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Bias and precision of measures of survival gain from right-censored data

Lamb, Karen E., Williamson, Elizabeth J., Coory, Michael and Carlin, John B. 2015, Bias and precision of measures of survival gain from right-censored data, Pharmaceutical statistics, vol. 14, no. 5, pp. 409-417, doi: 10.1002/pst.1700.

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Title Bias and precision of measures of survival gain from right-censored data
Author(s) Lamb, Karen E.ORCID iD for Lamb, Karen E.
Williamson, Elizabeth J.
Coory, Michael
Carlin, John B.
Journal name Pharmaceutical statistics
Volume number 14
Issue number 5
Start page 409
End page 417
Total pages 9
Publisher Wiley
Place of publication Chichester, Eng.
Publication date 2015-07
ISSN 1539-1604
Keyword(s) difference in mean survival
difference in median survival
life years gained
survival time
Summary In cost-effectiveness analyses of drugs or health technologies, estimates of life years saved or quality-adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close-out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain - difference in median, restricted mean and extended mean survival - when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost-effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade-offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable.
Language eng
DOI 10.1002/pst.1700
Field of Research 010402 Biostatistics
Socio Economic Objective 920204 Evaluation of Health Outcomes
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Wiley
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Created: Thu, 27 Aug 2015, 12:23:16 EST

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