Attributable fractions with survival data

Samuelsen, Sven Ove; Eide, Geir Egil

doi:10.1002/sim.3022

Cited by 52 publications

(62 citation statements)

References 43 publications

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“…More recently, methodological advances demonstrate that prospective cohort studies are preferable for PAF estimation because the calculations rely on censored time to event data [43,44,28]. Historically, there is a large body of literature estimating PAF from case-control and cross sectional data [24].…”

Section: Discussionmentioning

confidence: 99%

Population attributable fraction of type 2 diabetes due to physical inactivity in adults: a systematic review

et al. 2014

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BackgroundPhysical inactivity is a global pandemic. The population attributable fraction (PAF) of type 2 diabetes mellitus (T2DM) associated with physical inactivity ranges from 3% to 40%. The purpose of this systematic review was to determine the best estimate of PAF for T2DM attributable to physical inactivity and absence of sport participation or exercise for men and women.MethodsWe conducted a systematic review that included a comprehensive search of MEDLINE, EMBASE, SportDiscus, and CINAHL (1946 to April 30 2013) limited by the terms adults and English. Two reviewers screened studies, extracted PAF related data and assessed the quality of the selected studies. We reconstructed 95% CIs for studies missing these data using a substitution method.ResultsOf the eight studies reporting PAF in T2DM, two studies included prospective cohort studies (3 total) and six were reviews. There were distinct variations in quality of defining and measuring physical inactivity, T2DM and adjusting for confounders. In the US, PAFs for absence of playing sport ranged from 13% (95% CI: 3, 22) in men and 29% (95% CI: 17, 41) in women. In Finland, PAFs for absence of exercise ranged from 3% (95% CI: -11, 16) in men to 7% (95% CI: -9, 20) in women.ConclusionsThe PAF of physical inactivity due to T2DM is substantial. Physical inactivity is a modifiable risk factor for T2DM. The contribution of physical inactivity to T2DM differs by sex; PAF also differs if physical inactivity is defined as the absence of ‘sport’ or absence of ‘exercise’.

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Section: Discussionmentioning

confidence: 99%

Population attributable fraction of type 2 diabetes due to physical inactivity in adults: a systematic review

et al. 2014

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“…The methods for the estimation of PAF in a cohort study design, taking adequately into account the follow-up time, have been developed during recent years (Chen et al 2006;Samuelsen and Eide 2008;Cox et al 2009;Laaksonen et al 2010a,b). As far as these authors know, no publicly available program to implement these methods has, however, yet been provided, apparently hindering their wider application.…”

Section: Discussionmentioning

confidence: 99%

SASMacros for Calculation of Population Attributable Fraction in a Cohort Study Design

Laaksonen¹,

Virtala²,

Knekt³

et al. 2011

J. Stat. Soft.

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The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and its confidence interval for different types of outcomes in a cohort study design have been proposed. In this paper, we introduce SAS macros implementing these methods and illustrate their application with a data example on the impact of different risk factors on type 2 diabetes incidence.

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“…(1)) for time-to-event outcomes [6–9] and it is equivalent to the standard definition when time t is the end of follow-up in cohort studies [4]. In addition several estimation methods have been proposed for the CDF-based AR definition in cohort studies and the survival analysis context in contrast to the alternative definition based on instantaneous hazard functions [4, 5] for which only one method of estimation based on Cox’s proportional hazards model has been published [4]. …”

Section: Discussionmentioning

confidence: 99%

“…Several definitions have been proposed depending on whether authors interpret disease incidences P ( D ) and in Eq. (1) as cumulative distribution functions (CDFs) [6-9] or as instantaneous hazard functions [4, 5]. The two definitions converge only for rare diseases or low exposure prevalence [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of methods for estimating the attributable risk in the context of survival analysis

Gassama

Bénichou

Dartois

et al. 2017

BMC Med Res Methodol

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BackgroundThe attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data.MethodsUsing simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier’s estimator, one semiparametric based on Cox’s model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox’s model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points.ResultsUnder proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort.ConclusionIn practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of time in cohort studies if the proportional hazards assumption appears appropriate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0285-1) contains supplementary material, which is available to authorized users.

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Attributable fractions with survival data

Cited by 52 publications

References 43 publications

Population attributable fraction of type 2 diabetes due to physical inactivity in adults: a systematic review

Population attributable fraction of type 2 diabetes due to physical inactivity in adults: a systematic review

SASMacros for Calculation of Population Attributable Fraction in a Cohort Study Design

Comparison of methods for estimating the attributable risk in the context of survival analysis

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