A Bayesian model for combining standardized mean differences and odds ratios in the same meta-analysis.

ABSTRACT: In meta-analysis practice, researchers frequently face studies that report the same outcome differently, such as a continuous variable (e.g., scores for rating depression) or a binary variable (e.g., counts of patients with depression dichotomized by certain latent and unreported depression scores). For combining these two types of studies in the same analysis, a simple conversion method has been widely used to handle standardized mean differences (SMDs) and odds ratios (ORs). This conventional method uses a linear function connecting the SMD and log OR; it assumes logistic distributions for (latent) continuous measures. However, the normality assumption is more commonly used for continuous measures, and the conventional method may be inaccurate when effect sizes are large or cutoff values

SUBMITTER: Jing Y 

PROVIDER: S-EPMC9895126 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature