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Comparison of two SAS procedures for...

Bursac, Zoran.

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Comparison of two SAS procedures for longitudinal data with evaluation of SAS experimental procedure MI (multiple imputations).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Comparison of two SAS procedures for longitudinal data with evaluation of SAS experimental procedure MI (multiple imputations)./
作者:	Bursac, Zoran.
面頁冊數:	160 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1031.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3083746

Comparison of two SAS procedures for longitudinal data with evaluation of SAS experimental procedure MI (multiple imputations).
Bursac, Zoran.

Comparison of two SAS procedures for longitudinal data with evaluation of SAS experimental procedure MI (multiple imputations). - 160 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1031.

Thesis (Ph.D.)--The University of Oklahoma Health Sciences Center, 2003.

In this simulation study we compared the performance of SAS procedures MIXED and GENMOD in fitting multiple-population models to longitudinal growth data with missing values. Comparison was done under 40 simulated conditions that included two different percentages of missing data, high and medium correlation structures, four and seven repeated time points and 5 levels of data completeness. Completeness levels included complete data set, data missing at random (MAR), MAR with imputed values, non-ignorable missing data mechanism (NI) and NI with imputed values. Data imputation was performed using SAS experimental procedure MI. We found that PROC MIXED performed with higher accuracy than PROC GENMOD under more than 95% of experimental conditions and that multiple data imputation helped improve model components only for NI missing data mechanism.Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Comparison of two SAS procedures for longitudinal data with evaluation of SAS experimental procedure MI (multiple imputations).
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520 $a In this simulation study we compared the performance of SAS procedures MIXED and GENMOD in fitting multiple-population models to longitudinal growth data with missing values. Comparison was done under 40 simulated conditions that included two different percentages of missing data, high and medium correlation structures, four and seven repeated time points and 5 levels of data completeness. Completeness levels included complete data set, data missing at random (MAR), MAR with imputed values, non-ignorable missing data mechanism (NI) and NI with imputed values. Data imputation was performed using SAS experimental procedure MI. We found that PROC MIXED performed with higher accuracy than PROC GENMOD under more than 95% of experimental conditions and that multiple data imputation helped improve model components only for NI missing data mechanism.
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