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MISL: Multiple Imputation by Super Learning and Its Applications in Population Health.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	MISL: Multiple Imputation by Super Learning and Its Applications in Population Health./
作者:	Carpenito, Thomas Michael.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	188 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Public health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29253389
ISBN:	9798834000662

MISL: Multiple Imputation by Super Learning and Its Applications in Population Health.
Carpenito, Thomas Michael.

MISL: Multiple Imputation by Super Learning and Its Applications in Population Health. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 188 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Northeastern University, 2022.

This item must not be sold to any third party vendors.

BackgroundMissing data are an artifact of almost all health research. While advancements in machine learning have greatly increased our capacity in forming predictive models, their practicality is far less understood when generating imputations used for inference. We investigate a plausible imputation procedure and examine its use in various applications of population health.MethodsWe develop Multiple Imputation by Super Learning (MISL), a novel open-source-ensemble-based-multiple-imputation procedure and evaluate its use in several different simulation environments. We then use MISL to investigate how the quality of imputation impacts variable selection among correlated environmental mixtures of exposures and their association with preterm birth in mothers from the Puerto Rico Test site for Exploring Contamination Threats cohort study. Finally, we again use MISL to better understand the national distribution of firearm injury intent using data from the National Electronic Injury Surveillance System - Firearm Injury Surveillance Study.ResultsWe observe that MISL outperforms current leading methods for multiple imputation both in the presence and absence of interaction effects. Next, we observe that while imputations have little impact on the variable selection process, caution is advised to use a variety of different imputation methods to generate, rather than confirm, hypotheses in the presence of missing data and variable selection. Finally, with MISL, we discover the distribution of assault type incidents over time has remained constant, rather than what was previously believed to be steadily increasing.ConclusionsMISL is a demonstration of how classical statistical methodology can be updated to align with advancements in modern machine learning. The algorithm is adaptable and can be used in a wide variety of different situations, especially population health research.

ISBN: 9798834000662Subjects--Topical Terms:

534748
Public health.
Subjects--Index Terms:

Firearm injuries

MISL: Multiple Imputation by Super Learning and Its Applications in Population Health.
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506 $a This item must not be sold to any third party vendors.
520 $a BackgroundMissing data are an artifact of almost all health research. While advancements in machine learning have greatly increased our capacity in forming predictive models, their practicality is far less understood when generating imputations used for inference. We investigate a plausible imputation procedure and examine its use in various applications of population health.MethodsWe develop Multiple Imputation by Super Learning (MISL), a novel open-source-ensemble-based-multiple-imputation procedure and evaluate its use in several different simulation environments. We then use MISL to investigate how the quality of imputation impacts variable selection among correlated environmental mixtures of exposures and their association with preterm birth in mothers from the Puerto Rico Test site for Exploring Contamination Threats cohort study. Finally, we again use MISL to better understand the national distribution of firearm injury intent using data from the National Electronic Injury Surveillance System - Firearm Injury Surveillance Study.ResultsWe observe that MISL outperforms current leading methods for multiple imputation both in the presence and absence of interaction effects. Next, we observe that while imputations have little impact on the variable selection process, caution is advised to use a variety of different imputation methods to generate, rather than confirm, hypotheses in the presence of missing data and variable selection. Finally, with MISL, we discover the distribution of assault type incidents over time has remained constant, rather than what was previously believed to be steadily increasing.ConclusionsMISL is a demonstration of how classical statistical methodology can be updated to align with advancements in modern machine learning. The algorithm is adaptable and can be used in a wide variety of different situations, especially population health research.
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