ
Source: Biometrika, 2014;101(4), p. 799-814.
A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.
Authors: Geng Y, Lu W, Zhang HH
Source: Stat, 2014;3(1), p. 337-350.
On optimal treatment regimes selection for mean survival time.
Authors: Geng Y, Zhang HH, Lu W
Source: Stat Med, 2014 Dec 16;null, p. null.
EPub date: 2014 Dec 16.
Testing goodness-of-fit for the proportional hazards model based on nested case-control data.
Authors: Lu W, Liu M, Chen YH
Source: Biometrics, 2014 Dec;70(4), p. 845-51.
EPub date: 2014 Oct 8.
GENE-LEVEL PHARMACOGENETIC ANALYSIS ON SURVIVAL OUTCOMES USING GENE-TRAIT SIMILARITY REGRESSION.
Authors: Tzeng JY, Lu W, Hsu FC
Source: Ann Appl Stat, 2014;8(2), p. 1232-1255.
Accelerated intensity frailty model for recurrent events data.
Authors: Liu B, Lu W, Zhang J
Source: Biometrics, 2014 Mar 3;null, p. null.
EPub date: 2014 Mar 3.
Kernel Smoothed Profile Likelihood Estimation in the Accelerated Failure Time Frailty Model for Clustered Survival Data.
Authors: Liu B, Lu W, Zhang J
Source: Biometrika, 2013;100(3), p. 741-755.
A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes.
Authors: Kim JP, Lu W, Sit T, Ying Z
Source: J Am Stat Assoc, 2013 Jan 1;108(501), p. 217-227.
Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity.
Authors: Liu M, Lu W, Krogh V, Hallmans G, Clendenen TV, Zeleniuch-Jacquotte A
Source: Biostatistics, 2013 Sep;14(4), p. 682-94.
EPub date: 2013 Apr 30.
More efficient estimators for case-cohort studies.
Authors: Kim S, Cai J, Lu W
Source: Biometrika, 2013;100(3), p. 695-708.
A Seminonparametric Approach to Joint Modeling of A Primary Binary Outcome and Longitudinal Data Measured at Discrete Informative Times.
Authors: Yan S, Zhang D, Lu W, Grifo JA, Liu M
Source: Stat Biosci, 2012 Nov 1;4(2), p. 213-234.
MOMENT-BASED METHOD FOR RANDOM EFFECTS SELECTION IN LINEAR MIXED MODELS.
Authors: Ahn M, Zhang HH, Lu W
Source: Stat Sin, 2012 Oct 1;22(4), p. 1539-1562.
Time-varying latent effect model for longitudinal data with informative observation times.
Authors: Cai N, Lu W, Zhang HH
Source: Biometrics, 2012 Dec;68(4), p. 1093-102.
EPub date: 2012 Oct 1.
Sample size calculation for the proportional hazards cure model.
Authors: Wang S, Zhang J, Lu W
Source: Stat Med, 2012 Dec 20;31(29), p. 3959-71.
EPub date: 2012 Jul 11.
Variance Estimation in Censored Quantile Regression via Induced Smoothing.
Authors: Panga L, Lu W, Wang HJ
Source: Comput Stat Data Anal, 2012 Apr 1;56(4), p. 785-796.
EPub date: 2010 Apr 21.
A Semiparametric Marginalized Model for Longitudinal Data with Informative Dropout.
Authors: Liu M, Lu W
Source: J Probab Stat, 2012 Jan 1;2012(2012), p. null.
Variable selection for optimal treatment decision.
Authors: Lu W, Zhang HH, Zeng D
Source: Stat Methods Med Res, 2013 Oct;22(5), p. 493-504.
EPub date: 2011 Nov 23.
On estimation of linear transformation models with nested case-control sampling.
Authors: Lu W, Liu M
Source: Lifetime Data Anal, 2012 Jan;18(1), p. 80-93.
EPub date: 2011 Sep 13.
A note on monotonicity assumptions for exact unconditional tests in binary matched-pairs designs.
Authors: Li X, Liu M, Goldberg JD
Source: Biometrics, 2011 Dec;67(4), p. 1666-8.
EPub date: 2011 Apr 5.
Sufficient dimension reduction for censored regressions.
Authors: Lu W, Li L
Source: Biometrics, 2011 Jun;67(2), p. 513-23.
EPub date: 2010 Sep 28.
On Estimation of Partially Linear Transformation Models.
Authors: Lu W, Zhang HH
Source: J Am Stat Assoc, 2010 Jun 1;105(490), p. 683-691.
Sparse Estimation and Inference for Censored Median Regression.
Authors: Shows JH, Lu W, Zhang HH
Source: J Stat Plan Inference, 2010 Jul;140(7), p. 1903-1917.
Cox regression model with time-varying coefficients in nested case-control studies.
Authors: Liu M, Lu W, Shore RE, Zeleniuch-Jacquotte A
Source: Biostatistics, 2010 Oct;11(4), p. 693-706.
EPub date: 2010 Jun 3.
On Sparse Estimation for Semiparametric Linear Transformation Models.
Authors: Zhang HH, Lu W, Wang H
Source: J Multivar Anal, 2010 Aug 1;101(7), p. 1594-1606.
EFFICIENT ESTIMATION FOR AN ACCELERATED FAILURE TIME MODEL WITH A CURE FRACTION.
Authors: Lu W
Source: Stat Sin, 2010;20, p. 661-674.
Haplotype-based pharmacogenetic analysis for longitudinal quantitative traits in the presence of dropout.
Authors: Tzeng JY, Lu W, Farmen MW, Liu Y, Sullivan PF
Source: J Biopharm Stat, 2010 Mar;20(2), p. 334-50.