Statistical analysis plan for stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study

[1]  Eva Petkova,et al.  Generated effect modifiers (GEM's) in randomized clinical trials. , 2017, Biostatistics.

[2]  Michael R. Kosorok,et al.  Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens , 2016, 1611.02314.

[3]  Eva Petkova,et al.  Stratified Psychiatry via Convexity-Based Clustering with Applications Towards Moderator Analysis. , 2016, Statistics and its interface.

[4]  H. Kraemer,et al.  Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design. , 2016, Journal of psychiatric research.

[5]  C. Burger,et al.  RNA expression profiling , 2016 .

[6]  E. Petkova,et al.  Patient characteristics as a moderator of post-traumatic stress disorder treatment outcome: combining symptom burden and strengths , 2016, BJPsych Open.

[7]  Eva Petkova,et al.  Flexible functional regression methods for estimating individualized treatment rules , 2016, Stat.

[8]  Eric B. Laber,et al.  Tree-based methods for individualized treatment regimes. , 2015, Biometrika.

[9]  Eva Petkova,et al.  Treatment decisions based on scalar and functional baseline covariates , 2015, Biometrics.

[10]  F. Holsboer,et al.  RNA expression profiling in depressed patients suggests retinoid-related orphan receptor alpha as a biomarker for antidepressant response , 2015, Translational Psychiatry.

[11]  Donglin Zeng,et al.  On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning , 2015, Stat.

[12]  K. Tansey,et al.  An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. , 2014, The American journal of psychiatry.

[13]  H. Mayberg,et al.  Pretreatment Brain States Identify Likely Nonresponse to Standard Treatments for Depression , 2014, Biological Psychiatry.

[14]  L. Williams,et al.  Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder , 2014, British Journal of Psychiatry.

[15]  Lu Tian,et al.  A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates , 2012, 1212.2995.

[16]  Eric B. Laber,et al.  Dynamic treatment regimes: technical challenges and applications. , 2014, Electronic journal of statistics.

[17]  Wenbin Lu,et al.  Variable selection for optimal treatment decision , 2013, Statistical methods in medical research.

[18]  Yingqi Zhao,et al.  Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme , 2013, Biometrics.

[19]  Helena Chmura Kraemer,et al.  Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach , 2013, Statistics in medicine.

[20]  I. Craig,et al.  Candidate Genes Expression Profile Associated with Antidepressants Response in the GENDEP Study: Differentiating between Baseline ‘Predictors' and Longitudinal ‘Targets' , 2012, Neuropsychopharmacology.

[21]  D. Ghosh,et al.  On Bayesian methods of exploring qualitative interactions for targeted treatment , 2012, Statistics in medicine.

[22]  J. Mundt,et al.  Vocal Acoustic Biomarkers of Depression Severity and Treatment Response , 2012, Biological Psychiatry.

[23]  Donglin Zeng,et al.  Estimating Individualized Treatment Rules Using Outcome Weighted Learning , 2012, Journal of the American Statistical Association.

[24]  O. Wolkowitz,et al.  Resting leukocyte telomerase activity is elevated in major depression and predicts treatment response , 2012, Molecular Psychiatry.

[25]  Min Zhang,et al.  Estimating optimal treatment regimes from a classification perspective , 2012, Stat.

[26]  Hans-Georg Müller,et al.  Functional Data Analysis , 2016 .

[27]  J. Ware,et al.  Detecting Moderator Effects Using Subgroup Analyses , 2013, Prevention Science.

[28]  A. Gelman,et al.  Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box , 2011 .

[29]  Lacey Gunter,et al.  Variable Selection for Qualitative Interactions in Personalized Medicine While Controlling the Family-Wise Error Rate , 2011, Journal of biopharmaceutical statistics.

[30]  S. Murphy,et al.  PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES. , 2011, Annals of statistics.

[31]  Eva Petkova,et al.  Optimal Partitioning for Linear Mixed Effects Models: Applications to Identifying Placebo Responders , 2010, Journal of the American Statistical Association.

[32]  Rakesh Jain,et al.  Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: Results of the BRITE-MD study , 2009, Psychiatry Research.

[33]  D. Steinberg.  CART: Classification and Regression Trees , 2009 .

[34]  T. Tarpey,et al.  Partitioning of Functional Data for Understanding Heterogeneity in Psychiatric Conditions. , 2009, Statistics and its interface.

[35]  Eric J. Nestler,et al.  The molecular neurobiology of depression , 2008, Nature.

[36]  C. Robert.  Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv. , 2008 .

[37]  Irina Bondarenko,et al.  Diagnostics for Multiple Imputations , 2007 .

[38]  Jianqing Fan,et al.  Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.

[39]  Jianjun Li,et al.  Detecting Qualitative Interactions in Clinical Trials: An Extension of Range Test , 2006, Journal of biopharmaceutical statistics.

[40]  D. Kupfer,et al.  Acute and Longer- Term Outcomes in Depressed Outpatients Requiring One or Several Treatment Steps: A STAR*D Report , 2006 .

[41]  B. Lebowitz,et al.  Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. , 2006, The American journal of psychiatry.

[42]  Margaret Sullivan Pepe,et al.  Evaluating Markers for Selecting a Patient's Treatment , 2004, Biometrics.

[43]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[44]  James M. Robins,et al.  Optimal Structural Nested Models for Optimal Sequential Decisions , 2004 .

[45]  T. Tarpey,et al.  Profiling Placebo Responders by Self-Consistent Partitioning of Functional Data , 2003 .

[46]  D. Kupfer,et al.  Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. , 2003, The Psychiatric clinics of North America.

[47]  S. Murphy,et al.  Optimal dynamic treatment regimes , 2003 .

[48]  David E. Booth,et al.  Analysis of Incomplete Multivariate Data , 2000, Technometrics.

[49]  J. Schafer.  Multiple imputation: a primer , 1999, Statistical methods in medical research.

[50]  S. Wellek.  Testing for Absence of Qualitative Interactions Between Risk Factors and Treatment Effects , 1997 .

[51]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[52]  K. Anderson,et al.  Cardiovascular disease risk profiles. , 1991, American heart journal.

[53]  Richard A. Olshen,et al.  CART: Classification and Regression Trees , 1984 .

[54]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .