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

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. Howeve...

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Bibliographic Details
Published in:Statistical methods in medical research 2020-05, Vol.29 (5), p.1447-1465
Main Authors: McGregor, DE, Palarea-Albaladejo, J, Dall, PM, Hron, K, Chastin, SFM
Format: Article
Language:English
Subjects:
Composition
Exercise
Health education
Health problems
Medical research
Mortality
Nutrition
Nutrition Surveys
Physical activity
Proportional Hazards Models
Public health
Regression Analysis
Regression models
Statistical analysis
Statistical inference
Statistical methods
Statistical significance
Survival
Survival analysis
Time use
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Summary: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 includes 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. Compositional variables involve an intrinsic interplay between one another which precludes results and conclusions based on considering them in isolation as 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: 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).
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280219864125