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Model-Based Multiple Imputation by Chained-Equations for Multilevel Data Below the Limit of Detection.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model-Based Multiple Imputation by Chained-Equations for Multilevel Data Below the Limit of Detection./
作者:	Xu, Peixin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	116 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29281989
ISBN:	9798802743737

Model-Based Multiple Imputation by Chained-Equations for Multilevel Data Below the Limit of Detection.
Xu, Peixin.

Model-Based Multiple Imputation by Chained-Equations for Multilevel Data Below the Limit of Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 116 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2022.

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

Missing data are a problem in many scientific studies and have been addressed by many statisticians in the past two decades. Especially, exposure assessment in epidemiological research could be difficult with low concentrations in biologic samples. This results in a left-truncated missingness due to the limit of detection, which applies to both cross-sectional and longitudinal studies. Recently, an increasing number of literatures have emphasized the importance of including analysis model information in imputation, which is referred to as model-based imputation. For example, in epidemiological study, the analysis model information can be interaction terms between exposures and time or gender. The author proposed a model-based multiple imputation algorithm by chained-equations for multilevel data subject to limit of detection. This method accommodates (1) the detection limits, (2) multilevel data structure, as well as (3) complicated analysis model information, which can be used for both multilevel regression models with level-1 outcome and multiple informant models with level-2 outcome. To the best of the author's knowledge, there is no existing literature that has simultaneously addressed all three aspects in one algorithm. Simulation studies showed that the proposed algorithm outplayed traditional imputation methods with multilevel regression models and remained competitive with multiple informant models. The author further applied this method to the HOME Study data imputation. HOME Study collects longitudinal environmental chemical exposures and investigates their impact on a cohort of pregnant women in Cincinnati, Ohio. Specifically, the author would like to study the association of urinary OPEs concentration and children's reading ability. The result shows that OPE concentrations are inversely associated with the Reading Composite score at 8 years without covariate adjustment.

ISBN: 9798802743737Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Missing data

Model-Based Multiple Imputation by Chained-Equations for Multilevel Data Below the Limit of Detection.
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500 $a Advisor: Xie, Changchun.
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506 $a This item must not be sold to any third party vendors.
520 $a Missing data are a problem in many scientific studies and have been addressed by many statisticians in the past two decades. Especially, exposure assessment in epidemiological research could be difficult with low concentrations in biologic samples. This results in a left-truncated missingness due to the limit of detection, which applies to both cross-sectional and longitudinal studies. Recently, an increasing number of literatures have emphasized the importance of including analysis model information in imputation, which is referred to as model-based imputation. For example, in epidemiological study, the analysis model information can be interaction terms between exposures and time or gender. The author proposed a model-based multiple imputation algorithm by chained-equations for multilevel data subject to limit of detection. This method accommodates (1) the detection limits, (2) multilevel data structure, as well as (3) complicated analysis model information, which can be used for both multilevel regression models with level-1 outcome and multiple informant models with level-2 outcome. To the best of the author's knowledge, there is no existing literature that has simultaneously addressed all three aspects in one algorithm. Simulation studies showed that the proposed algorithm outplayed traditional imputation methods with multilevel regression models and remained competitive with multiple informant models. The author further applied this method to the HOME Study data imputation. HOME Study collects longitudinal environmental chemical exposures and investigates their impact on a cohort of pregnant women in Cincinnati, Ohio. Specifically, the author would like to study the association of urinary OPEs concentration and children's reading ability. The result shows that OPE concentrations are inversely associated with the Reading Composite score at 8 years without covariate adjustment.
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