Model-based inference on average causal effect in observational clustered data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health,Health Policy
Link
http://link.springer.com/article/10.1007/s10742-019-00196-2/fulltext.html
Reference61 articles.
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3. Austin, P.C., Stuart, E.A.: Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat. Med. 34(28), 3661–3679 (2015)
4. Austin, P.C., Mamdani, M.M., Stukel, T.A., Anderson, G.M., Tu, J.V.: The use of the propensity score for estimating treatment effects: administrative versus clinical data. Stat. Med. 24(10), 1563–1578 (2005)
5. Austin, P.C., Grootendorst, P., Anderson, G.M.: A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a monte carlo study. Stat. Med. 26(4), 734–753 (2007)
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