This paper critically examines a particular strategy for resolving the central ethical dilemma associated with randomized clincial trials (RCTs) -- the "community equipoise" strategy (CE). The dilemma is that RCTs appear to violate a physician's duty to choose that therapy which there is most reason to believe is in the patient's best interest, randomizing patients even once evidence begins to favor one treatment. The community equipose strategy involves the suggestion that our judgment that neither treatment is to be preferred (that there obtains a state of "equipoise") is to be assessed according to a community rather than an individual standard. Thus, though a physician may personally believe that there is some reason to prefer one treatment, patients can legitimately be randomized if there remains disagreement in the community of medical professionals. Rationales in favor of this conception include the following: (i) medical knowledge is best understood as residing in the community, (ii) the judgments of others count as evidence, and so should change one's own opinion, (iii) subjects would not be better off outside the trial, and (iv) the point of any trial is the resolution of dispute in the medical community. I critically examine these rationales and argue that they are insufficient. Amongst the problems are tensions between various of these underlying rationales, and important ambiguities in just what the CE criterion is to amount to. Finally, I argue that even if use of CE was justified, it would not justify carrying out RCTs anywhere near long enough to discharge our duty to gain reliable knowledge on which to base safe and effective medical practice. Hence, we need some different justification for carrying out RCTs.
In this article, I review and expand upon arguments showing that Freedman's so-called "clinical equipoise" criterion cannot serve as an appropriate guide and justification for the moral legitimacy of carrying out randomized clinical trials. At the same time, I try to explain why this approach has been given so much credence despite compelling arguments against it, including the fact that Freedman's original discussion framed the issues in a misleading way, making certain things invisible: Clinical equipoise is conflated with community equipoise, and several versions of each are also conflated. But a misleading impression is given that, rather than distinct criteria being arbitrarily conflated, a puzzle is solved and a number of features unified. Various issues are pushed under the rug, hiding flaws of the "clinical equipoise" approach and thus deceiving us into thinking that we have a solution when we do not. Particularly significant is the ignoring of the crucial distinction between the individual patient decision and the policy decision.
It is often claimed that a clinical investigator may ethically participate (e.g., enroll patients) in a trial only if she is in equipoise (if she has no way to ground a preference for one arm of the study). But this is a serious problem, for as data accumulate, it can be expected that there will be a discernible trend favoring one of the treatments prior to the point where we achieve the trial's objective. In this paper, I critically evaluate Benjamin Freedman's 'clinical equipoise' solution to this dilemma. I argue that Freedman actually puts forth at least two distinct contrasts--one in terms of community vs. individual equipoise, and another concerning clinical vs. theoretical equipoise--and that neither of them resolves the dilemma. I then make a proposal for a more adequate account of how to think about the circumstances under which entering subjects in trials would be justified--a 'sliding-scale equipoise' that arises out of a discussion of patients' values.
Trend tests for genetic association using a matched case-control design are studied, which allows for a variable number of controls per case. However, the tests depend on the scores based on the underlying genetic model, thus it may result in substantial loss of power when the model is misspecified. Since the mode of inheritance may be unknown for complex diseases, robust trend tests in matched case-control studies are developed. Simulation is conducted to compare the trend tests and the robust trend tests under various genetic models. The results are applied to detect candidate-gene association using an example from a case-control aetiologic study of sarcoidosis.
Field research with vectors is an essential aspect of vector biology research and vector-borne disease prevention and control. This type of research, which brings experimental vector manipulations into endemic areas, can present risks to human populations. This paper seeks to stimulate a full discussion within the medical entomology community of the risks associated with vector field research. Such discussions will promote development of a consensus, among investigators, sponsoring agencies and the communities within which the work is done, so that appropriate steps can be taken to minimize and manage the risks, and adequate oversight can be maintained.
Recognizing that all traits are the result of an interaction between genes and environment, I offer a set of criteria for nevertheless making sense of our practice of singling out certain traits as genetic ones, in effect making a distinction between "causes" and "mere conditions". The central criterion is that a trait is genetic if it is genetic differences that make the differences in that trait variable in a given population. A second criterion requires that genetic traits be individuated in a way that matches what some genetic factors cause specifically. Clarifying our causal and classificatory language here can help us to avoid confusions of both theoretical and practical significance.
The central dilemma concerning randomized clinical trials (RCTs) arises out of some simple facts about causal methodology (RCTs are the best way to generate the reliable causal knowledge necessary for optimally-informed action) and a prima facie plausible principle concerning how physicians should treat their patients (always do what it is most reasonable to believe will be best for the patient). A number of arguments related to this in the literature are considered. Attempts to avoid the dilemma fail. Appeals to informed consent and mechanisms for minimizing the resulting harm are important for policy, but informed consent is problematic and mechanisms for minimization of harm do not address the dilemma. Appeals to some sort of contract model of justification are promising and illuminating.
With both the cost and quality of health care under scrutiny, many in the health care industry have turned to outcomes research and practice guidelines for answers. But many physicians have resisted, claiming their clinical judgment is a better guide. Both camps may be right.
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