Efficient and robust methods for causally interpretable meta‐analysis: Transporting inferences from multiple randomized trials to a target population-Reference-Cited by-同舟云学术

Efficient and robust methods for causally interpretable meta‐analysis: Transporting inferences from multiple randomized trials to a target population

Published:2023-02-07 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Dahabreh Issa J.¹²³^ORCID,Robertson Sarah E.¹²^ORCID,Petito Lucia C.⁴^ORCID,Hernán Miguel A.¹²⁵^ORCID,Steingrimsson Jon A.⁶^ORCID

Affiliation:

1. CAUSALab Harvard T.H. Chan School of Public Health Boston Massachusetts USA

2. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA

3. Department of Biostatistics Harvard T.H. Chan School of Public Health Boston Massachusetts USA

4. Department of Preventative Medicine, Feinberg School of Medicine Northwestern University Chicago Illinois USA

5. Harvard‐MIT Division of Health Sciences and Technology Boston Massachusetts USA

6. Department of Biostatistics, School of Public Health Brown University Providence Rhode Island USA

Funder

Agency for Healthcare Research and Quality

National Institutes of Health

Patient-Centered Outcomes Research Institute

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13716

Reference50 articles.

1. Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention

2. On the Semi-Parametric Efficiency of Logistic Regression under Case-Control Sampling

3. Weighted Likelihood for Semiparametric Models and Two-phase Stratified Samples, with Application to Cox Regression

4. The statistical performance of matching-adjusted indirect comparisons: Estimating treatment effects with aggregate external control data

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