Method(s): Simulation of a patient population with a fixed life expectancy and treatment regime that is subject to censoring is used to analyze the effect of censoring on the KM-estimator of post-treatment survival times. An alternative approach to the estimation of PTS, where treatment and survival are treated as separate entities, is applied to same dataset. The results are compared to the theoretical PTS.
Results: The simulations demonstrate that applying the Kaplan-Meier directly to post-treatment survival times will produce incorrect PTS-estimates whereas the alternative approach performs well. In additional simulations the effect of heterogenic patient populations, treatment regimes and incomplete data on the estimated PTS is illustrated.
Conclusions: Applying the KM-estimator to censored PTS data leads to incorrect estimation of post-treatment survival. PTS will be estimated correctly only when treatment regime and survival are estimated separately and then combined. The methodology described allows estimation of PTS, and if so desired, discounted PTS which is generally used in cost effectiveness evaluations. The methodology can therefore be used and is applicable to the evaluation of loss of life reduction and/or cost effectiveness of measures reducing treatment risks of any long-term repetitive treatment or medication.