Premium
A Time‐varying Covariate Approach for Survival Analysis of Paediatric Outcomes
Author(s) -
Zhao Jian,
Zhao Yun,
Lee Andy H,
Binns Colin W
Publication year - 2017
Publication title -
paediatric and perinatal epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 88
eISSN - 1365-3016
pISSN - 0269-5022
DOI - 10.1111/ppe.12410
Subject(s) - covariate , medicine , hazard ratio , proportional hazards model , confidence interval , survival analysis , statistics , event (particle physics) , random effects model , demography , econometrics , meta analysis , mathematics , physics , quantum mechanics , sociology
Background Conventional survival analysis is commonly applied in the analysis of time‐to‐event data in paediatric studies, where the exposure variables of interest are often treated as time‐fixed. However, the values of these exposure variables can vary over time and time‐fixed analysis may introduce time‐dependent bias. Methods Time‐dependent bias is illustrated graphically considering two scenarios in longitudinal study settings for paediatric time‐to‐event outcomes. As an illustrative example, the time‐varying covariate approach was applied to survival analysis of breast‐feeding data ( n = 695) collected in China between 2010 and 2011, with an emphasis on the effects of covariates ‘solid foods introduction’ and ‘maternal return to work’ on breast‐feeding duration up to 12 months postpartum. Results Time‐varying exposures could occur before or after the occurrence of an event of interest so that time‐fixed analysis can lead to biased and imprecise parameter estimates. In the illustrative example, the reduced risk of ‘solid foods introduction’ (hazard ratio ( HR ) 0.61, 95% confidence interval ( CI ) 0.50, 0.75) on breast‐feeding cessation and an absence of an association with ‘maternal return to work’ ( HR 0.99, 95% CI 0.73, 1.36) from the time‐fixed analysis reversed ( HR 1.50, 95% CI 1.17, 1.93) and became significant ( HR 1.45, 95% CI 1.06, 2.00), respectively, based on the time‐varying covariate model. Conclusions The time‐varying covariate approach is preferable for survival analysis of time‐to‐event data in the presence of time‐varying exposures.