Frontiers reaches 6.4 on Journal Impact Factors

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Public Health | doi: 10.3389/fpubh.2018.00057

Unconditional or conditional logistic regression model for age-matched case-control data?

 Chia-Ling Kuo1, 2*, Delian Duan1, 2 and James Grady1, 2
  • 1Department of Community Medicine and Health Care, University of Connecticut Health Center, United States
  • 2Connecticut Institute for Clinical and Translational Science, United States

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose matching data and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case-control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.

Keywords: Frequency matching, Individual matching, Sparse data problem, Loose matching, precision in estimates and tests, width of 95% confidence interval

Received: 11 Nov 2017; Accepted: 14 Feb 2018.

Edited by:

Zhulin He, National Institute of Statistical Sciences, United States

Reviewed by:

Yingchen Wang, University of North Carolina at Greensboro, United States
Yesilda Balavarca, Nationales Centrum für Tumorerkrankungen (NCT), Germany  

Copyright: © 2018 Kuo, Duan and Grady. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Chia-Ling Kuo, University of Connecticut Health Center, Department of Community Medicine and Health Care, Farmington, United States,