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Using the Multilevel Generalized Mix...

Wilson, Rebbecca P.

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Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry./
Author:	Wilson, Rebbecca P.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	111 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790311
ISBN:	9780438064898

Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry.
Wilson, Rebbecca P.

Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 111 p.

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 2018.

Accelerometers provide objective measures of physical activity and sedentary behavior. Typically, the device is worn for one week during all waking hours to measure physical activity counts for a period time (e.g., minute). A challenge is accounting for time when the accelerometer is not worn which can bias assessments of physical activity and sedentary behavior. To circumvent this, researchers will limit analysis to participants with a minimum number of adherent days with sufficient wear time and average these days. Excluding accelerometer nonwear assumes missing completely at random (MCAR); yet, sedentary behavior and physical activity are related to nonwear.

ISBN: 9780438064898Subjects--Topical Terms:

1002712
Biostatistics.

Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry.
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100 1 $a Wilson, Rebbecca P. $3 3352418
245 1 0 $a Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 111 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Advisers: Shrikant Bangdiwala; Daniela Sotres-Alvarez.
502 $a Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 2018.
520 $a Accelerometers provide objective measures of physical activity and sedentary behavior. Typically, the device is worn for one week during all waking hours to measure physical activity counts for a period time (e.g., minute). A challenge is accounting for time when the accelerometer is not worn which can bias assessments of physical activity and sedentary behavior. To circumvent this, researchers will limit analysis to participants with a minimum number of adherent days with sufficient wear time and average these days. Excluding accelerometer nonwear assumes missing completely at random (MCAR); yet, sedentary behavior and physical activity are related to nonwear.
520 $a We proposed imputing counts/min for nonwear using a multilevel generalized mixed model (MGMM) and account for multivariate counts under a complex survey design. Using data from the Hispanic Community Health Study/ Study of Latinos (2008 -- 2011), and the HCHS/SOL Youth Study (2012 -- 2014), we utilize these methods to: (1) compare accelerometer wear and nonwear data in intervals of the day, (2) determine an association between accelerometer average counts/min and BMI, and (3) evaluate the different models using percent relative bias in simulated data.
520 $a Our results showed that (1) accelerometer average counts/min were higher for wear versus nonwear segments in an interval, thus, we concluded that the MCAR assumption of the ad hoc approach was not tenable. (2) The MGMM indicated a clear association between average count/min and BMI when missing values were imputed at the interval level. (3) The percent relative bias did not show enough evidence to support a smaller value for MGMM imputation evaluation models that were concordant with MGMM generated data. We concluded that imputing missing values at the smallest unit possible (e.g., interval), and then aggregating at the participant level, may reduce the potential for making a type 2 error.
520 $a Further research in this area will greatly improve physical activity guidelines established using accelerometer data that better accounts for nonwear time.
590 $a School code: 0153.
650 4 $a Biostatistics. $3 1002712
690 $a 0308
710 2 $a The University of North Carolina at Chapel Hill. $b Biostatistics. $3 1023527
773 0 $t Dissertation Abstracts International $g 79-10B(E).
790 $a 0153
791 $a Dr.P.H.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790311