1
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Liang M, Yu M. Relative contrast estimation and inference for treatment recommendation. Biometrics 2023; 79:2920-2932. [PMID: 36645310 DOI: 10.1111/biom.13826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/29/2022] [Indexed: 01/17/2023]
Abstract
When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale-invariant contrasts between the conditional treatment effects. By showing that all scale-invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two-step approach that minimizes a doubly robust loss function for initial estimation and then performs a one-step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.
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Affiliation(s)
- Muxuan Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA
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2
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Shen J, Hubbard RA, Linn KA. Estimation and evaluation of individualized treatment rules following multiple imputation. Stat Med 2023; 42:4236-4256. [PMID: 37496450 DOI: 10.1002/sim.9857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/12/2023] [Accepted: 07/14/2023] [Indexed: 07/28/2023]
Abstract
An individualized treatment rule (ITR) is a function that inputs patient-level information and outputs a recommended treatment. An important focus of precision medicine is to develop optimal ITRs that maximize a population-level distributional summary. However, guidance for estimating and evaluating optimal ITRs in the presence of missing data is limited. Our work is motivated by the Social Incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study. Participants were randomized to a control or one of three interventions designed to increase physical activity and were given wearable devices to record daily steps as a measure of physical activity. Many participants were missing at least one daily step count during the study period. In the primary analysis of the STEP UP trial, multiple imputation (MI) was used to address missingness in daily step counts. Despite ubiquitous use of MI in practice, it has been given relatively little attention in the context of personalized medicine. We fill this gap by describing two frameworks for estimation and evaluation of an optimal ITR following MI and assessing their performance using simulated data. One framework relies on splitting the data into independent training and testing sets for estimation and evaluation, respectively. The other framework estimates an optimal ITR using the full data and constructs anm $$ m $$ -out-of-n $$ n $$ bootstrap confidence interval to evaluate its performance. Finally, we provide an illustrative analysis to estimate and evaluate an optimal ITR from the STEP UP data with a focus on practical considerations such as choosing the number of imputations.
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Affiliation(s)
- Jenny Shen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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3
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Li Z, Chen J, Laber E, Liu F, Baumgartner R. Optimal Treatment Regimes: A Review and Empirical Comparison. Int Stat Rev 2023. [DOI: 10.1111/insr.12536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Zhen Li
- Department of Statistics North Carolina State University Raleigh 27607 NC USA
| | - Jie Chen
- Department of Biometrics Overland Pharmaceuticals Dover 19901 DE USA
| | - Eric Laber
- Department of Statistical Science, Department of Biostatistics and Bioinformatics Duke University Durham 27708 NC USA
| | - Fang Liu
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
| | - Richard Baumgartner
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
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4
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Morzywołek P, Steen J, Van Biesen W, Decruyenaere J, Vansteelandt S. On estimation and cross-validation of dynamic treatment regimes with competing risks. Stat Med 2022; 41:5258-5275. [PMID: 36055675 DOI: 10.1002/sim.9568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 06/09/2022] [Accepted: 07/11/2022] [Indexed: 11/12/2022]
Abstract
The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomized controlled trials have tried to answer this question, but these contrast only a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different prespecified timing strategies for renal replacement therapy initiation based on time-updated levels of serum potassium, pH, and fluid balance in critically ill patients with AKI with the aim to minimize 30-day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a nonparametric one - using an inverse probability weighted Aalen-Johansen estimator - and a semiparametric one - using dynamic-regime marginal structural models. Furthermore, we suggest an easy to implement cross-validation technique to assess the out-of-sample performance of the optimal dynamic treatment regime. Our work illustrates the potential of data-driven medical decision support based on routinely collected observational data.
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Affiliation(s)
- Paweł Morzywołek
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Johan Steen
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.,Renal Division, Ghent University Hospital, Ghent, Belgium.,Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Wim Van Biesen
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.,Renal Division, Ghent University Hospital, Ghent, Belgium
| | - Johan Decruyenaere
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.,Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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5
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Lyden GR, Vock DM, Sur A, Morrell N, Lee CM, Patrick ME. Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2022; 23:1053-1064. [PMID: 35543888 PMCID: PMC9357163 DOI: 10.1007/s11121-022-01371-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 10/18/2022]
Abstract
M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking.
