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      Causal inference with multi-state models - estimands and estimators of the population-attributable fraction

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          Abstract

          The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by a proper differentiation between estimands and estimators as well as causal effect measures and measures of association.

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          A definition of causal effect for epidemiological research.

          M A Hernán (2004)
          Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described-the redundant expression "causal effect" is used throughout the article to avoid confusion with a common use of "effect" meaning simply statistical association-and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.
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            The estimation and interpretation of attributable risk in health research.

            S Walter (1976)
            Various measures of attributable risk are discussed together with a rationale for their use as an alternative to relative risk in health research. Methods of estimation are presented for use with three important kinds of epidemiological study design with one dichotomous risk factor for a dichotomous disease outcome; the study designs are then compared with respect to efficiency. Procedures to analyse confounded, polytomous and interacting risk factors are proposed and it shown that there is a simple relationship between two distinct estimators previously suggested for use with deleterious and beneficial (or preventive) factors. Finally the relevance of attributable risk to an assessment of the potential effects of risk factor modification is discussed in the preventive medicine framework.
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              Hospital-acquired infections--appropriate statistical treatment is urgently needed!

              Martin Schumacher, Arthur Allignol, Jan Beyersmann … (2013)
              Research on hospital-acquired infections (HAIs) requires the highest methodological standards to minimize the risk of bias and to avoid misleading interpretation. There are two major issues related specifically to studies in this area, namely the timing of infection and the occurrence of so-called competing risks, which deserve special attention. Just as a patient who acquires a serious infection during hospital admission needs appropriate antibiotic treatment, data being collected in studies on hospital-acquired infections need appropriate statistical analysis. We illustrate the urgent need for appropriate statistical treatment of hospital-acquired infections with some examples from recently conducted studies.The considerations presented are relevant for investigations on risk factors for HAIs as well as for outcome studies.
              Article 0 comments Cited 43 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Publication date Created: 25 March 2019
                Article
                ArXiV ID: 1903.10315
                SO-VID: 5422055d-3636-4dd6-a64a-346d4dadb05d
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                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Comments A revised version of this manuscript has been submitted to a journal on March 8 2019
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                ScienceOpen disciplines: Methodology
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                ScienceOpen disciplines: Methodology

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