Pischon et al. Respond to 'Variable Selection versus Shrinkage in Control of Confounders

摘要

We read with great interest Dr. Greenland's invited commentary (1) about variable selection to control for confounding in observational studies. We agree with Dr. Greenland that the identification of confounders should be based primarily on background knowledge and not on significance testing. However, our proposed method (2) is not meant primarily as a variable selection procedure. Currently, relative risk estimates are commonly presented from nested models with increasing complexity of covariate use (3). This is not caused predominantly by the uncertainty of selecting the proper model, but rather by the interest to quantify the relative effect of adjustment for specific covariates on risk estimates. For example, relative risks from a multivariate model for a specific nutrient might be compared with those from a model with additional adjustment for other nutrients or foods to evaluate the relative importance of confounding by other dietary characteristics (3). Another potential application is the situation where it may be more likely that a covariate reflects a potential mediator rather than a confounder. Although such type of analysis requires several assumptions and careful interpretation, the comparison of models without and with adjustment for potentially intermediate variables might be informative to quantify the change in a beta coefficient when these covariates are taken into account (4). As pointed out by Dr. Greenland, the precision of the impact of a covariate on the incidence rate ratio in a given regression model may depend on sample size and measurement error. Our method allows deriving a confidence interval for the ratio of incidence rate ratios and is therefore an important tool for more precisely analyzing and interpreting results from Cox proportional hazards models (2)#