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Measuring and estimating treatment effect on dichotomous outcome of a population
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektronik, matematik och naturvetenskap, Matematik.
Department of Sports Medicine, Chengdu Sport University, Chengdu, China.
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
2016 (Engelska)Ingår i: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 25, nr 5, s. 1779-1790Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In different studies for treatment effect on dichotomous outcome of a certain population, one uses different regression models, leading to different measures of the treatment effect. In observational studies, the common measures of the treatment effect are the conditional risk ratio based on a log-linear model and the conditional odds ratio based on a logistic model; in randomized trials, the common measures are the marginal risk difference based on a linear model, the marginal risk ratio based on a log-linear model, and the marginal odds ratio based on a logistic model. In this paper we express these measures in terms of the risk of a dichotomous outcome conditional on covariates and treatment, where the risk is described by a regression model. Therefore these measures do not explicitly depend on the regression model. As a result, we are able to use one regression model in one study to estimate all these measures by their maximum likelihood estimates. We show that these measures have causal interpretations and reflect different aspects of the same underlying treatment effect under the assumption of no unmeasured confounding covariate given observed covariates. We construct approximate distributions of the maximum likelihood estimates of these measures and then by using the approximate distributions we get confidence intervals for these measures. As an illustration, we estimate these measures for the effect of a triple therapy on eradication of Helicobacter pylori among Vietnamese children and are able to compare the treatment effect in this study with those in other studies.

Ort, förlag, år, upplaga, sidor
2016. Vol. 25, nr 5, s. 1779-1790
Nyckelord [en]
Collapsibility of treatment effect, Treatment effect measure, Maximum likelihood estimate, Regression model
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:hig:diva-14100DOI: 10.1177/0962280213502146ISI: 000385555400003PubMedID: 24004484Scopus ID: 2-s2.0-84989904002OAI: oai:DiVA.org:hig-14100DiVA, id: diva2:615360
Tillgänglig från: 2013-04-10 Skapad: 2013-04-10 Senast uppdaterad: 2018-12-03Bibliografiskt granskad

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