A two-part mixed-effects model for analyzing clustered time-to-event data with clumping at zero
In longitudinal epidemiological studies consisting of a baseline stage and a follow-up stage, observations at the baseline stage may contain a countable proportion of negative responses. The time-to-event outcomes of those observations corresponding to negative responses at baseline can be denoted a...
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| Main Authors: | , , , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/154900 |
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| 總結: | In longitudinal epidemiological studies consisting of a baseline stage and a follow-up stage, observations at the baseline stage may contain a countable proportion of negative responses. The time-to-event outcomes of those observations corresponding to negative responses at baseline can be denoted as zeros, which are excluded from standard survival analysis. Consequently, some important information on these subjects is therefore lost in the analysis. Furthermore, subjects are often clustered within hospitals, communities or health service centers, resulting in correlated observations. The framework of the two-part model has been developed and utilized widely to analyze semi-continuous data or count data with excess zeros, but its application to clustered time-to-event data with clumping at zero remains sparse. |
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