A data-adaptive strategy for inverse weighted estimation of causal effects.
In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confou...
| Publicado en: | Health Services & Outcomes Research Methodology Vol. 14; no. 3; pp. 69 - 92 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=97810075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 97810075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13873741 OG0 jtl: Health Services & Outcomes Research Methodology issn: 13873741 maglogo: N pubinfo: dt: Sep2014 vid: 14 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 97810075 97810075 97810075 10.1007/s10742-014-0124-y 97810075 ppf: 69 ppct: 23 formats: tig: atl: A data-adaptive strategy for inverse weighted estimation of causal effects. aug: au: Zhu, Yeying Ghosh, Debashis Mitra, Nandita Mukherjee, Bhramar affil: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo N2L 3G1 Canada sug: subj: Causal Attribution Simulations Models, Statistical In Vitro Studies Data Analysis, Statistical Kaplan-Meier Estimator Survival Analysis Funding Source ab: In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we propose a weighted estimator combining parametric and nonparametric models. Some theoretical results regarding consistency of the procedure are given. Simulation studies are used to assess the performance of the newly proposed methods relative to existing methods, and a data analysis example from the Surveillance, Epidemiology and End Results database is presented. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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