Estimation of heterogeneity variance based on a generalized Q statistic in meta ‐analysis of log‐odds‐ratio

AbstractFor estimation of heterogeneity variance τ2$$ {\tau}^2 $$ in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized Q$$ Q $$ statistic, QF$$ {Q}_F $$, in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators based on the inverse-variance-weights version of Q$$ Q $$, QIV.$$ {Q}_{IV}. $$ In an extensive simulation, we studied the bias (including median bias) of the point estimators and the coverage (including left and right coverage error) of the confidence intervals. Most estimators add 0.5$$ 0.5 $$ to each cell of the 2×2$$ 2\times 2 $$ table when one cell contains a zero count; we include a version that always adds 0.5$$ 0.5 $$. The results show that: two of the new point estimators and two of the familiar point estimators are almost unbiased when the total sample size n≥250$$ n\ge 250 $$ and the probability in the Control arm (piC$$ {p}_{iC} $$) is 0.1, and when n≥100$$ n\ge 100 $$ and piC$$ {p}_{iC} $$ is 0.2 or 0.5; for 0.1≤τ2≤1$$ 0.1\le {\tau}^2\le 1 $$, all estimators have negative bias for small to medium sample sizes, but for larger sample sizes some of the new m edian-unbiased estimators are almost median-unbiased; choices of interval estimators depend on values of parameters, but one of the new estimators is reasonable when piC=0.1$$ {p}_{iC}=0.1 $$ and another, when piC=0.2$$ {p}_{iC}=0.2 $$ or piC=0.5$$ {...

Source: Research Synthesis Methods - June 29, 2023 Category: Chemistry Authors: Elena Kulinskaya, David C. Hoaglin Tags: RESEARCH ARTICLE Source Type: research