Here’s a very long post, to make up for the recent silence on the blog… Lately, I’ve been working on a new project involving the use of survival analysis data and results, specifically for health economic evaluation (cue Cake’s rendition below).
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I have to say I’m not really a massive expert on survival analysis, in the sense that it’s never been my main area of interest/research. But I think the particular case of cost-effectiveness modelling is actually very interesting $-$ the main point is that unlike in a standard trial, where the observed data are used to determine some median estimate of the survival curve (typically across the different treatment arms), in health economic evaluations the target quantity is actually the mean survival time(s), because these are then usually used to extrapolate the (limited!) trial data into a full decision-analytic model, often covering a relatively large time horizon. Among many, many others , I think Chris et al make a very good point for this, here.
Anyway, one of the main implications to this is that typically the practitioners are left with the task of fitting a (range of) parametric survival model(s) to their data. Nick Latimer among others have done excellent work in suggesting suitable guidelines. (In fact, both Chris and Nick did come to talk to one of our workshops/seminars, last summer).
Over and above the necessary choice of models, I think there are other interesting issues/challenges for the health economic modeller:
- (Parametric) Survival models are often tricky because there are many different parameterisations, leading to different results presentations. This can be very confusing and without extra care lead to disastrous consequences (because the economic model extrapolates on the wrong survival curves!).
- Even when the parameterisation is taken care of, we are normally interested in characterising …read more
Source:: r-bloggers.com