Confidence intervals for intraclass correlation coefficients in a nonlinear dose–response meta-analysis

This work is motivated by a meta-analysis case study on antipsychotic medications. The Michaelis–Menten curve is employed to model the nonlinear relationship between the dose and inline image receptor occupancy across multiple studies. An intraclass correlation coefficient (ICC) is used to quantify the heterogeneity across studies. To interpret the size of heterogeneity, an accurate estimate of ICC and its confidence interval is required. The goal is to apply a recently proposed generic beta-approach for construction the confidence intervals on ICCs for linear mixed effects models to nonlinear mixed effects models using four estimation methods. These estimation methods are the maximum likelihood, second-order generalized estimating equations and two two-step procedures. The beta-approach is compared with a large sample normal approximation (delta method) and bootstrapping. The confidence intervals based on the delta method and the nonparametric percentile bootstrap with various resampling strategies failed in our settings. The beta-approach demonstrates good coverages with both two-step estimation methods and consequently, it is recommended for the computation of confidence interval for ICCs in nonlinear mixed effects models for small studies. Keywords: Beta distribution;Maximum likelihood;Nonlinear regression;Restricted profile likelihood;Variance componen