Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

Blagus, Rok (author)

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.

• Language: English
• Keywords: statistics, linear models, bias, bias correction, Cox proportional hazards model, data augmentation, logistic regression model, relative risk estimation
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: MF - Faculty of Medicine; FŠ - Faculty of Sport
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 17 str.
• Numbering: Vol. 65, iss. 4, art. 2200133
• PID: 20.500.12556/RUL-147377
• UDC: 311
• ISSN on article: 1521-4036
• DOI: 10.1002/bimj.202200133
• COBISS.SI-ID: 141664515
• Publication date in RUL: 03.07.2023

Record is a part of a journal

• Title: Biometrical journal
• Shortened title: Biom. j.
• Publisher: Wiley
• ISSN: 1521-4036
• COBISS.SI-ID: 518615833

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: statistika, linearni modeli, pristranskost

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: N1-0035
• Name: Izboljšanje napovedovanja redkih dogodkov

• Funder: ARRS - Slovenian Research Agency
• Project number: P3-0154
• Name: Metodologija za analizo podatkov v medicini