Likelihood ratios: A real improvement for clinical decision making?

Dujardin, Bruno; Ende, Jef Van den; Gompel, Alfons Van; Unger, Jean-Pierre; Stuyft, Patrick Van der

doi:10.1007/bf01717448

Cited by 107 publications

(89 citation statements)

References 19 publications

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“…To remove the difficulty arising from interpretation of predictive values decision analysts have suggested an alternative method to assess the predictive properties of a test: the likelihood ratio [10,13,14,15]. Conceptually the likelihood ratio is the ratio of two probabilities, namely the probability that a specific test result is obtained in patients with the disease divided by the probability of obtaining the same test result in patients without the disease.…”

Section: Measures Of Test Accuracymentioning

confidence: 99%

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

Fischer

Bachmann²,

Jaeschke

2003

Intensive Care Medicine

763

568

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Background: One of the most challenging practical and daily problems in intensive care medicine is the interpretation of the results from diagnostic tests. In neonatology and pediatric intensive care the early diagnosis of potentially life-threatening infections is a particularly important issue. Focus: A plethora of tests have been suggested to improve diagnostic decision making in the clinical setting of infection which is a clinical example used in this article. Several criteria that are critical to evidence-based appraisal of published data are often not adhered to during the study or in reporting. To enhance the critical appraisal on articles on diagnostic tests we discuss various measures of test accuracy: sensitivity, specificity, receiver operating characteristic curves, positive and negative predictive values, likelihood ratios, pretest probability, posttest probability, and diagnostic odds ratio. Conclusions: We suggest the following minimal requirements for reporting on the diagnostic accuracy of tests: a plot of the raw data, multilevel likelihood ratios, the area under the receiver operating characteristic curve, and the cutoff yielding the highest discriminative ability. For critical appraisal it is mandatory to report confidence intervals for each of these measures. Moreover, to allow comparison to the readers' patient population authors should provide data on study population characteristics, in particular on the spectrum of diseases and illness severity.

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Section: Measures Of Test Accuracymentioning

confidence: 99%

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

Fischer

Bachmann²,

Jaeschke

2003

Intensive Care Medicine

763

568

View full text Add to dashboard Cite

show abstract

“…15 The prognostic strength of each variable and model was assessed by calculating the number of correctly classified patients, sensitivity and specificity, positive predictive value (PPV) and negative predictive value (NPV), 16 and positive likelihood ratio (PLR) defined as sensitivity/ (1-specificity) and (1-sensitivity)/specificity, respectively. [17][18][19] Kaplan-Meier estimates of the survival function were calculated and plotted in relation to the number of risk factors. Patients with at least 3 risk factors were identified as having the poorest prognosis.…”

Section: Statisticsmentioning

confidence: 99%

A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern

Carneiro

Bendahl

Engellau

et al. 2010

Cancer

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BACKGROUND:In soft tissue sarcoma, better distinction of high-risk and low-risk patients is needed to individualize treatment and improve survival. Prognostic systems used in clinical practice identify high-risk patients based on various factors, including age, tumor size and depth, histological type, necrosis, and grade. METHODS: Whole-tumor sections from 239 soft tissue sarcomas of the extremities were reviewed for the following prognostic factors: size, vascular invasion, necrosis, and growth pattern. A new prognostic model, referred to as SING (Size, Invasion, Necrosis, Growth), was established and compared with other clinically applied systems. RESULTS: Size, vascular invasion, necrosis, and peripheral tumor growth pattern provided independent prognostic information with hazard ratios of 2.2-2.6 for development of metastases in multivariate analysis. When these factors were combined into the prognostic model SING, high risk of metastasis was predicted with a sensitivity of 74% and a specificity of 85%. Moreover, the prognostic performance of SING compared favorably with other widely used systems. CONCLUSIONS: SING represents a promising prognostic model, and vascular invasion and tumor growth pattern should be considered in soft tissue sarcoma prognostication.

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“…Instead, we look for relative proofs of the presence or absence of disease, represented by their positive and negative likelihood ratios. The two combined constitute the odds ratio, which gives a fair representation of the overall discriminatory value of a sign or symptom (Dujardin et al 1994). Odds ratios and likelihood ratios are determined on the basis of cases and controls who belong to the same population, e.g.…”

Section: Clinical and Biological Manifestations Of Denguementioning

confidence: 99%

Erratum

1999

Tropical Med Int Health

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Likelihood ratios: A real improvement for clinical decision making?

Cited by 107 publications

References 19 publications

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern

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