Left censoring can occur with relative frequency when analyzing 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, sub, and 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 an occupational cohort study and a simulation study.
miRecSurv, compoisson, survsim
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For attribution, please cite this work as
Moriña, et al., "The R Journal: miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes", The R Journal, 2021
BibTeX citation
@article{RJ-2021-082,
author = {Moriña, David and Hernández-Herrera, Gilma and Navarro, Albert},
title = {The R Journal: miRecSurv Package: Prentice-Williams-Peterson Models with Multiple Imputation of Unknown Number of Previous Episodes},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-082},
doi = {10.32614/RJ-2021-082},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {419-426}
}