Therapeutic inferences for individual patients

Flores, Luis J.

doi:10.1111/jep.12293

Cited by 6 publications

(5 citation statements)

References 61 publications

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“…As the problem goes, a member of a population is also a member of many subgroups of that population in which the probabilities might differ. For practical purposes, one response makes intuitive sense: we should make decisions based on the probability in the narrowest informative reference class for which we can form a reasonable probability judgement (Flores, 2015). 19 If your high CVD risk patient is a member of the subgroup of high-risk patients that currently report crushing chest pain, then you are well advised to act based on the probability of a heart attack in this more narrowly defined group.…”

Section: The Trouble With the Particularizationmentioning

confidence: 99%

The Risk GP Model: The standard model of prediction in medicine

Fuller

Flores

2015

Studies in History and Philosophy of Science Part C: Studies in

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a b s t r a c tWith 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 measuresdmost commonly, risk measuresdto 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 GeneralizationeParticularization (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.

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Section: The Trouble With the Particularizationmentioning

confidence: 99%

The Risk GP Model: The standard model of prediction in medicine

Fuller

Flores

2015

Studies in History and Philosophy of Science Part C: Studies in

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“…Generalizability and transportability are not guaranteed and should be considered explicitly (66, 67). While reliable estimates are key for improving clinical care, estimates relevant to patients are likewise worth pursuing (69).…”

Section: Discussionmentioning

confidence: 99%

Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research

Lesko

Henderson

Varadhan

2018

Journal of Clinical Epidemiology

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When baseline risk of an outcome varies within a population, the effect of a treatment on that outcome will vary on at least one scale (e.g., additive, multiplicative). This treatment effect heterogeneity is of interest in patient-centered outcomes research. Based on a literature review and solicited expert opinion, we assert the following: (1) Treatment effect heterogeneity on the additive scale is most interpretable to health-care providers and patients using effect estimates to guide treatment decision-making; heterogeneity reported on the multiplicative scale may be misleading as to the magnitude or direction of a substantively important interaction. (2) The additive scale may give clues about sufficient-cause interaction, although such interaction is typically not relevant to patients' treatment choices. (3) Statistical modeling need not be conducted on the same scale as results are communicated. (4) Statistical testing is one tool for investigations, provided important subgroups are identified a priori, but test results should be interpreted cautiously given nonequivalence of statistical and clinical significance. (5) Qualitative interactions should be evaluated in a prespecified manner for important subgroups. Principled analytic plans that take into account the purpose of investigation of treatment effect heterogeneity are likely to yield more useful results for guiding treatment decisions.

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“…In the biomedical framework, the statistical average of the relevant subgroup is supposed to apply to any individual case, although there is always a possible variation in the causal context within the population. , , This problem is related to the question of external validity, ie the extrapolation of results in one population onto another population. This problem is because of the different contexts of the populations , .…”

Section: Inherent Values In the Biomedical Framework And Implicationsmentioning

confidence: 99%

Suffering without a medical diagnosis. A critical view on the biomedical attitudes towards persons suffering from burnout and the implications for medical care

Engebretsen

2018

Evaluation Clinical Practice

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The search for causal explanations in medically unexplained syndromes such as burnout has not been resolved by evidence-based medicine. A biomedical model encourages a reductionist diagnostic practice and a dualist split between physical and psychological symptoms. Therefore, diagnosing and treating these syndromes remains a challenge. Depression is a common aspect in burnout and, as a result, clinicians often diagnose burnout patients as depressed. The Norwegian government expects medical efficiency to reduce sick leaves. Medically treating depression has a documented effect. This practice may pose threats to the increasing number of individuals experiencing burnout. The clinical guidelines in evidence-based medicine mirror what counts as knowledge in medical inquiry, which in turn shapes attitudes towards individual patients. The aim of this article is 2-fold: firstly, to assess how the values that accompany the biomedical paradigm affect clinical care, and secondly, to replace the biomedical model with a genuine person-centred approach. In the study described, an existential phenomenological method was applied. Eight individuals, who experienced burnout, were included. They had been on long-term sick leave (>1 year) due to symptoms of fatigue and pain and fulfilled the criteria for Exhaustion Disorder (ICD-10, F43.8A). Their symptoms were not medically explained, and almost all the participants were labelled as depressed. Four themes emerged that described how they experienced living with burnout: "unhomelike being in the world," "the limit of diagnosis," "naked in the eyes of the public," and "a path to hopelessness." I identify 2 main problems; firstly, the mismatch between the patient's experience of his or her illness and the doctor's interpretation of the condition can lead to ineffective treatment. Secondly, the interviewees struggled to be recognized as ill. Thus, the inherent values in the biomedical paradigm might have serious implications for the medical care of patients with burnout.

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Therapeutic inferences for individual patients

Abstract: Clinicians should generally avoid unreflective extrapolations from research and address, as explicitly as possible, the challenge of estimating probabilities for individual patients. A key element of this task is the integration of data from research and non-research sources.

Cited by 6 publications

References 61 publications

The Risk GP Model: The standard model of prediction in medicine

The Risk GP Model: The standard model of prediction in medicine

Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research

Suffering without a medical diagnosis. A critical view on the biomedical attitudes towards persons suffering from burnout and the implications for medical care

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