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Avoiding the Redundant Effect on Reg...

Tamegnon, Monelle.

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Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model./
Author:	Tamegnon, Monelle.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	293 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-01, Section: B.
Contained By:	Dissertations Abstracts International80-01B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744799
ISBN:	9780438151628

Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model.
Tamegnon, Monelle.

Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 293 p.

Source: Dissertations Abstracts International, Volume: 80-01, Section: B.

Thesis (Ph.D.)--The University of Iowa, 2018.

This item is not available from ProQuest Dissertations & Theses.

Imputation is one well recognized method for handling missing data. Multiple imputation provides a framework for imputing missing data that incorporate uncertainty about the imputations at the analysis stage. An important factor to consider when performing multiple imputation is the imputation model. In particular, a careful choice of the covariates to include in the model is crucial. The current recommendation by several authors in the literature (Van Buren, 2012; Moons et al., 2006, Little and Rubin, 2002) is to include all variables that will appear in the analytical model including the outcome as covariates in the imputation model. When the goal of the analysis is to explore the relationship between the outcome and the variable with missing data (the target variable), this recommendation seems questionable. Should we make use of the outcome to fill-in the target variable missing observations and then use these filled-in observations along with the observed data on the target variable to explore the relationship of the target variable with the outcome? We believe that this approach is circular. Instead, we have designed multiple imputation approaches rooted in machines learning techniques that avoid the use of the outcome at the imputation stage and maintain reasonable inferential properties. We also compare our approaches performances to currently available methods.

ISBN: 9780438151628Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Clustering

Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model.
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245 1 0 $a Avoiding the Redundant Effect on Regression Analyses of Including an Outcome in the Imputation Model.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 293 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Jones, Michael;Zamba, Gideon.
502 $a Thesis (Ph.D.)--The University of Iowa, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Imputation is one well recognized method for handling missing data. Multiple imputation provides a framework for imputing missing data that incorporate uncertainty about the imputations at the analysis stage. An important factor to consider when performing multiple imputation is the imputation model. In particular, a careful choice of the covariates to include in the model is crucial. The current recommendation by several authors in the literature (Van Buren, 2012; Moons et al., 2006, Little and Rubin, 2002) is to include all variables that will appear in the analytical model including the outcome as covariates in the imputation model. When the goal of the analysis is to explore the relationship between the outcome and the variable with missing data (the target variable), this recommendation seems questionable. Should we make use of the outcome to fill-in the target variable missing observations and then use these filled-in observations along with the observed data on the target variable to explore the relationship of the target variable with the outcome? We believe that this approach is circular. Instead, we have designed multiple imputation approaches rooted in machines learning techniques that avoid the use of the outcome at the imputation stage and maintain reasonable inferential properties. We also compare our approaches performances to currently available methods.
590 $a School code: 0096.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Public health. $3 534748
653 $a Clustering
653 $a Imputation model
653 $a Multiple imputation
653 $a Penalized splines
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710 2 $a The University of Iowa. $b Biostatistics. $3 3191866
773 0 $t Dissertations Abstracts International $g 80-01B.
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744799