A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes
© 2017 John Wiley & Sons Ltd 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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Wiley-Blackwell Publishing Ltd.
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/63138 |
| _version_ | 1848761005038895104 |
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| author | Zhao, J. Zhao, Yun Lee, A. Binns, Colin |
| author_facet | Zhao, J. Zhao, Yun Lee, A. Binns, Colin |
| author_sort | Zhao, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 John Wiley & Sons Ltd 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. |
| first_indexed | 2025-11-14T10:24:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-63138 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:24:47Z |
| publishDate | 2017 |
| publisher | Wiley-Blackwell Publishing Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-631382018-02-06T06:23:28Z A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes Zhao, J. Zhao, Yun Lee, A. Binns, Colin © 2017 John Wiley & Sons Ltd 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. 2017 Journal Article http://hdl.handle.net/20.500.11937/63138 10.1111/ppe.12410 Wiley-Blackwell Publishing Ltd. restricted |
| spellingShingle | Zhao, J. Zhao, Yun Lee, A. Binns, Colin A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title | A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title_full | A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title_fullStr | A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title_full_unstemmed | A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title_short | A Time-varying Covariate Approach for Survival Analysis of Paediatric Outcomes |
| title_sort | time-varying covariate approach for survival analysis of paediatric outcomes |
| url | http://hdl.handle.net/20.500.11937/63138 |