Algorithms for imputing partially observed recurrent events with applications to multiple imputation in pattern mixture models.
J Biopharm Stat
; 28(3): 518-533, 2018.
Article
em En
| MEDLINE
| ID: mdl-28544854
ABSTRACT
Five algorithms are described for imputing partially observed recurrent events modeled by a negative binomial process, or more generally by a mixed Poisson process when the mean function for the recurrent events is continuous over time. We also discuss how to perform the imputation when the mean function of the event process has jump discontinuities. The validity of these algorithms is assessed by simulations. These imputation algorithms are potentially very useful in the implementation of pattern mixture models, which have been popularly used as sensitivity analysis under the non-ignorability assumption in clinical trials. A chronic granulomatous disease trial is analyzed for illustrative purposes.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Ensaios Clínicos como Assunto
/
Doença Granulomatosa Crônica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article