Marshall Joffe’s Contributions to Causal Inference, Biostatistics, and Epidemiology

Abstract We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall’s research and their significance for cau...

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Published inAmerican journal of epidemiology Vol. 193; no. 4; pp. 563 - 576
Main Authors Isenberg, Dane, Kennedy, Edward H, Landis, J Richard, Mitra, Nandita, Robins, James M, Roy, Jason, Stephens-Shields, Alisa J, Yang, Wei, Small, Dylan S
Format Journal Article
LanguageEnglish
Published United States Oxford University Press 08.04.2024
Oxford Publishing Limited (England)
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Abstract Abstract We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall’s research and their significance for causal inference but also offer a portrayal of Marshall’s personal accomplishments and character. Our discussion of Marshall’s research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall’s unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall’s research. While Marshall’s impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.
AbstractList We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall’s research and their significance for causal inference but also offer a portrayal of Marshall’s personal accomplishments and character. Our discussion of Marshall’s research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall’s unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall’s research. While Marshall’s impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.
We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.
Abstract We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall’s research and their significance for causal inference but also offer a portrayal of Marshall’s personal accomplishments and character. Our discussion of Marshall’s research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall’s unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall’s research. While Marshall’s impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.
Author Isenberg, Dane
Robins, James M
Roy, Jason
Kennedy, Edward H
Landis, J Richard
Stephens-Shields, Alisa J
Mitra, Nandita
Yang, Wei
Small, Dylan S
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  givenname: J Richard
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Copyright The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2023
The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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– notice: The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Issue 4
Keywords selective ignorability
posttreatment variables
noncompliance
case-control studies
longitudinal studies
Language English
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Snippet Abstract We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and...
We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By...
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SubjectTerms Biostatistics
Case-Control Studies
Causality
Epidemiology
Estimation
Humans
Inference
Male
Time dependence
Title Marshall Joffe’s Contributions to Causal Inference, Biostatistics, and Epidemiology
URI https://www.ncbi.nlm.nih.gov/pubmed/37943689
https://www.proquest.com/docview/3039799812
https://www.proquest.com/docview/2889238191
Volume 193
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