Biomedical subjects
Publications and source records attributed to F Gueyffier.
In chronic illness, when death or a non-fatal event can occur at any time, the current efficacy indices are no longer appropriate to express the effect of the treatment on the potential therapeutic objectives. The inappropriateness is not dependent on the effect model. Clues for solutions are proposed.
Efficacy indices do not contain the same information although they are all combinations of the same two quantities. Therefore, one should choose the proper index. Actually, none is entirely appropriate. Each more or less meets the specifications, depending on the underlying effect model for the therapy considered. However, one can say that the absolute benefit is more appropriate from the patient's point of view, the relative from the scientific point of view and the number of patients to treat from the policy maker's point of view. Nevertheless, this classification needs to be considered with caution. Finally, it emerges from the review that none is fully relevant to express the efficacy of a therapy, even in the most suitable condition, the acute illness.
The objectives of the reflection on biomarkers are far ahead of the issues of surrogacy, and constitute a major stake for pharmacology: enhancement of the liability of positive or negative screening for candidate drugs, speed-up of development processing, accurate identification of responders to a specific drug, and optimization of drug monitoring. Examples illustrate the situations where biomarkers are useful: diagnosis, prognosis, prediction of therapeutic response, either favourable or unwanted. The advance of pharmacogenetics leaves hope for optimization of drug use through the adaptation of drug choice to an individual profile. Some deficiencies have been identified concerning policies of transfer, evaluation or interdisciplinary validation. The optimization of the use of highest level evidence data, which is possible when appropriate biomarkers and clinical data are systematically collected during all the steps in drug development, the formal modelling of physiological, pathological and pharmacological processes and their assessment through computer simulation, and the simultaneous conduct of pragmatic and cognitive approaches, appear to be the necessary conditions for fulfillment of the objectives defined above.
The overall effect of antihypertensive drug treatment has been well documented. The proportion of patients who benefit varies according to their baseline cardiovascular risk, and is small for the majority of people treated. Some investigators propose limiting the treatment target population to patients at high cardiovascular risk, but several assumptions must be made to justify this procedure. The INDANA project is a meta-analysis based on individual patient data, and thus offers the opportunity to check the validity of these assumptions. Its main objective is to identify responders (and non-responders) in the drug treatment of hypertension. The rationale and methods for such an approach are presented here, with the solution for some technical problems. The conclusion of the data collection has shown that the project is feasible. The results of the main analysis should be available in 1996, and should contribute to the selection of responders and to the individualization of the treatment of hypertension.
The aim of therapeutic information is that people who need it have access to data of the highest level of evidence. In this context, a message is defined as the medium of information. The conditions for an ideal message to the prescriber are described: its content, the qualities it must meet, and the functional issues it will tackle. An example illustrates the importance of the form of a message. The difference between recommendation and message is highlighted.
The efficacy of a drug is a quantitative concept rather than a qualitative one. This quantity is expressed by several efficacy indices. None of them meet all the requirements. However, that of absolute benefit is especially suitable for the patients because it tells them the exact gain they can expect from taking the treatment. The absolute benefit varies according to patients' profiles because it interacts with some components of these profiles. In theory, such interactions can be used to predict the size of the absolute benefit for each patient, as well to describe better than with the current tools the therapy target population. We explain why meta-analysis and effect models are means of improving the prediction of the size of the effect and the definition of the therapy target population.
Reliable syntheses are more and more needed by physicians. In the therapeutic field, this need is illustrated by the growing number of syntheses using meta-analysis tools. Some of these syntheses are based on individual patient data, and this offers three kinds of advantages: the favourable consequences of a collaborative approach, the enhancement of data reliability, and a more complete extraction of useful information thanks to wider analysis potential. Most often, achieving the first two advantages does not require the individual patient data to be available, and a wider analysis potential seems to be the major rationale of such an approach. For instance, the assessment of the variation of treatment effect with along time is more accurate, but may also raise doubts about the validity of some hypotheses, for example, those underlying classical analysis techniques, or at the basis of even acknowledged pathophysiological theory. Ultimately, the concepts of identifying responders (here defined in terms of quantity or quality of life) and identifying the therapy target population are concretely applied in a way that will affect classical medical practice. Tomorrow, drugs will not be prescribed to a hypertensive individual because of the blood pressure level, but considering the size of the predicted therapeutic benefit.