  Left censoring can occur with relative frequency when analysing recurrent
events in epidemiological 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 objective of this paper is to propose a statistical
method that performs successfully in these circumstances. Our proposal is based
on the use of models with specific baseline hazard, imputing the number of
prior episodes when unknown, 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 performance is examined in different scenarios through a
comprehensive simulation study.The proposed method achieves notable performance
even when the percentage of subjects at risk before the beginning of the
follow-up is very elevated, with biases that are often under 10\% and coverages
of around 95\%, sometimes somewhat conservative. If the baseline hazard is
constant, it seems to be that the ``Gap Time'' approach is better; if it is not
constant, the ``Counting Process'' seems to be a better choice. Because of the
lack of knowledge of the prior episodes that have been experienced by a part
(or all) of subjects, the use of common baseline methods is not advised. Our
proposal seems to perform acceptably in the majority of the scenarios proposed,
becoming an interesting alternative in this context.
