In February 2010, the World Medical Association hosted an international symposium on the ethics of placebo controls in clinical trials (WMA 2010). Despite years of debate, ethicists, clinical trialists, and policy makers remain divided over the ethical acceptability of using placebos in research when a proven, effective treatment is available. The protracted nature of this problem is due, at least in part, to a perceived conflict between the opposing demands placed on clinical research by science and ethics. A good, scientifically (...) valid trial, it is argued, must be “assay sensitive,” and without using a placebo control, there can be no guarantee that it is. In this article, we revisit some of the claims made about .. (shrink)

The claims of randomized controlled trials to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This article describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive method. (...) It argues that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The article reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard. (shrink)

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. (shrink)

The claims of RCTs to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This paper describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive method. It argues (...) that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The paper reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard. (shrink)

Obviously medicine should be evidence-based. The issues lie in the details: what exactly counts as evidence? Do certain kinds of evidence carry more weight than others? (And if so why?) And how exactly should medicine be based on evidence? When it comes to these details, the evidence-based medicine (EBM) movement has got itself into a mess – or so it will be argued. In order to start to resolve this mess, we need to go 'back to basics'; and that means (...) turning to the philosophy of science. The theory of evidence, or rather the logic of the interrelations between theory and evidence, has always been central to the philosophy of science – sometimes under the alias of the 'theory of confirmation'. When taken together with a little philosophical commonsense, this logic can help us move towards a position on evidence in medicine that is more sophisticated and defensible than anything that EBM has been able so far to supply. (shrink)

Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; (...) generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions. (shrink)