  Cure models in survival analysis deal with populations in which a part of the
individuals cannot experience the event of interest. Mixture cure models
consider the target population as a mixture of susceptible and non-susceptible
individuals. The statistical analysis of these models focuses on examining the
probability of cure (incidence model) and inferring on the time-to-event in the
susceptible subpopulation (latency model).
  Bayesian inference on mixture cure models has typically relied upon Markov
chain Monte Carlo (MCMC) methods. The integrated nested Laplace approximation
(INLA) is a recent and attractive approach for doing Bayesian inference. INLA
in its natural definition cannot fit mixture models but recent research has new
proposals that combine INLA and MCMC methods to extend its applicability to
them (Bivand et al., 2014, G\'omez-Rubio et al., 2017, G\'omez-Rubio and Rue,
2018}.
  This paper focuses on the implementation of INLA in mixture cure models. A
general mixture cure survival model with covariate information for the latency
and the incidence model within a general scenario with censored and
non-censored information is discussed. The fact that non-censored individuals
undoubtedly belong to the uncured population is a valuable information that was
incorporated in the inferential process.
