Contesting that a debate on evidence-based health care has taken place, this article charts three paths to the future: continuing avoidance of debate by proponents of evidence-based medicine (EBM); conflict, which the EBM movement courts and critics have espoused, and dialogue. The last portal allows for integration, which would end the disagreement between EBM and its critics and make a debate unnecessary. In search of integration, I sketch a bridge whose construction requires not compromise but a win- win approach. The (...) bridge is a medicine of meaning (MOM). Consolidating multiple pillars of evidence to unify questions that are not necessarily the same for protagonists and critics of EBM, a MOM contends that the purpose or meaning of medicine is always healing and helping, and each party finds meaning in medicine by contributing to this common purpose in its own distinctive way. (shrink)

Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses in clinical research have been carried out from a ‘frequentist’ perspective. The presence of an alternative paradigm – the Bayesian paradigm – has been relatively unknown in clinical research until recently. There is currently a growing interest in the use of Bayesian statistics in health care research. This is due both to a growing realization of the limitations of frequentist methods and to the ability of (...) Bayesian methods explicitly to incorporate prior expert knowledge and belief into the analyses. This is in contrast to frequentist methods, where prior experience and beliefs tend to be incorporated into the analyses in an ad hoc fashion. This paper outlines the frequentist and Bayesian paradigms. Acute myocardial infarction mortality data are then analysed from both a Bayesian and a frequentist perspective. In some analyses, the two methods are seen to produce comparable results; in others, they produce different results. It is noted that in this example, there are clinically relevant questions that are more easily addressed from a Bayesian perspective. Finally, areas in clinical research where Bayesian ideas are increasingly common are highlighted. (shrink)

Randomized controlled clinical trials play an important role in the development of new medical therapies. There is, however, an ethical issue surrounding the use of randomized treatment allocation when the patient is suffering from a life threatening condition and requires immediate treatment. Such patients can only benefit from the treatment they actually receive and not from the alternative therapy, even if it ultimately proves to be superior. We discuss a novel new way to analyse data from such clinical trials based (...) on the use of the recently developed theory of imprecise probabilities. This work draws an explicit distinction between the related but nevertheless distinct questions of inference and decision in clinical trials. The traditional question of scientific interest asks 'Which treatment offers the greater chance of success?' and is the primary reason for conducting the clinical trial. The question of decision concerns the welfare of the patients in the clinical trial, asking whether the accumulated evidence favours one treatment over the other to such an extent that the next patient should decline randomization and instead express a preference for one treatment. Consideration of the decision question within the framework of imprecise probabilities leads to a mathematical definition of equipoise and a method for governing the randomization protocol of a clinical trial. This paper describes in detail the protocol for the conduct of clinical trials based on this new method of analysis, which is illustrated in a retrospective analysis of data from a clinical trial comparing the anti-emetic drugs ondansetron and droperidol in the treatment of postoperative nausea and vomiting. The proposed methodology is compared quantitatively using computer simulation studies with conventional clinical trial designs and is shown to maintain high statistical power with reduced sample sizes, at the expense of a high type I error rate that we argue is irrelevant in some specific circumstances. Particular emphasis is placed on describing the type of medical conditions and treatment comparisons where the new methodology is expected to provide the greatest benefit. (shrink)

Chapter 1 Who this book is for and who it is not for1 There are already too many books offering solutions to the problems of the health service. ...

The traditional medical epistemology, resting on a biomedical paradigmatic monopoly, fails to display an adequate representation of medical knowledge. Clinical knowledge, including the complexities of human interaction, is not available for inquiry by means of biomedical approaches, and consequently is denied legitimacy within a scientific context. A gap results between medical research and clinical practice. Theories of knowledge, especially the concept of tacit knowing, seem suitable for description and discussion of clinical knowledge, commonly denoted the art of medicine. A metaposition (...) allows for inquiry of clinical knowledge, inviting an expansion of the traditional medical epistemology, provided that relevant criteria for scientific knowledge within this field are developed and applied. The consequences of such approaches are discussed. (shrink)