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Affiliation(s)
- Grace R Lyden
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - David M Vock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Aparajita Sur
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Nicole Morrell
- Center for Applied Research and Educational Improvement, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA
| | - Christine M Lee
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Megan E Patrick
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, MI, 48106-1248, USA.
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6
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Liang M, Choi YG, Ning Y, Smith MA, Zhao YQ. Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2022; 23:262. [PMID: 38098839 PMCID: PMC10720606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.
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Affiliation(s)
- Muxuan Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida 32611, USA
| | - Young-Geun Choi
- Department of Statistics, Sookmyung Women's University, Seoul 04310, Korea
| | - Yang Ning
- Department of Statistics and Data Science, Cornell University, Ithaca, Newyork 14853, USA
| | - Maureen A Smith
- Departments of Population Health and Family Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Ying-Qi Zhao
- Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
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7
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Smith SK, Somers TJ, Kuhn E, Laber E, Sung AD, Syrjala KL, Feger B, Kelleher SA, Majestic C, Gebert R, LeBlanc M, Owen JE, Applebaum AJ. A SMART approach to optimizing delivery of an mHealth intervention among cancer survivors with posttraumatic stress symptoms. Contemp Clin Trials 2021; 110:106569. [PMID: 34536584 PMCID: PMC8595815 DOI: 10.1016/j.cct.2021.106569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/01/2021] [Accepted: 09/13/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND/AIMS Many cancer survivors who received intensive treatment such as hematopoietic stem cell transplantation (HCT) experience posttraumatic stress disorder (PTSD) symptoms. PTSD is associated with lower quality of life and other symptoms that require clinical treatment. The iterative treatment decisions that happen in clinical practice are not adequately represented in traditional randomized controlled trials (RCT) of PTSD treatments. The proposed stepped-care SMART design allows for evaluation of initial response to the Cancer Distress Coach mobile app; adaptive stepped-care interventions; and precision treatment strategies that tailor treatment selection to patient characteristics. METHODS/DESIGN HCT survivors (N = 400) reporting PTSD symptoms are being recruited at two cancer centers and randomly assigned to: 1) Cancer Distress Coach app or 2) Usual Care. The app includes educational and cognitive behavioral therapy (CBT)-based activities. Four weeks post-randomization, participants re-rate their PTSD symptoms and, based on intervention response, non-responders are re-randomized to receive video-conferenced sessions with a therapist: 3) coaching sessions in using the mobile app; or 4) CBT specific to HCT survivors. Participants complete outcome measures of PTSD, depression, and anxiety after Months 1, 3, and 6. Participant characteristics moderating intervention responses will be examined. CONCLUSIONS This novel adaptive trial design will afford evidence that furthers knowledge about optimizing PTSD interventions for HCT survivors. To our knowledge, this study is the first SMART design evaluating PTSD symptom management in cancer survivors. If successful, it could be used to optimize treatment among a range of cancer and other trauma survivors.
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Affiliation(s)
- Sophia K Smith
- School of Nursing, Duke University, Durham, NC, United States of America; Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States of America.
