Testing for violations of the homogeneity needed for conditional logistic regression

R. H. Rieger and C. R. Weinberg

Journal of Applied Statistics, 2009, vol. 36, issue 10, 1147-1157

Abstract: In epidemiologic studies where the outcome is binary, the data often arise as clusters, as when siblings, friends or neighbors are used as matched controls in a case-control study. Conditional logistic regression (CLR) is typically used for such studies to estimate the odds ratio for an exposure of interest. However, CLR assumes the exposure coefficient is the same in every cluster, and CLR-based inference can be badly biased when homogeneity is violated. Existing methods for testing goodness-of-fit for CLR are not designed to detect such violations. Good alternative methods of analysis exist if one suspects there is heterogeneity across clusters. However, routine use of alternative robust approaches when there is no appreciable heterogeneity could cause loss of precision and be computationally difficult, particularly if the clusters are small. We propose a simple non-parametric test, the test of heterogeneous susceptibility (THS), to assess the assumption of homogeneity of a coefficient across clusters. The test is easy to apply and provides guidance as to the appropriate method of analysis. Simulations demonstrate that the THS has reasonable power to reveal violations of homogeneity. We illustrate by applying the THS to a study of periodontal disease.

Keywords: clustered binary outcomes; conditional logistic regression; heterogeneity of response (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760802638124 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:36:y:2009:i:10:p:1147-1157

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760802638124

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().