I Arostegui1, J M Quintana, A Urkaregi. 1. Departamento de Matemática Aplicada, Estadística e Investigación Operativa, Universidad del País Vasco, Leioa, Bizkaia, Spain. inmaculada.arostegui@ehu.es
Abstract
OBJECTIVES: Methodology based on expert panels has been commonly used to evaluate the appropriateness of interventions. An important issue is the adequate synthesis of the generated information in an applicable way to clinical decision making. This paper shows how statistical procedures help synthesize the results of an expert panel. METHODS: Three statistical techniques were applied to an expert panel that developed explicit criteria to assess the appropriateness of total hip joint replacement: classification tree, regression tree and multiple correspondence analysis combined with automatic classification. RESULTS: Results provided by the three models were shown in graphical displays and were compared to the original panel results using crude and weighted probability of misclassification. Results were also applied to real interventions in order to know the implication of the misclassification on real patients. CONCLUSIONS: The statistical techniques help summarize data from panels of experts and provide useful decision models for clinical practice, especially when the number of indications is big. However, degree of misclassification and its implication should be taken into account.
OBJECTIVES: Methodology based on expert panels has been commonly used to evaluate the appropriateness of interventions. An important issue is the adequate synthesis of the generated information in an applicable way to clinical decision making. This paper shows how statistical procedures help synthesize the results of an expert panel. METHODS: Three statistical techniques were applied to an expert panel that developed explicit criteria to assess the appropriateness of total hip joint replacement: classification tree, regression tree and multiple correspondence analysis combined with automatic classification. RESULTS: Results provided by the three models were shown in graphical displays and were compared to the original panel results using crude and weighted probability of misclassification. Results were also applied to real interventions in order to know the implication of the misclassification on real patients. CONCLUSIONS: The statistical techniques help summarize data from panels of experts and provide useful decision models for clinical practice, especially when the number of indications is big. However, degree of misclassification and its implication should be taken into account.