| | - Tamara J Somers
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States of America; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America
| | - Eric Kuhn
- Dissemination and Training Division, National Center for PTSD, Palo Alto, CA, United States of America; Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, United States of America
| | - Eric Laber
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America
| | - Anthony D Sung
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States of America; Department of Medicine, Division of Hematologic Malignancies and Cellular Therapy, Duke University, Durham, NC, United States of America
| | - Karen L Syrjala
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Bryan Feger
- Duke Clinical Research Institute, Durham, NC, United States of America
| | - Sarah A Kelleher
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America
| | - Catherine Majestic
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America
| | - Rebecca Gebert
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Matthew LeBlanc
- School of Nursing, Duke University, Durham, NC, United States of America
| | - Jason E Owen
- Dissemination and Training Division, National Center for PTSD, Palo Alto, CA, United States of America
| | - Allison J Applebaum
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America; Department of Psychology in Psychiatry, Weill Cornell Medicine, New York, NY, United States of America
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8
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Imai K, Li ML. Experimental Evaluation of Individualized Treatment Rules. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1923511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Kosuke Imai
- Department of Government and Department of Statistics, Harvard University, Cambridge, MA
| | - Michael Lingzhi Li
- Operation Research Center, Massachusetts Institute of Technology, Cambridge, MA
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9
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Wu Y, Wang L, Fu H. Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1929246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Yunan Wu
- Yale University, Department of Biostatistics, New Haven, 06520 United States
| | - Lan Wang
- University of Miami, Department of Management Science, Coral Gables, 33124 United States
| | - Haoda Fu
- Eli Lilly and Company, Biometrics and Advanced Analytics, Indianapolis, United States
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10
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Kapelner A, Bleich J, Levine A, Cohen ZD, DeRubeis RJ, Berk R. Evaluating the Effectiveness of Personalized Medicine With Software. Front Big Data 2021; 4:572532. [PMID: 34085036 PMCID: PMC8167073 DOI: 10.3389/fdata.2021.572532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement. Our software, "Personalized Treatment Evaluator" (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method's promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression.
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Affiliation(s)
- Adam Kapelner
- Department of Mathematics, Queens College, CUNY, Queens, NY, United States
| | - Justin Bleich
- Department of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, PA, United States
| | - Alina Levine
- Department of Mathematics, Queens College, CUNY, Queens, NY, United States
| | - Zachary D. Cohen
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Robert J. DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Richard Berk
- Department of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, PA, United States
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11
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Rekkas A, Paulus JK, Raman G, Wong JB, Steyerberg EW, Rijnbeek PR, Kent DM, van Klaveren D. Predictive approaches to heterogeneous treatment effects: a scoping review. BMC Med Res Methodol 2020; 20:264. [PMID: 33096986 PMCID: PMC7585220 DOI: 10.1186/s12874-020-01145-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 10/12/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. METHODS We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. RESULTS The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). CONCLUSIONS Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.
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Affiliation(s)
- Alexandros Rekkas
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jessica K Paulus
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, 800 Washington St, Box 63, Boston, MA, 02111, USA
| | - Gowri Raman
- Center for Clinical Evidence Synthesis, ICRHPS, Tufts Medical Center, Boston, MA, USA
| | - John B Wong
- Division of Clinical Decision Making, Tufts Medical Center, Boston, MA, USA
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, 800 Washington St, Box 63, Boston, MA, 02111, USA.
| | - David van Klaveren
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, 800 Washington St, Box 63, Boston, MA, 02111, USA
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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12
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Wu Y, Wang L. Resampling-based confidence intervals for model-free robust inference on optimal treatment regimes. Biometrics 2020; 77:465-476. [PMID: 32687215 DOI: 10.1111/biom.13337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 06/24/2020] [Indexed: 12/01/2022]
Abstract
We propose a new procedure for inference on optimal treatment regimes in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asymptotic distributions or do not necessarily guarantee consistent estimation of the parameter indexing the Bayes rule due to the use of surrogate loss. We first study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation. This estimator is shown to have an asymptotic normal distribution. Furthermore, we verify that a resampling procedure provides asymptotically accurate inference for both the parameter indexing the optimal treatment regime and the optimal value function. A new algorithm is developed to calculate the proposed estimator with substantially improved speed and stability. Numerical results demonstrate the satisfactory performance of the new methods.
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Affiliation(s)
- Yunan Wu
- School of Statistics, University of Minnesota, Minneapolis, Minnesota
| | - Lan Wang
- Department of Management Science, University of Miami, Coral Gables, Florida
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13
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Qiu H, Carone M, Sadikova E, Petukhova M, Kessler RC, Luedtke A. Optimal individualized decision rules using instrumental variable methods. J Am Stat Assoc 2020; 116:174-191. [PMID: 33731969 PMCID: PMC7959164 DOI: 10.1080/01621459.2020.1745814] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 12/30/2019] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
Abstract
There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule.
