Using Bayes’ rule in diagnostic testing: a graphical explanation

Johnson, Kevin

doi:10.1515/dx-2017-0011

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…Therefore, a provocative test should still represent a mandatory step for GHD diagnosis in most cases; our model, however, could be of significant help in clinical practice for the identification of false-positive and/or falsenegative stimulation test results. In fact, by Bayes theorem (29)(30)(31), when applying a diagnostic test characterized by approximately 90% sensitivity and specificity, if the pre-test probability of a disease or condition is < 25%, the post-test probability still remains < 75% after a positive test result. Conversely, if the pre-test probability is > 75%, the post-test probability still remains > 25% after a negative test result.…”

Section: Discussionmentioning

confidence: 99%

“…In light of this, the results of a stimulation test should not be interpreted as an unquestionable dichotomous answer on patient's diagnosis. On the contrary, it should be encouraged to think to the process of GHD diagnosis through a Bayesian approach, in which any additional test result modifies (upward or downward) the probability that a given patient has GHD, as already discussed and suggested for many other medical and endocrinological conditions (29)(30)(31)(32)(33). This Bayesian approach to the GHD diagnostic work-up is methodologically well-grounded and allows a more efficient handling and interpretation of stimulation test results, but poses a great problem, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests

et al. 2021

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BackgroundThe diagnosis of adult GH deficiency (GHD) relies on a reduced GH response to provocative tests. Their diagnostic accuracy, however, is not perfect, and a reliable estimation of pre-test GHD probability could be helpful for a better interpretation of their results.MethodsEighty patients showing concordant GH response to two provocative tests, i.e. the insulin tolerance test and the GHRH + arginine test, were enrolled. Data on IGF-I values and on the presence/absence of other pituitary deficits were collected and integrated for the estimation of GHD probability prior to stimulation tests.ResultsAn independent statistically significant association with the diagnosis of GHD was found both for IGF-I SDS (OR 0.34, 95%-CI 0.18-0.65, p=0.001) and for the presence of other pituitary deficits (OR 6.55, 95%-CI 2.06-20.83, p=0.001). A low (<25%) pre-test GHD probability could be predicted when IGF-I SDS > +0.91 in the presence of other pituitary deficits or IGF-I SDS > -0.52 in the absence of other pituitary deficits. A high (>75%) pre-test GHD probability could be predicted when IGF-I SDS < -0.82 in the presence of other pituitary deficits or IGF-I SDS < -2.26 in the absence of other pituitary deficits.ConclusionThis is the first study that proposes a quantitative estimation of GHD probability prior to stimulation tests. Our risk class stratification represents a simple tool that could be adopted for a Bayesian interpretation of stimulation test results, selecting patients who may benefit from a second stimulation test and possibly reducing the risk of wrong GHD diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests

et al. 2021

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“…Bayes described the relationship between the posterior probability of an outcome conditional on an exposure based on the unconditional prior probability and an evidence factor. This can be restated in terms of the posterior odds of an outcome (D) conditional on an exposure (E ) based on the population-level prior odds and an evidence factor (likelihood ratio) [21,22]. The latter can be given by the following expression: and we will subsequently use E to denote nonexposure as well as D to denote the absence of the outcome.…”

Section: Derivation From First Principlesmentioning

confidence: 99%

Controversy and Debate: Questionable utility of the relative risk in clinical research: Paper 1: A call for change to practice

Doi

Furuya‐Kanamori

et al. 2022

Journal of Clinical Epidemiology

111

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Background and Objectives: In clinical trials, the relative risk or risk ratio (RR) is a mainstay of reporting of the effect magnitude for an intervention. The RR is the ratio of the probability of an outcome in an intervention group to its probability in a control group. Thus, the RR provides a measure of change in the likelihood of an event linked to a given intervention. This measure has been widely used because it is today considered a measure with ''portability'' across varying outcome prevalence, especially when the outcome is rare. It turns out, however, that there is a much more important problem with this ratio, and this paper aims to demonstrate this problem.Methods: We used mathematical derivation to determine if the RR is a measure of effect magnitude alone (i.e., a larger absolute value always indicating a stronger effect) or not. We also used the same derivation to determine its relationship to the prevalence of an outcome. We confirm the derivation results with a follow-up analysis of 140,620 trials scraped from the Cochrane.Results: We demonstrate that the RR varies for reasons other than the magnitude of the effect because it is a ratio of two posterior probabilities, both of which are dependent on baseline prevalence of an outcome. In addition, we demonstrate that the RR shifts toward its null value with increasing outcome prevalence. The shift toward the null happens regardless of the strength of the association between intervention and outcome. The odds ratio (OR), the other commonly used ratio, measures solely the effect magnitude and has no relationship to the prevalence of an outcome in a study nor does it overestimate the RR as is commonly thought.Conclusions: The results demonstrate the need to (1) end the primary use of the RR in clinical trials and meta-analyses as its direct interpretation is not meaningful, (2) replace the RR by the OR, and (3) only use the postintervention risk recalculated from the OR for any expected level of baseline risk in absolute terms for purposes of interpretation such as the number needed to treat. These results will have far-reaching implications such as reducing misleading results from clinical trials and meta-analyses and ushering in a new era in the reporting of such trials or meta-analyses in practice.

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“…11 The positive likelihood ratio is the probability of having a disease, outcome, or event if one has a positive test vs the probability of having a disease, event, or outcome when the test is negative (TP/FN) or (sensitivity/1-specificity). 11,12 And negative likelihood ratio is the probability of having an event, disease or outcome with a negative test over the probability of having a negative test in the absence of disease (FN/TN) or (1-sensitivity/specificity). 12 Therefore, likelihood ratios are ratios of 2 probabilities.…”

Section: Likelihood Ratiosmentioning

confidence: 99%

Likelihood Ratios: An Important Concept for Palliative Physicians to Understand

Davis

Soni

Strobel

2022

Am J Hosp Palliat Care

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Palliative care has several tools and questionnaires which are commonly used for patient-related outcomes and prognosis. As an example, the Surprise Question (I would or would not be surprised that this person would have died in a year) has been used as a screen for palliative care referral but also used as a prognostic tool. Diagnostic tests, prognostic tools, and tools for gauging outcomes have certain sensitivity and specificity in predicting a diagnosis or outcome. Clinicians often use positive and negative predictive values in judging the merits of a diagnostic tool or questionnaire. However positive and negative predictive values are highly dependent on the prevalence of disease or outcome in a population and thus are not portable across studies. Likelihood ratios are both portable across populations but also provide the strength of the diagnostic or predictive measure of a test or questionnaire. In this article, we review the value and limitations of likelihood ratios and illustrate the value of using likelihood ratios using 3 studies centered on the Surprise Question published in 2022.

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Using Bayes’ rule in diagnostic testing: a graphical explanation

Cited by 18 publications

References 5 publications

Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests

Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests

Controversy and Debate: Questionable utility of the relative risk in clinical research: Paper 1: A call for change to practice

Likelihood Ratios: An Important Concept for Palliative Physicians to Understand

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