In a narrow meaning, responders to a therapy are all those who will react as expected following the administration of this therapy. However, a wider definition is worth considering: all those for whom the administration of the therapy will be beneficial. Innovative therapies are increasingly expensive and hazardous, and limiting prescriptions to responders is both economically and ethically compulsory. The theoretical basis for such an approach exists. The process of defining the profile of responders consists of identifying the characteristics of the patients that interact with the size of the effect and integrating them quantitatively in a predictive model. The effect model, which is the relation between the risks of the event with and without the treatment, can be used for the prediction. It can integrate interactions of the efficacy with risk factors and/or genes. The data to be used to achieve both the identification of the interactions and the building of the predictive model are those from the studied population, the set of patients enrolled in clinical trials. Hence, the process of defining the therapy is an extrapolation from the studied population. To carry out the extrapolation process one can use various available techniques, of which none fully fits the purpose. No method is currently both fully adequate and validated. Finally, the predictive models, which we need to identify responders, do not exist in practice. Fortunately, new research approaches have been developed recently.
Clinical Chemistry and Laboratory Medicine (
CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine.
CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor of over three.
CCLM is the official journal of nine national clinical societies and associated with EFLM.