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Affiliation(s)
| | - Marco Carone
- Dept. of Biostatistics, University of Washington
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14
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Sies A, Van Mechelen I. Estimating the quality of optimal treatment regimes. Stat Med 2019; 38:4925-4938. [PMID: 31424128 DOI: 10.1002/sim.8342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 07/13/2019] [Accepted: 07/18/2019] [Indexed: 11/08/2022]
Abstract
When multiple treatment alternatives are available for a disease, an obvious question is which alternative is most effective for which patient. One may address this question by searching for optimal treatment regimes that specify for each individual the preferable treatment alternative based on that individual's baseline characteristics. When such a regime has been estimated, its quality (in terms of the expected outcome if it was used for treatment assignment of all patients in the population under study) is of obvious interest. Obtaining a good and reliable estimate of this quantity is a key challenge for which so far no satisfactory solution is available. In this paper, we consider for this purpose several estimators of the expected outcome in conjunction with several resampling methods. The latter have been evaluated before within the context of statistical learning to estimate the prediction error of estimated prediction rules. Yet, the results of these evaluations were equivocal, with different best performing methods in different studies, and with near-zero and even negative correlations between true and estimated prediction errors. Moreover, for different reasons, it is not straightforward to extrapolate the findings of these studies to the context of optimal treatment regimes. To address these issues, we set up a new and comprehensive simulation study. In this study, combinations of different estimators with .632+ and out-of-bag bootstrap resampling methods performed best. In addition, the study shed a surprising new light on the previously reported problematic correlations between true and estimated prediction errors in the area of statistical learning.
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Affiliation(s)
- Aniek Sies
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Iven Van Mechelen
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
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15
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Abstract
Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.
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Affiliation(s)
- Michael R Kosorok
- Department of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.;
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleight, North Carolina, 27695, U.S.A.;
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16
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Sies A, Demyttenaere K, Van Mechelen I. Studying treatment-effect heterogeneity in precision medicine through induced subgroups. J Biopharm Stat 2019; 29:491-507. [PMID: 30794033 DOI: 10.1080/10543406.2019.1579220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Precision medicine, in the sense of tailoring the choice of medical treatment to patients' pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidence-based way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment-subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment-subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the data-analytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods can handle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments.
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Affiliation(s)
- Aniek Sies
- a Faculty of Psychology and Educational Sciences , KU Leuven , Leuven , Belgium
| | | | - Iven Van Mechelen
- a Faculty of Psychology and Educational Sciences , KU Leuven , Leuven , Belgium
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17
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Chambaz A, Zheng W, van der Laan MJ. TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD. Ann Stat 2017; 45:2537-2564. [PMID: 29398733 PMCID: PMC5794253 DOI: 10.1214/16-aos1534] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
This article studies the targeted sequential inference of an optimal treatment rule (TR) and its mean reward in the non-exceptional case, i.e., assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adaptive statistical parameter is worthy of interest on its own. Our main result is a central limit theorem which enables the construction of confidence intervals on both mean rewards under the current estimate of the optimal TR and under the optimal TR itself. The asymptotic variance of the estimator takes the form of the variance of an efficient influence curve at a limiting distribution, allowing to discuss the efficiency of inference. As a by product, we also derive confidence intervals on two cumulated pseudo-regrets, a key notion in the study of bandits problems. A simulation study illustrates the procedure. One of the corner-stones of the theoretical study is a new maximal inequality for martingales with respect to the uniform entropy integral.
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Affiliation(s)
- Antoine Chambaz
- UPL, Université Paris Nanterre
- University of California, Berkeley
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18
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Wang L, Lin Y, Chen JT. Simultaneous inference for treatment regimes. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2016.1217017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Lu Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Yong Lin
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH USA
| | - John T. Chen
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH USA
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19
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Abstract
Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not there exists some subset of observed covariates in which the treatment is more effective than the standard practice of no treatment. Furthermore, we wish to quantify the improvement in population mean outcome that will be seen if this subgroup receives treatment and the rest of the population remains untreated. We show that this problem is surprisingly challenging given how often it is an (at least implicit) study objective. Blindly applying standard techniques fails to yield any apparent asymptotic results, while using existing techniques to confront the non-regularity does not necessarily help at distributions where there is no treatment effect. Here, we describe an approach to estimate the impact of treating the subgroup which benefits from treatment that is valid in a nonparametric model and is able to deal with the case where there is no treatment effect. The approach is a slight modification of an approach that recently appeared in the individualized medicine literature.
