Monte Carlo estimation of extrapolation of quality‐adjusted survival for follow‐up studies

Abstract

The expected quality-adjusted survival (QAS) for an index population with a speci"c 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 "rst created a reference population from the life tables of the general population according to the Monte Carlo method. Secondly, we "tted 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 "tted 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.

Estimation of loss of quality-adjusted life expectancy (QALE) for patients with operable versus inoperable lung cancer: Adjusting quality-of-life and lead-time bias for utility of surgery

“…After obtaining the survival function of the cohort through Kaplan-Meier estimate, a method proposed by Huang and Wang was used to extrapolate the survival function beyond the end of the follow-up period [13]. This approach assumed that NSCLC generated a constant excess hazard after the initial follow-up period, and its calculation comprised three steps.…”

Estimation of Quality-Adjusted Life Expectancy in Patients Under Prolonged Mechanical Ventilation

“…The expected lifetime utility loss for PMV patients was calculated by assuming a uniform utility of one for the age-and gendermatched reference subjects and subtracting the QALE of PMV patients [14,16,18]. In other words, based on the hazard function or vital statistics of Taiwan, we simulated survival functions of ten reference people of the same age and gender for every PMV patient and assumed that the utility of their QOL is one that would have been the QALE of each PMV patient had they not developed the condition.…”

Temporal changes in loss of life expectancy due to cancer in Australia: a flexible parametric approach

“…By the use of a relative survival approach, the LOLE is not dependent on accurate cause of death information and additionally provides estimates of the loss in expectation of life for an entire cohort diagnosed with a specific cancer compared to the general population, irrespective of whether they died from that cancer. Although the specific methods differ, there are similarities between this approach and that developed by Hwang and Wang [8,9] and applied recently [10], in that they both take background mortality information into account for extrapolation of survival beyond the end of follow-up.…”