Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk".


Journal

Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: England
ID NLM: 0370625

Informations de publication

Date de publication:
27 Mar 2024
Historique:
received: 31 03 2024
accepted: 01 05 2024
medline: 31 5 2024
pubmed: 31 5 2024
entrez: 31 5 2024
Statut: ppublish

Résumé

The five discussions of our paper provide several modeling alternatives, extensions, and generalizations that can potentially guide future research in meta-analysis. In this rejoinder, we briefly summarize and comment on some of those points.

Identifiants

pubmed: 38819314
pii: 7685813
doi: 10.1093/biomtc/ujae040
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.

Auteurs

Thanthirige Lakshika M Ruberu (TLM)

Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

Danielle Braun (D)

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.

Giovanni Parmigiani (G)

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.

Swati Biswas (S)

Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

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Classifications MeSH