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.