  Kaplan-Meier survival analysis represents the most objective measure of
treatment efficacy in oncology, though subjected to potential bias, which is
worrisome in an era of precision medicine. Independent of the bias inherent to
the design and execution of clinical trials, bias may be the result of patient
censoring, or incomplete observation. Unlike disease/progression free survival,
overall survival is based on a well defined time point and thus avoids interval
censoring, but right-censoring, due to incomplete follow-up, may still be a
source of bias. We study three mechanisms of right-censoring and find that one
of them, surrogate of patient lost to follow-up, is able to impact Kaplan-Meier
survival, improving significantly the estimation of survival in comparison with
complete follow-up datasets, as measured by the hazard ratio. We also present
two bias indexes able to signal datasets with right-censoring associated
overestimation of survival. These bias indexes can detect bias in public
available datasets
