Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies-Reference-Cited by-同舟云学术

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Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies

Published:2022-10-19 Issue: Volume: Page:1-11
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Chen Kan¹,Heng Siyu²,Long Qi³^ORCID,Zhang Bo⁴^ORCID

Affiliation:

1. Graduate Group of Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA;

2. Department of Biostatistics, School of Global Public Health, New York University, New York City, NY;

3. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA;

4. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA

Funder

National Institutes of Health

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2022.2116447

Reference43 articles.

1. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

2. Bridging observational studies and randomized experiments by embedding the former in the latter

3. Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets

4. Evaluating a Key Instrumental Variable Assumption Using Randomization Tests

5. Evaluation of Right-Heart Catheterization in the Critically Ill Patient without Acute Myocardial Infarction

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes;Biometrika;2024-04-13

2. The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials;Statistical Communications in Infectious Diseases;2024-01-01

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