東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data Analysis and Multiple Imputatio...

Bailey, Brittney.

FindBook

Google Book

Amazon

博客來

Data Analysis and Multiple Imputation for Two-Level Nested Designs.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Analysis and Multiple Imputation for Two-Level Nested Designs./
作者:	Bailey, Brittney.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	153 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11011134
ISBN:	9780438649842

Data Analysis and Multiple Imputation for Two-Level Nested Designs.
Bailey, Brittney.

Data Analysis and Multiple Imputation for Two-Level Nested Designs. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 153 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2018.

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

This work examined methods to account for the clustering that occurs in nested clinical trial designs, which if ignored when analyzing or imputing data can result in invalid inference and misleading conclusions. We explored two distinct areas of research depending on whether the design is partially nested or fully nested. For partially nested designs, where subjects are nested within clusters in at least one study condition but subjects in another condition remain independent, we conducted a simulation study to identify the best method of analysis for binary outcomes. We compared four logistic regression models: standard logistic regression, logistic regression with generalized estimating equations, and mixed effects logistic regression with either random intercepts (LRI) or random slopes (LRC). For the logistic regression models with random effects, we additionally considered three estimation methods: penalized quasilikelihood, Laplace approximation, or adaptive Gaussian quadrature (AGQ). We showed that for partially nested designs with at least ten clusters and at least ten subjects per cluster, the LRC model estimated by AGQ produced least biased estimates of both the intervention effect and the intracluster correlation coefficient and better maintained the type I error rate. For fully nested designs, we explored methods for handling missing continuous outcomes in cluster randomized trials, the most common fully nested trial design. Random effects regression imputation has been the recommended approach to multiple imputation (MI) in cluster randomized trials, but we proposed three new semiparametric multiple imputation procedures that are more robust to misspecification of the imputation model. The new methods combined two predictive mean matching (PMM) models, one that ignores clustering and one that uses fixed effects for clusters. In the parametric setting, ignoring clustering in the imputation model results in underestimation of the MI variance, while using fixed effects for clusters results in overestimation of the MI variance. Our newly proposed PMM procedures leverage the estimated bias in the MI variance from these two imputation models in one of three ways: weighting the distance metric (PMM-dist), weighting the average of the final imputed values from the two procedures (PMM-avg), or performing a weighted draw from the final imputed values from the two procedures (PMM-draw). We conducted a simulation study to evaluate the performance of our proposed methods relative to established methods for multiple imputation of missing data. In addition to reducing the bias in the MI variance estimator relative to established methods, our newly proposed methods were more robust to model misspecification than even the random effects imputation methods. These results held whether the data were missing completely at random or at random.

ISBN: 9780438649842Subjects--Topical Terms:

1002712
Biostatistics.

Data Analysis and Multiple Imputation for Two-Level Nested Designs.
LDR:04070nmm a2200337 4500 001 2208491
005 20191021073449.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438649842
035 $a (MiAaPQ)AAI11011134
035 $a (MiAaPQ)OhioLINK:osu1531822703002162
035 $a AAI11011134
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bailey, Brittney. $3 3435526
245 1 0 $a Data Analysis and Multiple Imputation for Two-Level Nested Designs.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 153 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Shoben, Abigail;Andridge, Rebecca.
502 $a Thesis (Ph.D.)--The Ohio State University, 2018.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This work examined methods to account for the clustering that occurs in nested clinical trial designs, which if ignored when analyzing or imputing data can result in invalid inference and misleading conclusions. We explored two distinct areas of research depending on whether the design is partially nested or fully nested. For partially nested designs, where subjects are nested within clusters in at least one study condition but subjects in another condition remain independent, we conducted a simulation study to identify the best method of analysis for binary outcomes. We compared four logistic regression models: standard logistic regression, logistic regression with generalized estimating equations, and mixed effects logistic regression with either random intercepts (LRI) or random slopes (LRC). For the logistic regression models with random effects, we additionally considered three estimation methods: penalized quasilikelihood, Laplace approximation, or adaptive Gaussian quadrature (AGQ). We showed that for partially nested designs with at least ten clusters and at least ten subjects per cluster, the LRC model estimated by AGQ produced least biased estimates of both the intervention effect and the intracluster correlation coefficient and better maintained the type I error rate. For fully nested designs, we explored methods for handling missing continuous outcomes in cluster randomized trials, the most common fully nested trial design. Random effects regression imputation has been the recommended approach to multiple imputation (MI) in cluster randomized trials, but we proposed three new semiparametric multiple imputation procedures that are more robust to misspecification of the imputation model. The new methods combined two predictive mean matching (PMM) models, one that ignores clustering and one that uses fixed effects for clusters. In the parametric setting, ignoring clustering in the imputation model results in underestimation of the MI variance, while using fixed effects for clusters results in overestimation of the MI variance. Our newly proposed PMM procedures leverage the estimated bias in the MI variance from these two imputation models in one of three ways: weighting the distance metric (PMM-dist), weighting the average of the final imputed values from the two procedures (PMM-avg), or performing a weighted draw from the final imputed values from the two procedures (PMM-draw). We conducted a simulation study to evaluate the performance of our proposed methods relative to established methods for multiple imputation of missing data. In addition to reducing the bias in the MI variance estimator relative to established methods, our newly proposed methods were more robust to model misspecification than even the random effects imputation methods. These results held whether the data were missing completely at random or at random.
590 $a School code: 0168.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 $a The Ohio State University. $b Biostatistics. $3 3435527
773 0 $t Dissertations Abstracts International $g 80-06B.
790 $a 0168
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11011134