The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
The use of propensity scores and observational data to estimate randomized controlled trial generalizability bias
Abstract
Although randomized controlled trials are considered the ‘gold standard’ for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is unknown whether estimators of effect size are biased by excluding a portion of the target population from enrollment. We propose to use observational data to estimate the bias due to enrollment restrictions, which we term generalizability bias. In this paper, we introduce a class of estimators for the generalizability bias and use simulation to study its properties in the presence of non‐constant treatment effects. We find the surprising result that our estimators can be unbiased for the true generalizability bias even when all potentially confounding variables are not measured. In addition, our proposed doubly robust estimator performs well even for mis‐specified models. Copyright © 2013 John Wiley & Sons, Ltd.
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“Failure to do this has resulted in criticisms of prior research on barriers to treatment lacking external validity (Pressler & Kaizar, 2013).”
From Barriers of mental health treatment utilization among first‐year college students: First cross‐national results from the WHO World Mental Health International College Student Initiative (2019) · cites this paper as Pressler & Kaizar, 2013
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Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
External validity of randomised controlled trials: “To whom do the results of this trial apply?”
Rothwell, P. M. (2005) · The Lancet · doi:10.1016/s0140-6736(04)17670-8 · Cited by 2,544
Lunceford, J. K., & Davidian, M. (2004) · Statistics in Medicine · doi:10.1002/sim.1903 · Cited by 1,674
Bayesian Nonparametric Modeling for Causal Inference
Hill, J. L. (2011) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2010.08162 · Cited by 1,139
Van Spall, H. G. C., Toren, A., Kiss, A., & Fowler, R. A. (2007) · JAMA · doi:10.1001/jama.297.11.1233 · Cited by 1,055
Lee, P. Y. (2001) · JAMA · doi:10.1001/jama.286.6.708 · Cited by 849
Kang, J. D. Y., & Schafer, J. L. (2007) · Statistical Science · doi:10.1214/07-sts227 · Cited by 797
Multiple-bias modelling for analysis of observational data
Greenland, S. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.00349.x · Cited by 497
Research works printed but not linked: 2
- 10.1002/0470.023678.ch1b · DOI
- Cochran WG, 1997, Sampling Techniques
Cited by
Kennedy-Martin, T., Curtis, S., Faries, D., Robinson, S., & Johnston, J. (2015) · Trials · doi:10.1186/s13063-015-1023-4 · Cited by 878
Ebert, D. D., Mortier, P., Kaehlke, F., Bruffaerts, R., Baumeister, H., Auerbach, R. P., Alonso, J., Vilagut, G., Martínez, K. U., Lochner, C., et al. (2019) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1782 · Cited by 407
Assessing the generalizability of randomized trial results to target populations
Stuart, E. A., Bradshaw, C. P., & Leaf, P. J. (2015) · Prevention Science · doi:10.1007/s11121-014-0513-z · Cited by 296
Metadata
- Authors
- Pressler, T. R., & Kaizar, E. E.
- Affiliations
- Quantum Health Columbus OH 43235 U.S.A.
- Department of Statistics The Ohio State University Columbus OH 43210 U.S.A.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2013-04
- Volume
- 32
- Issue
- 20
- Pages
- 3552-3568
- ISSN
- 0277-6715
- 1097-0258
- PMC
- PMC3967586
- Cited by
- 32
- References
- 29
- Language
- en
- Licence
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 883 d)
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- Pressler, T. R., & Kaizar, E. E. (2013). The use of propensity scores and observational data to estimate randomized controlled trial generalizability bias. Statistics in Medicine. https://doi.org/10.1002/sim.5802
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
