Multiwave validation sampling for error‐prone electronic health records-Reference-Cited by-同舟云学术

Multiwave validation sampling for error‐prone electronic health records

Published:2022-07-11 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Shepherd Bryan E.¹^ORCID,Han Kyunghee²^ORCID,Chen Tong³,Bian Aihua¹,Pugh Shannon⁴,Duda Stephany N.⁵,Lumley Thomas³^ORCID,Heerman William J.⁶^ORCID,Shaw Pamela A.⁷^ORCID

Affiliation:

1. Department of Biostatistics Vanderbilt University Nashville Tennessee

2. Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Chicago

3. Department of Statistics University of Auckland Auckland New Zealand

4. Department of Emergency Medicine Vanderbilt University Medical Center Nashville Tennessee

5. Department of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee

6. Department of Pediatrics Vanderbilt University Medical Center Nashville Tennessee

7. Biostatistics Unit Kaiser Permanente Washington Health Research Institute Seattle Washington

Funder

Patient-Centered Outcomes Research Institute

National Institute of Allergy and Infectious Diseases

National Center for Advancing Translational Sciences

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.13713

Reference33 articles.

1. Two‐phase sampling designs for data validation in settings with covariate measurement error and continuous outcome

2. Design and analysis of two‐phase studies with binary outcome applied to Wilms tumour prognosis

3. Improved Horvitz–Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology

4. Optimal multiwave sampling for regression modeling in two‐phase designs

5. Optimal sampling for design‐based estimators of regression models

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