It is sometimes thought that randomized study group allocation is uniquely proficient at producing comparison groups that are evenly balanced for all confounding causes. Philosophers have argued that in real randomized controlled trials this balance assumption typically fails. But is the balance assumption an important ideal? I run a thought experiment, the CONFOUND study, to answer this question. I then suggest a new account of causal inference in ideal and real comparative group studies that helps clarify the roles of confounding (...) variables and randomization. _1_ Confounders and Causes _2_ The Balance Assumption _3_ The CONFOUND Study _3.1_ CONFOUND 1 _3.2_ CONFOUND 2 _4_ Disjunction C and the Ideal Study _4.1_ The ultimate ‘other cause’: C _4.2_ The ideal comparative group study _4.3_ Required conditions for causal inference _5_ Confounders as Causes, Confounders as Correlates _6_ Summary. (shrink)

Introduction: Clinical practice guidelines (CPGs) are an important source of justification for clinical decisions in modern evidence-based practice. Yet, we have given little attention to how they argue their evidence. In particular, how do CPGs argue for treatment with long-term medications that are increasingly prescribed to older patients? Approach and rationale: I selected six disease-specific guidelines recommending treatment with five of the medication classes most commonly prescribed for seniors in Ontario, Canada. I considered the stated aims of these CPGs and (...) the techniques employed towards those aims. Finally, I reconstructed and logically analysed the arguments supporting recommendations for pharmacotherapy. Analysis: The primary function of CPGs is rhetorical, or persuasive, and their means of persuasion include both a display of their credibility and their argumentation. Arguments supporting pharmacotherapy recommendations for the target population follow a common inductive pattern: statistical generalization from randomized controlled trial (RCT) and meta-analysis evidence. Two of the CPGs also argue their treatment recommendations for older patients in this style, while three fail to justify pharmacotherapy specifically for the older population. Discussion: The arguments analysed lack the auxiliary assumptions that would warrant making a generalization about the clinical effectiveness of medications for the older population. Guidelines reason using simple induction, while ignoring important inferential gaps. Future guidelines should aspire to be well-reasoned rather than simply evidence-based; argue from a plurality of evidence; be wary of hasty inductions; appropriately limit the scope of their recommendations; and avoid making law-like, prescriptive generalizations. (shrink)

What kind of a thing are chronic diseases? Are they objects, bundles of signs and symptoms, properties, processes, or fictions? Rather than using concept analysis—the standard approach to disease in the philosophy of medicine—to answer this metaphysical question, I use a bottom-up, inductive approach. I argue that chronic diseases are bodily states or properties—often dispositional, but sometimes categorical. I also investigate the nature of related pathological entities: pathogenesis, etiology, and signs and symptoms. Finally, I defend my view against alternate accounts (...) of the nature of disease. (shrink)

Over the past several decades, we devoted much energy to generating, reviewing and summarizing evidence. We have given far less attention to the issue of how to thoughtfully apply the evidence once we have it. That’s fine if all we care about is that our clinical decisions are evidence-based, but not so good if we also want them to be well-reasoned. Let us not forget that evidence based medicine (EBM) grew out of an interest in making medicine ‘rational’, with the (...) idea that rational clinical evaluations should be evidence-based. I agree with the uncontroversial statement that the best decision is supported, at least in part, by the best available evidence. Rationality, however, is constituted by reasoning, not evidence. Complete arguments are necessary for rational evaluations, arguments that begin with general evidence and end in a conclusion about a particular patient. In order to traverse these inferential gaps, medicine must address the issue of how to establish, as an intermediate premise, what the evidence has to say about the efficacy of an intervention for particular patients in a particular practice setting. (shrink)

The new field of meta-research investigates industry bias, publication bias, contradictions between studies, and other trends in medical research. I argue that its findings should be used as meta-evidence for evaluating therapies. ‘Meta-evidence’ is evidence about the support that direct ‘first-order evidence’ provides the hypothesis. I consider three objections to my proposal: the irrelevance objection, the screening-off objection, and the underdetermination objection. I argue that meta-research evidence works by rationally revising our confidence in first-order evidence and, consequently, in the hypothesis—typically, (...) downward. (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target (...) population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)