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

ABSTRACT: 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, inve

SUBMITTER: Ross RK 

PROVIDER: S-EPMC8338772 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature