Evaluation of Statistical Methods Used to Meta-Analyse Results from Interrupted Time Series Studies: A Simulation Study

Autor/inn/en	Korevaar, Elizabeth; Turner, Simon L.; Forbes, Andrew B.; Karahalios, Amalia; Taljaard, Monica; McKenzie, Joanne E.
Titel	Evaluation of Statistical Methods Used to Meta-Analyse Results from Interrupted Time Series Studies: A Simulation Study
Quelle	In: Research Synthesis Methods, 14 (2023) 6, S.882-902 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Korevaar, Elizabeth) ORCID (Turner, Simon L.) ORCID (Forbes, Andrew B.) ORCID (Karahalios, Amalia) ORCID (Taljaard, Monica) ORCID (McKenzie, Joanne E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1669
Schlagwörter	Meta Analysis; Maximum Likelihood Statistics; Factor Analysis; Public Health; Policy Formation; Simulation; Error of Measurement; Accuracy + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Faktorenanalyse; Gesundheitswesen; Politische Betätigung; Simulation program; Simulationsprogramm; Messfehler
Abstract	Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01