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Data-Driven Model Building for Life Course Epidemiology

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Petersen, Anne H ; Osler, Merete ; Ekstrøm, Claus T. / Data-Driven Model Building for Life Course Epidemiology. I: American Journal of Epidemiology. 2021 ; Bind 190, Nr. 9. s. 1898-1907.

Bibtex

@article{93d22d00b1c94bdca014456e35f012b3,
title = "Data-Driven Model Building for Life Course Epidemiology",
abstract = "Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.",
author = "Petersen, {Anne H} and Merete Osler and Ekstr{\o}m, {Claus T}",
note = "{\textcopyright} The Author(s) 2021. 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.",
year = "2021",
month = sep,
day = "1",
doi = "10.1093/aje/kwab087",
language = "English",
volume = "190",
pages = "1898--1907",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "9",

}

RIS

TY - JOUR

T1 - Data-Driven Model Building for Life Course Epidemiology

AU - Petersen, Anne H

AU - Osler, Merete

AU - Ekstrøm, Claus T

N1 - © The Author(s) 2021. 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.

PY - 2021/9/1

Y1 - 2021/9/1

N2 - Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.

AB - Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.

U2 - 10.1093/aje/kwab087

DO - 10.1093/aje/kwab087

M3 - Journal article

C2 - 33778840

VL - 190

SP - 1898

EP - 1907

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 9

ER -

ID: 64864764