RE: order of covariate inclusion -> avoiding stepwise approaches -> abandoning exploratory analysis?

From: marc.gastonguay@snet.net Subject: RE: [NMusers] order of covariate inclusion -> avoiding stepwise approaches -> abandoning exploratory analysis? Date: 9/26/2003 1:55 PM Hello Dave, You raise an important point. I can see why you'd say that clinical relevance is dependent upon the data gathered in as much that extrapolation of any conclusions from a clinical trial are dependent upon the characteristics of the population studied, etc. Let's assume we're in the case where you can extrapolate results of a trial to the general patient population. Isn't clinical relevance of a model parameter something that must be assessed given some understanding of what is a meaningful change in some endpoint (concentration, response, toxicity, etc.). For example - the clinical relevance of a covariate-induced change in clearance could be assessed by understanding how much exposure can vary before the pharmacodynamic response yeilds unacceptible toxicity or lack of efficacy. If we cannot determine the clinical relevance of a covariate effect, it is very difficult to make use of the model in a "learning" mode (it may still be useful for prediction). When we start with a full model, we can objectively evaluate whether or not a covariate effect is supported by the data at hand by examining point and interval estimates of the covariate parameters. If the original hypothesis is that all of the covariates in the full model are clinically relevant, the data may very well contradict this by resulting in a covariate parameter estimate that is precise and near zero (or the null value for that covariate). The approach I described for covariate modeling does utilize some Bayesian notions in that inference is based on parameter estimates and uncertainties, rather than p-values, but it does not completely rely on informative priors. Not every parameter in the full model has to be based on prior knowledge. The full model could also include covariates that you know nothing about, but are interested in exploring. Marc