A Simple Method for Estimating the Odds Ratio with Incomplete Paired Data

• Activity Number: 184
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Stephen Looney*+ and Kelly M. Miller
• Companies: Georgia Regents University and Georgia Regents University
• Keywords: matching ; discordant pairs ; cross-product ratio ; simulation ; bias ; mean squared error

Abstract

Matched case-control studies are commonly used to evaluate the association between the exposure to a risk factor and a disease. The odds ratio is typically used to quantify this association. Difficulties in estimating the true odds ratio arise, however, when the exposure status is unknown for one individual in a pair. In the case where the exposure status is known for both individuals in all pairs, the true odds ratio is estimated as the ratio of the counts in the discordant cells of the observed two-by-two table. In the case where all data are independent, the odds ratio is estimated using the cross-product ratio from the observed table. In this paper we suggest a simple method for estimating the odds ratio when the sample consists of a combination of paired and unpaired observations. This method uses a weighted average of the two odds ratio estimates described above. We compare our method to existing methods via simulation. The proposed method performs better than or as well as the competing methods under almost all conditions.