miRecSurv package: Prentice-Williams-Peterson models with multiple imputation of unknown number of previous episodes
Left censoring can occur with relative frequency when analysing recurrent events in epi demiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study, may cause the unawareness of prior episod...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/183236 |
| Acceso en línea: | https://hdl.handle.net/2445/183236 |
| Access Level: | acceso abierto |
| Palabra clave: | Dependència (Estadística) Imputació múltiple (Estadística) Ciències de la salut Investigació mèdica Dependence (Statistics) Multiple imputation (Statistics) Medical sciences Medicine research |
| Sumario: | Left censoring can occur with relative frequency when analysing recurrent events in epi demiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study, may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over-, suband equidispersion, with a stratified model depending on whether the individual had or had not previously been at risk, and the use of a frailty term. The usage of the package is illustrated by means of a real data example based on a occupational cohort study and a simulation study. |
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