A flexible AFT model for misclassified clustered interval-censored data
| Autor: | Alejandro Jara, Arnošt Komárek, María José García-Zattera |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Male
Statistics and Probability Time Factors Computer science Bayesian probability Oral Health Interval (mathematics) Oral health Accelerated failure time model computer.software_genre 01 natural sciences General Biochemistry Genetics and Molecular Biology 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Statistics Cluster Analysis Humans Computer Simulation Longitudinal Studies 030212 general & internal medicine 0101 mathematics Child Event (probability theory) Models Statistical General Immunology and Microbiology Applied Mathematics Bayes Theorem General Medicine Mixture model Random effects model Distribution (mathematics) Female Data mining General Agricultural and Biological Sciences computer |
| Zdroj: | Biometrics Artículos CONICYT CONICYT Chile instacron:CONICYT |
| ISSN: | 0006-341X |
| DOI: | 10.1111/biom.12424 |
| Popis: | Motivated by a longitudinal oral health study, we propose a flexible modeling approach for clustered time-to-event data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times (interval-censored data) and on top of that, the determination of the occurrence of the event is subject to misclassification. The clustered time-to-event data are modeled using an accelerated failure time model with random effects and by assuming a penalized Gaussian mixture model for the random effects terms to avoid restrictive distributional assumptions concerning the event times. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. A Bayesian implementation of the proposed model is described in a detailed manner. We additionally provide empirical evidence showing that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also provide results of a simulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|