Propensity score matching and complex surveys Article Swipe
Peter C. Austin
,
Nathaniel Jembere
,
Maria Chiu
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1177/0962280216658920
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1177/0962280216658920
Researchers are increasingly using complex population-based sample surveys to estimate the effects of treatments, exposures and interventions. In such analyses, statistical methods are essential to minimize the effect of confounding due to measured covariates, as treated subjects frequently differ from control subjects. Methods based on the propensity score are increasingly popular. Minimal research has been conducted on how to implement propensity score matching when using data from complex sample surveys. We used Monte Carlo simulations to examine two critical issues when implementing propensity score matching with such data. First, we examined how the propensity score model should be formulated. We considered three different formulations depending on whether or not a weighted regression model was used to estimate the propensity score and whether or not the survey weights were included in the propensity score model as an additional covariate. Second, we examined whether matched control subjects should retain their natural survey weight or whether they should inherit the survey weight of the treated subject to which they were matched. Our results were inconclusive with respect to which method of estimating the propensity score model was preferable. In general, greater balance in measured baseline covariates and decreased bias was observed when natural retained weights were used compared to when inherited weights were used. We also demonstrated that bootstrap-based methods performed well for estimating the variance of treatment effects when outcomes are binary. We illustrated the application of our methods by using the Canadian Community Health Survey to estimate the effect of educational attainment on lifetime prevalence of mood or anxiety disorders.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/0962280216658920
- https://journals.sagepub.com/doi/pdf/10.1177/0962280216658920
- OA Status
- hybrid
- Cited By
- 180
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2484604119
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2484604119Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/0962280216658920Digital Object Identifier
- Title
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Propensity score matching and complex surveysWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
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2016-07-26Full publication date if available
- Authors
-
Peter C. Austin, Nathaniel Jembere, Maria ChiuList of authors in order
- Landing page
-
https://doi.org/10.1177/0962280216658920Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1177/0962280216658920Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://journals.sagepub.com/doi/pdf/10.1177/0962280216658920Direct OA link when available
- Concepts
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Propensity score matching, Covariate, Statistics, Confounding, Matching (statistics), Sample size determination, Medicine, Population, Demography, Econometrics, Mathematics, Environmental health, SociologyTop concepts (fields/topics) attached by OpenAlex
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180Total citation count in OpenAlex
- Citations by year (recent)
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2025: 19, 2024: 29, 2023: 28, 2022: 29, 2021: 33Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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