A hierarchical step-model for causation of bias-evaluating cancer treatment with epidemiological methods

Abstract

As epidemiological methods are used increasingly to evaluate the effects of cancer treatment, guidelines for the application of such methods in clinical research settings are necessary. Towards this end, we present a hierarchical step-model for causation of bias, which depicts a real-life study as departing from a perfect setting and proceeding step-wise towards a calculated, often adjusted, effect-parameter. Within this model, a specific error (which influences the effect-measure according to one of four sets of rules) is introduced on one (and only one) of the model's four steps. This hierarchical step-model for causation of bias identifies all sources of bias in a study, each of which depicts one or several errors which can be further categorized into one of the model's four steps. Acceptance of this model has implications for ascertaining the degree to which a study effectively evaluates the effects of cancer treatment (level of scientific evidence).