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Monte Carlo estimation of extrapolation of quality‐adjusted survival for follow‐up studies
Author(s) -
Hwang JingShiang,
Wang JungDer
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990715)18:13<1627::aid-sim159>3.0.co;2-d
Subject(s) - monte carlo method , statistics , extrapolation , population , censoring (clinical trials) , econometrics , logit , survival analysis , mathematics , index (typography) , computer science , medicine , environmental health , world wide web
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. Copyright © 1999 John Wiley & Sons, Ltd.