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Smith S, Almirall D, Bauer M, Liebrecht C, Kilbourne A. (When) Is More Better? Comparative Effectiveness of External Vs External+Internal Facilitation on Site‐Level Uptake of a Collaborative Care Model in Community‐Based Practices That Are Slow to Adopt. Health Serv Res 2020. [DOI: 10.1111/1475-6773.13413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- S. Smith
- University of Michigan Ann Arbor MI United States
| | - D. Almirall
- University of Michigan Institute for Social Research Ann Arbor MI United States
| | - M. Bauer
- Harvard Medical School Boston MA United States
| | - C. Liebrecht
- University of Michigan Ann Arbor MI United States
| | - A. Kilbourne
- University of Michigan Ann Arbor MI United States
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Griffin BA, McCaffrey D, Almirall D, Setodji C, Burgette L. Chasing balance and other recommendations for improving nonparametric propensity score models. J Causal Inference 2017; 5:20150026. [PMID: 29503788 PMCID: PMC5830178 DOI: 10.1515/jci-2015-0026] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Abstract:In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance and inferences about causal effects: pursuing covariate balance between the treatment groups or tuning the propensity score model on the basis of a model fit criterion. Second, we examine how well GBM can handle irrelevant covariates that are included in the estimation model. We find that chasing balance rather than model fit when estimating propensity scores yielded better covariate balance and more accurate treatment effect estimates. Additionally, we find that adding irrelevant covariates to GBM increased imbalance and bias in the treatment effects. The findings from this paper have useful implications for other work focused on improving methods for estimating propensity scores.
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Affiliation(s)
- BA Griffin
- RAND Corporation. 1200 South Hayes Street. Arlington, VA 22202
| | - D McCaffrey
- Educational Testing Service (ETS). Ewing New Jersey
| | - D Almirall
- University of Michigan, Institute for Social Research. Ann Arbor, Michigan
| | - C Setodji
- RAND Corporation. 1200 South Hayes Street. Arlington, VA 22202
| | - L. Burgette
- RAND Corporation. 1200 South Hayes Street. Arlington, VA 22202
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