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The expected quality-adjusted survival (QAS) for an index population with a specific disease can be estimated by summing the product of the survival function and the mean quality of life function of the population. In many follow-up studies with heavy censoring, the expected QAS may not be well estimated due to the lack of data beyond the close of follow-up. In this paper, we first created a reference population from the life tables of the general population according to the Monte Carlo method. Secondly, we fitted a simple linear regression line to the logit of the ratio of quality-adjusted survival functions for the index and reference populations up to the end of follow-up. Finally, combining information on the reference population with the fitted line, we predicted the expected quality-adjusted survival curve beyond the follow-up period for the index population. Simulation studies have shown that the simple Monte Carlo estimation procedure is a potential approach for estimating expected QAS and the survival function beyond the follow-up with a certain degree of accuracy.
Hwang et al. (Thu,) studied this question.