Integrating Meta-Analysis Into a Specific Study (InMASS) for Estimating the Target-Population Average Treatment Effect.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Integrating Meta-Analysis Into a Specific Study (InMASS) for Estimating the Target-Population Average Treatment Effect.
Συγγραφείς:	Hanada K; Department of Biostatistics, Faculty of Medicine, Wakayama Medical University, Kimiidera, Wakayama, Japan., Kojima M; Department of Data Science for Business Innovation, Chuo University, Hachioji, Tokyo, Japan.
Πηγή:	Biometrical journal. Biometrische Zeitschrift [Biom J] 2026 Oct; Vol. 68 (5), pp. e70169.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Wiley-VCH Verlag GmbH & Co. KGaA Country of Publication: Germany NLM ID: 7708048 Publication Model: Print Cited Medium: Internet ISSN: 1521-4036 (Electronic) Linking ISSN: 03233847 NLM ISO Abbreviation: Biom J Subsets: MEDLINE
Imprint Name(s):	Publication: <2005-> : Weinheim : Wiley-VCH Verlag GmbH & Co. KGaA Original Publication: Weinheim, Wiley-VCH Verlag GmbH & Co. KGaA
Ιατρικοί όροι (MeSH):	Biometry/methods , Meta-Analysis as Topic, Humans ; Treatment Outcome ; Randomized Controlled Trials as Topic
Περίληψη:	Randomized controlled trials remain the benchmark for estimating treatment effects, yet practical constraints often restrict sample sizes in a prespecified target population. In such settings, evidence from completed trials is frequently available only in aggregate form, which precludes direct individual-level integration. We propose InMASS, an inferential framework for estimating the target-population average treatment effect (TATE) for a prespecified target population by integrating aggregate evidence from multiple external trials. InMASS reconstructs pseudo individual-level information from routinely reported summary statistics via a meta-regression model and transports this information to the target population using density-ratio weighting. Under a weak covariate shift assumption, we show that reconstructing second-order moments is sufficient for consistent and asymptotically normal estimation of the target-population TATE, irrespective of the covariate distribution in external trials. Importantly, efficiency gains depend on the total amount of external information rather than on increasing the size of the target trial. Simulation studies and a real-data application demonstrate that InMASS materially improves precision and increases statistical power relative to analyses based solely on the target trial, including settings with unbalanced allocation or single-arm designs. These findings underscore the practical utility of InMASS for target-specific inference when individual-level data are limited. (© 2026 Wiley‐VCH GmbH.)
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Grant Information:	JP24K23862 JSPS; JP26K21184 JSPS; JP26K21185 JSPS
Contributed Indexing:	Keywords: meta‐analysis; target‐population average treatment effect; transfer learning; weighted linear regression
Entry Date(s):	Date Created: 20260907 Date Completed: 20260907 Latest Revision: 20260914
Update Code:	20260914
PubMed Central ID:	PMC13548349
DOI:	10.1002/bimj.70169
PMID:	42703888
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1521-4036
DOI:	10.1002/bimj.70169