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:: Volume 21, Issue 1 (9-2010) ::
مجله‌ی بررسی‌ها 2010, 21(1): 109-136 Back to browse issues page
Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression
Shirin Golchi *, Sanaz Mohandesi
Abstract:   (2171 Views)

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks of fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the expectation-maximization (EM) method of weights and multiple imputations (MI). Sample data are drawn randomly from a population with known characteristics. Missing data on covariates are simulated under two conditions: missing completely at random and missing at random with different missing rates. A logistic regression model was fit to each sample using either the EM or MI approach. The performance of these two approaches is compared on four criteria: bias, efficiency, coverage, and rejection rate. Results generally favored MI over EM. Practical issues such as implementation, inclusion of continuous covariates, and interactions between covariates are discussed. 

Keywords: For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks of fill the void by comparing two approaches
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Type of Study: Research | Subject: General
Received: 2009/05/15 | Accepted: 2009/11/23 | Published: 2016/01/9
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Golchi S, Mohandesi S. Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression. مجله‌ی بررسی‌ها. 2010; 21 (1) :109-136
URL: http://ijoss.srtc.ac.ir/article-1-70-en.html


Volume 21, Issue 1 (9-2010) Back to browse issues page
مجله‌ی بررسی‌های آمار رسمی ایران (علمی - ترویجی) Ijoss Iranian Journal of Official Statistics Studies
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