
- Experimental Design An Experimental Design for the Development of Adaptive
- Active Learning for Personalizing Treatment Department of Statistics
- Page 1 Collins, Linda M. A Conceptual Framework for Adaptive Preventive Interventions
- Adapting to Non-regularity when Constructing Dynamic Treatment Regimes
- Identify Qualitative Interaction Through Value of Information
- Customizing Treatment to the Patient: Adaptive Treatment Strategies https://www.nihms.nih.gov/pmc/articlerender.fcgi?artid=22097 1 of 7 07/18/2007 10:42 AM
- Statistical Inference in Dynamic Treatment Regimes Eric B. Laber, Min Qian, Daniel J. Lizotte, and Susan A. Murphy
- Structural Nested Mean Models for Assessing Time-Varying Causal Effect Moderation
- An Analysis for Menstrual Data with Time-Varying SUSAN A. MURPHY1, GILLIAN R. BENTLEY2 AND MARY ANN O'HANESIAN3
- Evaluation of Sample Size Formulae for Developing Adaptive Treatment Strategies Using a SMART Design.
- Sample Size Formulae for Two-Stage Randomized Trials with Survival Outcomes
- %This program calculates the powers of the tests based on estimators of the survival probabilities at the end of study period and the weighted
- Performance Guarantees for Individualized Treatment Rules (Supplementary Material)
- Adaptive Confidence Intervals for the Test Error in Classification Eric B. Laber and Susan A. Murphy1
- Screening Experiments for Developing Dynamic Treatment Regimes
- Variable Selection for Optimal Decision Making Lacey Gunter1,2
- Drug Use Prevention Data, Missing Assessments and Survival Jennifer M. Bacik1, Susan A. Murphy1, AND James C. Anthony2
- LIKELIHOOD INFERENCE IN THE ERRORS-IN-VARIABLES MODEL
- Likelihood Ratio Based Con dence Intervals in Survival Analysis S.A. MURPHY
- Structural Nested Mean Models for Assessing Time-Varying Causal Effect Moderation
- Structural Nested Mean Models for Assessing Time-Varying Causal Effect Moderation
- Computer science, adaptive treatment strategies, and
- Experimental Designs for Developing Adaptive Treatment Strategies
- Inverse Preference Elicitation for Dynamic Treatment Regimens
- Constructing Dynamic Treatment Regimes
- An Addiction Aftercare Study Responder counseling
- Small Sample Inference for Generalization Error Modeling and Computations
- Dynamically Individualizing Treatments Statistical Challenges and Some Solutions
- Inferential Challenges in Constructing Dynamic Treatment Regimes
- Using Data to Inform Sequential, Individualized
- Structural Nested Mean Models for Assessing Time-Varying Causal Effect Moderation
- Inference for Dynamic Treatment E. Laber & S. A. Murphy
- Adaptive Treatment Strategies Dan Lizotte
- Experimental Designs for Developing Adaptive Treatment Strategies
- Structural Nested Mean Models for Assessing Time-Varying Effect Moderation
- Performance Guarantee for Individualized Treatment Rules
- Bias Correction and Confidence Intervals for Fitted Q-iteration
- Small Sample Inference in Classification Using the CUD Susan A. Murphy
- Each panel represents a causal parameter in a K = 3 SNMM. These results suggest that it is possible to have gains in statistical efficiency using the 2-Stage Estimator (as compared to an unbiased G-Estimator)
- 7. Reference Chakraborty, B. (2007). Inference for Dynamic Treatment Regimes via Q-Learning. Unpublished PhD
- Getting SMART About Developing Individualized Sequences of Health Interventions Society for Behavioral Medicine 2011 Seminar 06 -Wednesday, April 27, 3:15PM-6:00PM
- YEAR OF MEETING: __2011___ TODAY'S DATE: ____10/12/2010_______ Page 1 of 4
- WORKSHOP TO BE GIVEN AT THE ADDICTION HEALTH SERVICES RESEARCH (AHSR) CONFERENCE Tentative Workshop Title
- ABCT 2011 Workshop Proposal Association for Behavioral and Cognitive Therapies
- Supplementary Material for "Sample Size Formulae for Two-Stage Randomized Trials with Censored Data"
- Drug Use Prevention Data, Missing Assessments and Survival Jennifer M. Bacik 1 , Susan A. Murphy 1 , AND James C. Anthony 2
- References (RAND -08/05) 1 Papers that explain how to use clinical experience, theory, etc to form dynamic
- Experimental Designs for Building and Refining CAM Interventions
- Inference for Dynamic Treatment Regimes E. Laber & S. A. Murphy
- Stat 426 EXAM 1 Name: Exam Instructions: Use a separate piece of paper to answer each question. Label the
- Advertise on NYTimes.com THE TIMES MAGAZINE T MAGAZINE KEY PLAY
- SEMIPARAMETRIC LIKELIHOOD RATIO INFERENCE BY S.A. MURPHY1 AND A.W. VAN DER VAART
- Efficient Reinforcement Learning Multiple Reward Functions
- Adaptive Treatment Strategies Getting SMART About Developing Individualized
- Anthony, M and Bartlett, P (1999). Neural Network Learning: Theoretical Foundations. Cambridge University Press.
