Transcript Tony O'Hagan

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 …