title: Bias and precision of measures of survival gain from right-censored data.
abstract: 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.
authors.affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, VIC, Australia.|Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia.|Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia.
authors.forename: Karen E
authors.identifier: http://orcid.org/0000-0001-9782-8450
authors.initials: KE
authors.lastname: Lamb
authors.affiliation: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.|Farr Institute of Health Informatics Research, London, UK.
authors.forename: Elizabeth J
authors.initials: EJ
authors.lastname: Williamson
authors.affiliation: Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Coory
authors.affiliation: Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia.|Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia.|School of Population & Global Health, University of Melbourne, Melbourne, VIC, Australia.
authors.forename: John B
authors.initials: JB
authors.lastname: Carlin
mesh_terms: D015982:Bias; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination; D006801:Humans; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016019:Survival Analysis
keywords: difference in mean survival; difference in median survival; life years gained; simulation; survival time