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

Publisher
SAGE
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).
Year
2019
Category
Refereed journal
Output Tags
WP 2.2 Livestock production, health, welfare and disease control (RESAS 2016-21)