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Balancing vs modeling approaches to weighting in practice

Published:2020-09-03 Issue:24 Volume:39 Page:3227-3254
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Chattopadhyay Ambarish¹,Hase Christopher H.¹,Zubizarreta José R.¹²³^ORCID

Affiliation:

1. Department of Statistics Harvard University Cambridge Massachusetts USA

2. Department of Health Care Policy, Harvard Medical School Harvard University Boston Massachusetts USA

3. Department of Biostatistics Harvard School of Public Health, Harvard University Boston Massachusetts USA

Funder

Alfred P. Sloan Foundation

National Institutes of Health

Patient-Centered Outcomes Research Institute

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8659

Reference67 articles.

1. For objective causal inference, design trumps analysis

2. Matching to Remove Bias in Observational Studies

3. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

4. Matching Methods for Causal Inference: A Review and a Look Forward

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

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2. A Two‐Step Framework for Validating Causal Effect Estimates;Pharmacoepidemiology and Drug Safety;2024-09

3. Minimally invasive colorectal cancer surgery: an observational study of medicare advantage and fee-for-service beneficiaries;Surgical Endoscopy;2024-08-19

4. Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation;Statistical Methods in Medical Research;2024-07-25

5. Optimal refinement of strata to balance covariates;Biometrics;2024-07-01

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