Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes.

ABSTRACT: Researchers often use outcome-dependent sampling to study the exposure-outcome association. The case-control study is a widely used example of outcome-dependent sampling when the outcome is binary. When the outcome is ordinal, standard ordinal regression models generally produce biased coefficients when the sampling fractions depend on the values of the outcome variable. To address this problem, we studied the performance of survey-weighted ordinal regression models with weights inversely proportional to the sampling fractions. Through an extensive simulation study, we compared the performance of four ordinal regression models (SM: stereotype model; AC: adjacent-category logit model; CR: continuation-ratio logit model; and CM: cumulative logit model), with and without sampling weights unde

SUBMITTER: Mitani AA 

PROVIDER: S-EPMC11577697 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature