Calculating the Power of a Planned Individual Participant Data Meta-Analysis of Randomised Trials to Examine a Treatment-Covariate Interaction with a Time-to-Event Outcome

Autor/inn/en	Riley, Richard D.; Collins, Gary S.; Hattle, Miriam; Whittle, Rebecca; Ensor, Joie
Titel	Calculating the Power of a Planned Individual Participant Data Meta-Analysis of Randomised Trials to Examine a Treatment-Covariate Interaction with a Time-to-Event Outcome
Quelle	In: Research Synthesis Methods, 14 (2023) 5, S.718-730 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Riley, Richard D.) ORCID (Collins, Gary S.) ORCID (Hattle, Miriam) ORCID (Whittle, Rebecca) ORCID (Ensor, Joie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1650
Schlagwörter	Computation; Meta Analysis; Participant Characteristics; Data; Statistical Analysis; Randomized Controlled Trials; Sample Size; Statistical Distributions; Matrices + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Daten; Statistische Analyse; Wahrscheinlichkeitsverteilung; Matrizenrechnung
Abstract	Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment-covariate interactions at the participant-level (i.e., treatment effect modifiers). We focus on a time-to-event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial--the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common-effect assumption, and (v) calculating the power of the IPDMA based on a two-sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed. (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