  We present the first application of a hierarchical Markov Chain Monte Carlo
(MCMC) follow-up on continuous gravitational-wave candidates from real-data
searches. The follow-up uses an MCMC sampler to draw parameter-space points
from the posterior distribution, constructed using the matched-filter as a
log-likelihood. As outliers are narrowed down, coherence time increases,
imposing more restrictive phase-evolution templates. We introduce a novel Bayes
factor to compare results from different stages: The signal hypothesis is
derived from first principles, while the noise hypothesis uses extreme value
theory to derive a background model. The effectiveness of our proposal is
evaluated on fake Gaussian data and applied to a set of 30 outliers produced by
different continuous wave searches on O2 Advanced LIGO data. The results of our
analysis suggest all but five outliers are inconsistent with an astrophysical
origin under the standard continuous wave signal model. We successfully ascribe
four of the surviving outliers to instrumental artifacts and a strong hardware
injection present in the data. The behavior of the fifth outlier suggests an
instrumental origin as well, but we could not relate it to any known
instrumental cause.
