Document details for 'Cox regression survival analysis with compositional covariates: application to modelling mortality risk from 24-hour physical activity patterns'

Authors McGregor, D.E., Palarea Albaladejo, J., Dall, P.M., Hron, K. and Chastin, S.F.M.
Publication details Statistical Methods in Medical Research, https://doi.org/10.1177/09622802. SAGE.
Publisher details SAGE
Keywords rvival analysis, Cox regression, compositional data, time use, accelerometry, physical activity, sedentary behaviour, NHANES
Abstract Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular statistical tool used in this context. However, when the exposure include compositional covariables; that is, variables representing a relative makeup such as a nutritional or physical activity behaviour composition, some basic assumptions of the Cox regression model and associated significance tests are violated. Amongst others, compositional variables involve an intrinsic interplay between one another which confronts results and conclusions based on considering them in isolation as it is ordinarily done. In this work, we introduce a formulation of the Cox regression model in terms of log-ratio coordinates which suitably deals with the constraints of compositional covariates, facilitates the use of common statistical inference methods and allows for scientifically meaningful interpretations. We illustrate its practical application to a public health problem like the estimation of the mortality hazard associated with the composition of daily activity behaviour (physical activity, sitting time and sleep) using data from the U.S. National Health and Nutrition Examination Survey (NHANES).
Last updated 2019-07-30

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