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مجله‌ی بررسی‌ها 2008, 19(1): 119-144 Back to browse issues page
Compensating for Missing Data from Longitudinal Studies Using WinBUGS
Leili Tapak *, Omid Hamidi
Abstract:   (3359 Views)

Abstract. Missing data is a common problem in survey based research. There are many packages that compensate for missing data but few can easily compensate for missing longitudinal data. WinBUGS compensates for missing data using multiple imputation, and is able to incorporate longitudinal structure using random effects. We demonstrate the superiority of longitudinal imputation over cross-sectional imputation using WinBUGS. We use example data from the Australian Longitudinal Study on Woman’s Health. We give a SAS macro that uses WinBUGS to analyze longitudinal models with missing covariate data, and demonstrate its use in a longitudinal study of terminal cancer patients and their carers.

Keywords: missing data, multiple imputation, longitudinal data, WinBUGS, SAS.
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Type of Study: Research | Subject: General
Received: 2008/05/26 | Accepted: 2008/12/21 | Published: 2016/03/1
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Tapak L, Hamidi O. Compensating for Missing Data from Longitudinal Studies Using WinBUGS. مجله‌ی بررسی‌ها 2008; 19 (1) :119-144
URL: http://ijoss.srtc.ac.ir/article-1-118-en.html

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Volume 19, Issue 1 (9-2008) Back to browse issues page
مجله‌ی بررسی‌های آمار رسمی ایران Ijoss Iranian Journal of Official Statistics Studies
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