Online research in philosophy

What kinds of evidence reliably support predictions of effectiveness for health and social care interventions? There is increasing reliance, not only for health care policy and practice but also for more general social and economic policy deliberation, on evidence that comes from studies whose basic logic is that of JS Mill's method of difference. These include randomized controlled trials, case–control studies, cohort studies, and some uses of causal Bayes nets and counterfactual-licensing models like ones commonly developed in econometrics. The topic of this paper is the 'external validity' of causal conclusions from these kinds of studies. We shall argue two claims. Claim, negative: external validity is the wrong idea; claim, positive: 'capacities' are almost always the right idea, if there is a right idea to be had. If we are right about these claims, it makes big problems for policy decisions. Many advice guides for grading policy predictions give top grades to a proposed policy if it has two good Mill's-method-of difference studies that support it. But if capacities are to serve as the conduit for support from a method-of-difference study to an effectiveness prediction, much more evidence, and much different in kind, is required. We will illustrate the complexities involved with the case of multisystemic therapy, an internationally adopted intervention to try to diminish antisocial behaviour in young people.

This essay explores some concerns about the quality of informed consent in patients whose autonomy is diminished by fatal illness. It argues that patients with diminished autonomy cannot give free and voluntary consent, and that recruitment of such patients as subjects in human experimentation exploits their vulnerability in a morally objectionable way. Two options are given to overcome this objection: (i) recruit only those patients who desire to contribute to medical knowledge, rather than gain access to experimental treatment, or (ii) provide prospective subjects the choice to participate in standard doubleblind study or receive the experimental treatment. Either option would guarantee that patients in desperate conditions are given a more meaningful choice and a richer freedom, and thus a higher quality of informed consent, than under standard randomized trials. Keywords: autonomy, double-blind trials, prerandomized and randomized trials, informed consent CiteULike Connotea Del.icio.us What's this?

The principle of clinical equipoise requires that, aside from certain exceptional cases, second generation treatments ought to be tested against standard therapy. In violation of this principle, placebo-controlled trials (PCTs) continue to be used extensively in the development and licensure of second-generation treatments. This practice is typically justified by appeal to methodological arguments that purport to demonstrate that active-controlled trials (ACTs) are methodologically flawed. Foremost among these arguments is the so called assay sensitivity argument. In this paper, I take a closer look at this argument. Following Duhem, I argue that all trials, placebo-controlled or not, rely on external information for their meaningful interpretation. Pending non-circular empirical evidence that we can trust the findings of PCTs to a greater degree than the findings of ACTs, I conclude that the assay sensitivity argument fails to demonstrate that placebo-controlled trials are preferable, methodologically or otherwise, to active-controlled trials. Contrary to the intentions of its authors, the fundamental lesson taught by the assay sensitivity argument is Duhemian: the validity of all clinical trials depends on external information.

The current controversy as to the proper role of the placebo control in the evaluation of new treatments for schizophrenia requires an analysis that is sensitive to both ethical and scientific issues. Clinical equipoise, widely regarded as the moral foundation of the randomized controlled trial (RCT), requires the use of best available treatment as the control in RCT. Scientific criticisms of the use of an active control are examined and none present an insuperable barrier to the use of an active control. Indeed, scrutiny of the most recent argument for the use of placebo controls, 'assay sensitivity', suggests that the use of placebo may be the cause of the problem pointed to. Scientific, regulatory, ethical and legal advantages of the use of an active control are described. While the use of a placebo control may be acceptable in carefully defined circumstances, in most cases the use of an active control in schizophrenia research is ethically and scientifically preferable.

Randomized controlled trials (RCTs) are widely taken as the gold standard for establishing causal conclusions. Ideally conducted they ensure that the treatment ‘causes’ the outcome—in the experiment. But where else? This is the venerable question of external validity. I point out that the question comes in two importantly different forms: Is the specific causal conclusion warranted by the experiment true in a target situation? What will be the result of implementing the treatment there? This paper explains how the probabilistic theory of causality implies that RCTs can establish causal conclusions and thereby provides an account of what exactly that causal conclusion is. Clarifying the exact form of the conclusion shows just what is necessary for it to hold in a new setting and also how much more is needed to see what the actual outcome would be there were the treatment implemented.

Nancy is ultimately most concerned about how to determine the relevance of evidence to implementation of evidence-based policy guidelines, in other words, the transferability of study results to a population different from the one that was studied and in which procedures or conditions are not the same as those in the study. And she is concerned about the privileged position Randomized Controlled Trials (RCTs) are given in the ranking schemes for evidence-based policy, because as she sees it RCTs do not address this question while other methods do. RCTs are highly regarded because of their strength in ruling out confounding variables, but they can be weak on the transferability problem because the manipulations necessary for controlled experiment also guarantee that the setting and population are different from the situation and population targeted for application. However, both of these familiar points are simplifications that can be misleading. Some non-RCT type studies (e.g. soft interventions) can also be very good at ruling out confounding variables.1 And, as I will explain, the problems leading to the transferability problem – interacting variables and a difference between study and target populations – are present in any study, not unique to RCTs. In addition.