Decreased Susceptibility of Marginal Odds Ratios to Finite-sample Bias.

Szczegóły
Abstrakt

Tytuł:: Decreased Susceptibility of Marginal Odds Ratios to Finite-sample Bias.
Autorzy:: Ross RK; From the Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC.
Cole SR
Richardson DB
Źródło:: Epidemiology (Cambridge, Mass.) [Epidemiology] 2021 Sep 01; Vol. 32 (5), pp. 648-652.
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural
Język:: English
Imprint Name(s):: Publication: <2000>- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
MeSH Terms:: Odds Ratio*
Bias ; Computer Simulation ; Humans ; Logistic Models ; Probability
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Grant Information:: R01 AG056479 United States AG NIA NIH HHS; R01 AI157758 United States AI NIAID NIH HHS; R01 CA242852 United States CA NCI NIH HHS; T32 HD052468 United States HD NICHD NIH HHS
Entry Date(s):: Date Created: 20210518 Date Completed: 20210929 Latest Revision: 20230908
Update Code:: 20240104
PubMed Central ID:: PMC8338772
DOI:: 10.1097/EDE.0000000000001370
PMID:: 34001751
: Czasopismo naukowe

Parameters representing adjusted treatment effects may be defined marginally or conditionally on covariates. The choice between a marginal or covariate-conditional parameter should be driven by the study question. However, an unappreciated benefit of marginal estimators is a reduction in susceptibility to finite-sample bias relative to the unpenalized maximum likelihood estimator of the covariate-conditional odds ratio (OR). Using simulation, we compare the finite-sample bias of different marginal and conditional estimators of the OR. We simulated a logistic model to have 15 events per parameter and two events per parameter. We estimated the covariate-conditional OR by maximum likelihood with and without Firth's penalization. We used three estimators of the marginal OR: g-computation, inverse probability of treatment weighting, and augmented inverse probability of treatment weighting. At 15 events per parameter, as expected, all estimators were effectively unbiased. At two events per parameter, the unpenalized covariate-conditional estimator was notably biased but penalized covariate-conditional and marginal estimators exhibited minimal bias.
Competing Interests: The authors report no conflicts of interest.
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