Separable effects for adherence.
Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal "per-protocol" estimands. However, when sustained use is challenging to satisfy in practice, the usefulne...
| Publicado en: | American Journal of Epidemiology Vol. 194; no. 4; pp. 1122 - 1131 |
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| Autores principales: | , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2025
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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=184348098&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184348098 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: Apr2025 vid: 194 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184348098 184348098 184348098 10.1093/aje/kwae277 184348098 ppf: 1122 ppct: 9 formats: tig: atl: Separable effects for adherence. aug: au: Wanis, Kerollos Nashat Stensrud, Mats Julius Sarvet, Aaron Leor affil: Department of Breast Surgical Oncology and Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States sug: subj: Medication Compliance Causal Attribution Epidemiology Models, Statistical Algorithms Treatment Outcomes Pharmacy and Pharmacology Study Design Health Services Research ab: Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal "per-protocol" estimands. However, when sustained use is challenging to satisfy in practice, the usefulness of these estimands can be limited. Here we propose a different class of estimands: separable effects for adherence. These estimands compare modified medications, holding fixed a component responsible for nonadherence. Under assumptions about treatment components' mechanisms of effect, a separable effects estimand can quantify the effectiveness of medication initiation strategies on an outcome of interest under the adherence mechanism of one of the medications. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators. This article is part of a Special Collection on Pharmacoepidemiology. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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