Between-case incidence rate ratio for single case experimental designs with count outcomes: A Monte Carlo simulation with conditional and marginal models

Abstract

Abstract Calculating design-comparable effect sizes in single-case experimental designs (SCEDs) is essential for research synthesis to identify evidence-based practices that integrate findings from both SCEDs and group-based designs. The field has made significant progress in developing additive design-comparable effect sizes in recent years. This study aims to evaluate the statistical properties of a newly developed proportional estimator, the between-case incidence rate ratio (BC-IRR), for SCEDs with count outcomes. Two analytical frameworks are introduced for estimating BC-IRR: generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs). A large-scale Monte Carlo simulation is conducted to assess the bias of the point estimate, the standard error bias, mean squared error, and coverage rate. We also demonstrate the estimation of BC-IRR using GLMMs and GEEs with real data. Based on the simulation and demonstration results, we provide recommendations for applied researchers, discuss limitations, and outline directions for future research.

The authors' abstract, as published at the source. Research Synthesis Methods, 2026 · DOI ↗