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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F20%3A73604803" target="_blank" >RIV/61989592:15310/20:73604803 - isvavai.cz</a>

Výsledek na webu

<a href="https://obd.upol.cz/id_publ/333184689" target="_blank" >https://obd.upol.cz/id_publ/333184689</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0962280219864125" target="_blank" >10.1177/0962280219864125</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA18-09188S" target="_blank" >GA18-09188S: Využití analýzy kompozičních dat pro hodnocení kombinovaného efektu pohybové aktivity, sedavého chování a spánku na dětskou obezitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

STATISTICAL METHODS IN MEDICAL RESEARCH

ISSN

0962-2802

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

1447-1465

Kód UT WoS článku

000479470500001

EID výsledku v databázi Scopus

2-s2.0-85070291570