Joint modeling of longitudinal and survival data–A caes study in Primary Biliary Cirrhosis data
碩士 === 國立中央大學 === 統計研究所 === 98 === In survival analysis, it’s very common that the interesting covariates were measured intermittently at different measurment times for different patients. In this scenario, the repeated measurments could include measurment errors and measurments can not be observed...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/42621981453752734228 |
| Summary: | 碩士 === 國立中央大學 === 統計研究所 === 98 === In survival analysis, it’s very common that the interesting covariates were measured intermittently at different measurment times for different patients. In this scenario, the repeated measurments could include measurment
errors and measurments can not be observed after the survival time. Those situations could result in biased inferences for study when using Cox partial likelihood. To corret the bias, we use a joint model approach which models survival time and the longitudinal covariates simultaneously. This approach was applied to analyze Primary Biliary Cirrhosis patients data with the main interest of exploring the relationship between longitudinal Mayo risk score and survival. The results
suggested that the drug D-penicillamine and age groups have no significant effect on survival and the longitudinal covariate Mayo risk score can be well described through a cubic random coefficient model and has
a significant impact on patients’ lifetime. Moreover, from AUC (area under the ROC curve) of ROC curve (Receiver Operating Characteristic curve) which suggests that the Mayo risk score has better prediction capacity than the biomarker, bilirubin.
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