Costs Some introductory remarks by Tony O’Hagan

Welcome! Welcome to the fourth CHEBS dissemination workshop This forms part of our Focus Fortnight on “Costs” Our format allows plenty of time for discussion of the issues raised in each talk, so please feel free to join in!

Focus on mean cost In Health Economics, we want to estimate mean cost ›That is, we want the population mean ›That is, the average over the (large, but finite) population of individual patients for whom a health care provider is responsible ›Often we want to compare mean costs of two or more interventions ›We usually then want to compare with the mean efficacies of those interventions

Modelling costs We cannot observe the mean cost We can ‘observe’ costs for individual patients in the population To use these data to learn about the mean cost, we need to model the distribution of individual costs in the population Modelling costs is the topic of this FF

New statistical challenges Statisticians have developed a massive body of techniques for analysis of efficacy in RCTs Costs add a new dimension ›Their distributions are very non-standard ›Models of costs will be more complex than the familiar models of efficacy ›Add the fact that we need to model the association between efficacy and cost (another FF?!) In cost-effectiveness trials we face a whole array of new challenges

What do we know? Cost distributions are generally highly skew, long-tailed and peaked (leptokurtic) Their shape characteristics depend on the particular pattern of resource use prevalent in the disease and intervention being studied ›So one trial is likely to produce a markedly different distribution from the next ›But we can often expect distributions with similar characteristics in different arms of the same trial We should use this knowledge

Current approaches Nonparametric ›Sample means (robust, asymptotically normal) ›Nonparametric bootstrap (more sensitive to distribution shape, also only asymptotically valid) ›Various other methods (sign test, Mann-Whitney etc) inappropriate because they don’t address population means Parametric ›Model distribution ›Can use transformation, but must make inference about means on original scale

Controversy A series of papers advised different approaches – very crudely … ›Briggs & Gray (1998) emphasised generality of bootstrap ›Thompson & Barber (2000) advocated methods based on sample mean ›O’Hagan & Stevens (2003) criticised nonparametrics and advocated parametric modelling (and Bayes) One output of this FF will be a consensus paper!

Other modelling issues Covariate adjustment ›Methods should extend cleanly to covariate modelling Decomposing total costs ›Analyse resource use separately Multi-centre trials ›How to model between-centre differences? Semi-parametric modelling Extrapolation ›In time, to different populations, to tails …