Why clinicians are natural bayesians

Gill, Christopher J.; Sabin, Lora; Schmid, Christopher H.

doi:10.1136/bmj.330.7499.1080

Cited by 209 publications

(149 citation statements)

References 11 publications

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“…In this sense, our findings may reflect the Bayesian processes inherent in many clinical decisions. 41 These are areas that deserve further research and may point the way to finding an effective role for screening instruments in nonspecialist settings.…”

Section: Discussionmentioning

confidence: 99%

Screening and case-finding instruments for depression: a meta-analysis

Gilbody¹,

Sheldon²,

House³

2008

Canadian Medical Association Journal

370

275

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Section: Discussionmentioning

confidence: 99%

Screening and case-finding instruments for depression: a meta-analysis

Gilbody¹,

Sheldon²,

House³

2008

Canadian Medical Association Journal

370

275

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“…Moreover, two studies conducted among medical students suggested that traditional teaching of Bayesian reasoning may not be effective enough in improving diagnostic performance 18,20 . Conversely, Bayesian skills might be acquired through clinical experience, i.e., through regular exposure to the interpretation of diagnostic tests 3,21,22 . However, we found that more experienced physicians did not perform better.…”

Section: Interpretation Of the Results And Implicationsmentioning

confidence: 99%

Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial

et al. 2010

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BACKGROUND: The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE:To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN: Randomized trial. PARTICIPANTS: Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION: We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES: We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60-79%, 80-94%, 95-99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the "no information" scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS: Most respondents in each of the six groups (67%-82%) selected a post-test probability of 95-99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0-14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS: Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors.KEY WORDS: Bayes' theorem; predictive value of tests; prevalence; sensitivity and specificity; diagnosis; risk assessment; probability; evidence-based medicine.

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“…Indeed, the choice of pretest probabilities in clinical settings may be inaccurate and vary widely (18)(19)(20). However, this uncertainty in pretest probability represents no obstacle for clinicians, once they use their intuition based on their experience, as they are natural Bayesians (6,14).…”

Section: Discussionmentioning

confidence: 99%

“…The Bayesian approach provides a kind of probabilistic reasoning where the probability of occurrence of an event is obtained conditionally on the knowledge of the occurrence of an earlier event (6,7). Thus, the Bayesian approach basically consists of three steps: (a) determining a prior estimate of the parameter of interest based on former knowledge and previous experiences; (b) finding an appropriate likelihood function or a mathematical expression that describes the distribution of the observed data; and (c) combining the prior distribution with the likelihood function using the Bayes' theorem, providing the posterior distribution of the parameter.…”

Section: Introductionmentioning

confidence: 99%

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective

Cipoli

Martinez

Castro

et al. 2012

Arq Bras Endocrinol Metab

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Objective: To estimate the pretest probability of Cushing's syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. Materials and methods: Physicians were requested, in seven endocrinology meetings, to answer three questions: "Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your probability of diagnosing Cushing's Syndrome?"; "For how long have you been practicing Endocrinology?"; and "Where do you work?" . A Bayesian beta regression, using the WinBugs software was employed. Results: We obtained 294 questionnaires. The mean pretest probability of CS diagnosis was 51.6% (95%CI: 48.7-54.3). The probability was directly related to experience in endocrinology, but not with the place of work. Conclusion: Pretest probability of CS diagnosis was estimated using a Bayesian methodology. Although pretest likelihood can be context-dependent, experience based on years of practice may help the practitioner to diagnosis CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7 Keywords Cushing's syndrome; Bayes theorem; pretest probability RESUMO Objetivo: Estimar a probabilidade pré-teste do diagnóstico de síndrome de Cushing (SC) por meio de julgamento clínico utilizando abordagem Bayesiana. Materiais e métodos: Médicos responderam a três perguntas, em sete congressos de endocrinologia. Após obtenção da história clínica/exame físico, sem exames laboratoriais, apenas com base em sua experiência pessoal, qual a probabilidade de diagnosticar SC?; Há quanto tempo você pratica endocrinologia?; Onde você trabalha? Uma regressão beta Bayesiana, utilizando o software WinBugs, foi empregada. Resultados: Foram obtidos 294 questionários. A estimativa Bayesiana da probabilidade média de diagnosticar SC foi 51,6% (IC 95%: 48,3). Houve relação direta entre probabilidade de diagnosticar SC e experiência da prática endócrina, porém não com o local de trabalho. Conclusão: A probabilidade pré-teste do diagnóstico de SC foi estimada utilizando uma metodologia Bayesiana. Embora a probabilidade pré-teste possa ser dependente do contexto, a experiência de anos de prática pode auxiliar no diagnóstico intuitivo da CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7 Descritores Síndrome de Cushing; teorema de Bayes; probabilidade pré-teste

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Why clinicians are natural bayesians

Cited by 209 publications

References 11 publications

Screening and case-finding instruments for depression: a meta-analysis

Screening and case-finding instruments for depression: a meta-analysis

Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective

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