Abstract
Data modeling can be applied to improving the precision of clinical studies and multiple regression modeling is increasingly used for this purpose. To assess the uncertainties and risks of misinterpretations commonly encountered in regression analyses and rarely communicated in research papers. Regression analyses add uncertainties to the data in the form of subjective judgments and uncertainty about the appropriate transformation of the data. Additional flaws include; the assumption that baseline characteristics are independent of treatment efficacies; the loss of sensitivity of testing if the models do not fit the data well enough; the risk that clinical phenomena like toxicity effects and complete remissions go unobserved; the risk of clinically unrealistic results if multiple variables are included. Regression analyses, although a very good tool for exploratory research, are not sufficiently reliable for randomized clinical trials.
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