- Revised 3/04 This paper benefited substantially from conversations with William Axinn. This research was
- Adaptive Confidence Intervals for the Test Error in Classification Online Supplement
- Informing the Individualization of Treatment Using PragmaticTrial Data
- Adaptive Confidence Intervals for the Test Error in Classification
- SEMIPARAMETRIC MIXTURES IN CASECONTROL STUDIES
- Efficient RL with Multiple Reward Functions for Randomized Clinical Trial Analysis
- Inference for Dynamic Treatment E. Laber & S. A. Murphy
- An Experimental Paradigm for Developing Adaptive
- A Novel Approach for Developing Whole System Herbal Therapies
- Adaptive Confidence Intervals for Regression
- Inbal Nahum-Shani The Methodology Center, Penn StateThe Methodology Center, Penn State
- Adaptive Treatment Strategies SMART Studies
- Adaptive Confidence Intervals for the Test Error in Classification
- Small Sample Inference for Generalization Error in Classification Using the CUD Bound
- Marginal Mean Models for Dynamic Regimes S. A. Murphy, M. J. van der Laan, J. M. Robins and CPPRG 1
- [1] Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, pages 11371145, 1995.
- The Multi-phase Optimization Strategy: A New Way to Develop Multi-component Interventions
- Projected partial likelihood and its application to longitudinal SUSAN MURPHY AND BING LI
- Acknowledgements: We thank Martin Keller and the investigators
- Informing the Individualization of Treatment: Exploratory Analysis of STAR*D
- MLE IN THE PROPORTIONAL ODDS MODEL BY S.A. MURPHY1, A.J. ROSSINI
- "Inverse Preference Elicitation" for Dynamic Treatment Regimes
- A Central Limit Theorem for Local Martingales with Applications to the Analysis of Longitudinal Data
- Adaptive Confidence Intervals for the Test Error in Classification
- Inference for Dynamic Treatment E. Laber & S. A. Murphy
- Submitted to the Annals of Statistics PERFORMANCE GUARANTEES FOR INDIVIDUALIZED
- Model Selection for Individualized Treatment Rules Min Qian, Susan A. Murphy
- [1] Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, pages 11371145, 1995.
- A Comparison of Randomized Trials and the Multi-phase Optimization Strategy for Behavioral
- DISCRETE-TIME MULTILEVEL HAZARD ANALYSIS Jennifer S. Barber*
- A-Learning for Approximate Planning Department of Electrical Engineering
- Inverse Preference Elicitation for Dynamic Treatment Regimes
- An Experimental Paradigm for Developing Adaptive Treatment
- This paper benefited substantially from conversations with William Axinn. This research was supported by National Institute of Child Health and Human Development grant HD32912, by National
- Optimizing interventions 1 A Strategy for Optimizing and Evaluating Behavioral Interventions
- Ignorable dropout in longitudinal studies BY MARIA ELEANOR V. TIPA, SUSAN A. MURPHY
- Inference for Dynamic Treatment E. Laber & S. A. Murphy
- NCDEU 2011 Full-Day Workshop Proposal NCDEU 2011 Annual Meeting, June 13-16, 2011 (Boca Raton, Florida)
- Statistical Methodology for a SMART Design in the Development of Adaptive
- Small Sample Prediction Intervals for the Test Error In Classification
- S.A. Murphy Univ. of Michigan
- Inference for Dynamic Treatment E. Laber & S. A. Murphy
- SAMISCTM/MJFF Satellite Meeting 22 February/S Murphy
- CPDD 2011 Annual Meeting June 22, 2011 Hollywood, FL
- Adaptive Approach to Naltrexone Treatment for Alcoholism
- Active Learning for Developing Personalized Treatment Department of Statistics
- Experiences with a Novel Clinical Trial Design Developing Dynamic, Sequential Treatments that Optimize Mental Health Outcomes
- Time-varying Subgroups Analysis 1 Running head: TIME-VARYING SUBGROUPS ANALYSIS
- Innovative Communication Intervention for Older Nonverbal
- Maximum Likelihood Estimation of the Structural Nested Mean Model Using SAS PROC NLP: With Application to the Study of Time-Varying Moderators of the Effect of Weight Change on Quality of Life
- James R. McKay, Ph.D. University of Pennsylvania
- STATISTICS 612: Advanced Topics in Theoretical Statistics Course description: Topics will include a review of large sample theory for common para-
- Dynamic Treatment Regimes Min Qian1,, Inbal Nahum-Shani2 and Susan A. Murphy1
- Getting SMART About Developing Individualized Sequences of Health Interventions
- What are adaptive treatment strategies (ATS)? Give examples of ATSs. Discuss why ATSs are needed and how they inform clinical practic
- An emerging and exciting area of clinical science involves the SA Murphy 1
- ISCTM/MJFF Satellite Meeting 22 February/S Murphy 1
- Adaptive confidence intervals for nonregular parameters
- ISCTM/MJFF Satellite Meeting 22 February/S Murphy 1
- ISCTM/MJFF Satellite Meeting 22 February/S Murphy 1
- ISCTM/MJFF Satellite Meeting 22 February/S Murphy 1
- Statistical Inference in Dynamic Treatment Regimes Eric B. Laber, Daniel J. Lizotte, Min Qian,
- SA Murphy 1 Getting SMART about Adapting Interventions
- SMART Design Issues and the Consideration of Opposing Daniel Almirall
- Adaptive confidence intervals for nonregular parameters