Estimation of Heterogeneity Variance Based on a Generalized "Q" Statistic in Meta-Analysis of Log-Odds-Ratio

Autor/inn/en	Kulinskaya, Elena; Hoaglin, David C.
Titel	Estimation of Heterogeneity Variance Based on a Generalized "Q" Statistic in Meta-Analysis of Log-Odds-Ratio
Quelle	In: Research Synthesis Methods, 14 (2023) 5, S.671-688 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kulinskaya, Elena) ORCID (Hoaglin, David C.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1647
Schlagwörter	Q Methodology; Statistical Analysis; Meta Analysis; Intervals; Statistical Bias; Sample Size; Probability; Error of Measurement; Accuracy + Suchen Sie Ihr Suchwort? Statistische Analyse; Meta-analysis; Metaanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Messfehler
Abstract	For estimation of heterogeneity variance T[superscript 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 statistic, Q[subscript 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[subscript 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 to each cell of the 2×2 table when one cell contains a zero count; we include a version that always adds 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[greater than or equal to]250 and the probability in the Control arm (piC) is 0.1, and when n[greater than or equal to]100 and piC is 0.2 or 0.5; for 0.1[less than or equal to]T[superscript 2][less than or equal to]1, all estimators have negative bias for small to medium sample sizes, but for larger sample sizes some of the new median-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 and another, when piC=0.2 or piC=0.5; and lack of balance between left and right coverage errors for small n and/or piC implies that the available approximations for the distributions of Q[subscript IV] and Q[subscript F] are accurate only for larger sample sizes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01