Bias Corrected H-likelihood Approach for Joint Models of Longitudinal and Survival Data, With Application to Community Acquired Pneumonia
| Autor: | Gleb Haynatzki, Karl Stessy Bisselou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Estimation
Computer science business.industry General Neuroscience Computation Repeated measures design Context (language use) General Medicine Machine learning computer.software_genre Random effects model General Biochemistry Genetics and Molecular Biology Survival data Artificial intelligence General Agricultural and Biological Sciences Joint (audio engineering) business computer |
| Zdroj: | WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE. 18:119-125 |
| ISSN: | 2224-2902 1109-9518 |
| DOI: | 10.37394/23208.2021.18.14 |
| Popis: | Time-to-event coupled with longitudinal trajectories are often of interest in biomedicine, and one popular approach to analysing such data is with a Joint Model (JM). JMs often have intractable marginal likelihoods, and one way to tackle this issue is by using the hierarchical likelihood (HL) estimation approach by Lee and Nelder [12]. The HL approximation sometimes results in biased estimates, and we propose a biascorrection approach (C-HL) that has been used for other models (eg, frailty models). We have applied, for the first time, the C-HL in the context of joint modelling of time-to-event and repeated measures data. Our C-HL method shows efficiency improvement, which comes at a cost of a more expensive computation than the existing HL approach. Additionally, we illustrate our method with a new MIMIC-IV CAP dataset |
| Databáze: | OpenAIRE |
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