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Affiliation(s)
- Alexander R Luedtke
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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20
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Lizotte DJ, Tahmasebi A. Prediction and tolerance intervals for dynamic treatment regimes. Stat Methods Med Res 2017; 26:1611-1629. [PMID: 28695763 DOI: 10.1177/0962280217708662] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.
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Affiliation(s)
- Daniel J Lizotte
- Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada
| | - Arezoo Tahmasebi
- Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada
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21
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Affiliation(s)
- Qian Guan
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - Brian J Reich
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
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22
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Abstract
An individualized treatment rule (ITR) is a treatment rule which assigns treatments to individuals based on (a subset of) their measured covariates. An optimal ITR is the ITR which maximizes the population mean outcome. Previous works in this area have assumed that treatment is an unlimited resource so that the entire population can be treated if this strategy maximizes the population mean outcome. We consider optimal ITRs in settings where the treatment resource is limited so that there is a maximum proportion of the population which can be treated. We give a general closed-form expression for an optimal stochastic ITR in this resource-limited setting, and a closed-form expression for the optimal deterministic ITR under an additional assumption. We also present an estimator of the mean outcome under the optimal stochastic ITR in a large semiparametric model that at most places restrictions on the probability of treatment assignment given covariates. We give conditions under which our estimator is efficient among all regular and asymptotically linear estimators. All of our results are supported by simulations.
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Affiliation(s)
- Alexander R. Luedtke
- Division of Biostatistics, University of California, 101 Haviland Hall, Berkeley, California 94720-7358, USA
| | - Mark J. van der Laan
- Division of Biostatistics, University of California, 101 Haviland Hall, Berkeley, California 94720-7358, USA
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23
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Luedtke AR, van der Laan MJ. STATISTICAL INFERENCE FOR THE MEAN OUTCOME UNDER A POSSIBLY NON-UNIQUE OPTIMAL TREATMENT STRATEGY. Ann Stat 2016; 44:713-742. [PMID: 30662101 PMCID: PMC6338452 DOI: 10.1214/15-aos1384] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We consider challenges that arise in the estimation of the mean outcome under an optimal individualized treatment strategy defined as the treatment rule that maximizes the population mean outcome, where the candidate treatment rules are restricted to depend on baseline covariates. We prove a necessary and sufficient condition for the pathwise differentiability of the optimal value, a key condition needed to develop a regular and asymptotically linear (RAL) estimator of the optimal value. The stated condition is slightly more general than the previous condition implied in the literature. We then describe an approach to obtain root-n rate confidence intervals for the optimal value even when the parameter is not pathwise differentiable. We provide conditions under which our estimator is RAL and asymptotically efficient when the mean outcome is pathwise differentiable. We also outline an extension of our approach to a multiple time point problem. All of our results are supported by simulations.
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24
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Laber EB, Zhao YQ, Regh T, Davidian M, Tsiatis A, Stanford JB, Zeng D, Song R, Kosorok MR. Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med 2015; 35:1245-56. [PMID: 26506890 DOI: 10.1002/sim.6783] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 10/07/2015] [Accepted: 10/08/2015] [Indexed: 12/18/2022]
Abstract
A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.
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Affiliation(s)
- Eric B Laber
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Ying-Qi Zhao
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Todd Regh
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Marie Davidian
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Anastasios Tsiatis
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Joseph B Stanford
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Donglin Zeng
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Rui Song
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
| | - Michael R Kosorok
- John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, U.K
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25
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Zhang B, Tsiatis AA, Laber EB, Davidian M. Response to reader reaction. Biometrics 2014; 71:267-273. [PMID: 25355405 DOI: 10.1111/biom.12229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Baqun Zhang
- School of Statistics, Renmin University of China, Beijing, China 100872
| | - Anastasios A. Tsiatis
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
| | - Eric B. Laber
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
| | - Marie Davidian
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
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