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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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              Risk factors for the development of nosocomial pneumonia and mortality on intensive care units: application of competing risks models

              Martin Wolkewitz, Ralf-Peter Vonberg, Hajo Grundmann … (2008)
              Introduction Pneumonia is a very common nosocomial infection in intensive care units (ICUs). Many studies have investigated risk factors for the development of infection and its consequences. However, the evaluation in most of theses studies disregards the fact that there are additional competing events, such as discharge or death. Methods A prospective cohort study was conducted over 18 months in five intensive care units at one university hospital. All patients that were admitted for at least 2 days were included, and surveillance of nosocomial pneumonia was conducted. Various potential risk factors (baseline- and time-dependent) were evaluated in two competing risks models: the acquisition of nosocomial pneumonia and discharge (dead or alive; model 1) and for the risk of death in the ICU and discharge alive (model 2). Results Patients from 1,876 admissions were included. A total of 158 patients developed nosocomial pneumonia. The main risk factors for nosocomial pneumonia in the multivariate analysis in model 1 were: elective surgery (cause-specific hazard ratio = 1.95; 95% CI 1.33 to 2.85) or emergency surgery (1.59; 95% CI 1.10 to 2.28) prior to ICU admission, usage of a nasogastric tube (3.04; 95% CI 1.25 to 7.37) and mechanical ventilation (5.90; 95% CI 2.47 to 14.09). Nosocomial pneumonia prolonged the length of ICU stay but was not directly associated with a fatal outcome (p = 0.55). Conclusion More studies using competing risk models, which provide more accurate data compared to naive survival curves or logistic models, should be carried out to verify the impact of risk factors and patient characteristics for the acquisition of nosocomial infections and infection-associated mortality.
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                Author and article information

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                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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