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

Blagus, Rok (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: statistics, linear models, bias, bias correction, Cox proportional hazards model, data augmentation, logistic regression model, relative risk estimation
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: MF - Medicinska fakulteta ; FŠ - Fakulteta za šport
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 17 str.
• Številčenje: Vol. 65, iss. 4, art. 2200133
• PID: 20.500.12556/RUL-147377
• UDK: 311
• ISSN pri članku: 1521-4036
• DOI: 10.1002/bimj.202200133
• COBISS.SI-ID: 141664515
• Datum objave v RUL: 03.07.2023

Gradivo je del revije

• Naslov: Biometrical journal
• Skrajšan naslov: Biom. j.
• Založnik: Wiley
• ISSN: 1521-4036
• COBISS.SI-ID: 518615833

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: statistika, linearni modeli, pristranskost

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: N1-0035
• Naslov: Izboljšanje napovedovanja redkih dogodkov

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P3-0154
• Naslov: Metodologija za analizo podatkov v medicini