Clinical Diagnosis by Likelihood Triangle

Differing medical conditions often exhibit similar symptoms. Rasch measures can assist clinicians by portraying the relative likelihoods of diagnoses based on observed symptom severity.

Suppose three types of injury produce similar symptoms but with different intensities. Samples of patients identified to have each type of injury are used to calibrate the symptoms on the relevant variable of injury severity. This defines three variables, gives each symptom three calibrations, and produces three raw-score-to-measure tables, one for each diagnosis.

When a new patient comes in for diagnosis, a rating of the severity of each symptom is recorded. The sum of these ratings, the patient's raw score, produces three measures, (Bj, Bk and Bl), one for each of the diagnoses, j, k, and l.

From these measures, the likelihood of the observed pattern of symptom severities is estimated for each diagnosis.

Diagj = Prod(Pji(Xi)), for i=1 to L, is the likelihood of diagnosis j. Pji(Xi) is the usual Rasch probability of the patient's observed severity level Xi, on symptom i for diagnosis j, when the patient's overall measure is Bj.

The likelihood of each diagnosis is plotted to locate the patient among the diagnoses. The plot is an equilateral triangle, in which the distance from the side opposite the diagnostic vertex is proportional to the likelihood of that diagnosis. This locates the profile of symptom severities in the plane of the three diagnoses, nearer the more likely diagnoses.

The vertices of the equilateral triangle, the locations of the diagnoses, are at (0,0) for diagnosis l, (m,0) for diagnosis k, and (m/2, msqrt((3/4)) for diagnosis j, where m is a chosen to give a conveniently sized triangle. The location of the central profile point, *, is ( m(Diagk + Diagj/2)/(Diagj + Diagk + Diagl), mDiagj(sqrt((3/4))/(Diagj + Diagk + Diagl) ).

If diagnosis j is most probable, with likelihood 0.3, and diagnosis k is 0.2, diagnosis l is a least probable 0.1, the vertices are at (0,0) (1,0) and (0.5, 0.87) when m=1. The profile location is (0.58, 0.43). This shows a location nearer j than k and far from l, enabling the clinician to select at a glance an efficient screening procedure for identifying the correct diagnosis.

Clinical diagnosis by likelihood triangle. Granger CV. … Rasch Measurement Transactions, 1994, 8:2 p.357

Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn. George Engelhard, Jr. & Jue Wang	Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene	Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver	Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone	Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes	Statistical Analyses for Language Testers (Facets), Rita Green	Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind	Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M	Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind	Rasch Measurement: Applications, Khine	Winsteps Tutorials - free Facets Tutorials - free	Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre	Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
Other Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free	An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse	Rasch Measurement Theory Analysis in R, Wind, Hua	Applying the Rasch Model in Social Sciences Using R, Lamprianou	El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar	Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch	Rasch Models for Measurement, David Andrich	Constructing Measures, Mark Wilson	Best Test Design - free, Wright & Stone Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias	Diseño de Mejores Pruebas - free, Spanish Best Test Design	A Course in Rasch Measurement Theory, Andrich, Marais	Rasch Models in Health, Christensen, Kreiner, Mesba	Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen

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