title: New approaches to enhance the accuracy of the diagnosis of reflux disease.
abstract: The accuracy of symptoms in diagnosing gastro-oesophageal reflux disease (GORD) is complicated by the lack of a gold standard test. Statistical techniques such as latent class and Bayesian analyses can estimate accuracy of symptoms without a gold standard. Both techniques require three independent diagnostic tests. Latent class analysis makes no assumptions about the performance of the tests. Bayesian analysis is useful when the accuracy of the other tests is known. These statistical techniques should be used in the future to validate GORD symptom questionnaires comparing them with endoscopy, oesophageal pH monitoring, and response to proton pump inhibitor therapy. Studies that evaluate GORD symptoms are usually done in secondary care. The prevalence of GORD in primary care will be lower and this reduces the positive predictive value of symptoms. There will be some bias in the type of patient referred for diagnosis and this usually decreases the specificity of symptom diagnosis.
authors.affiliation: Gastroenterology Division, McMaster University, Ontario, Canada. evansl@mcmaster.ca
authors.forename: P
authors.initials: P
authors.lastname: Moayyedi
authors.forename: J
authors.initials: J
authors.lastname: Duffy
authors.forename: B
authors.initials: B
authors.lastname: Delaney
mesh_terms: D001499:Bayes Theorem; D005764:Gastroesophageal Reflux / Q000175:diagnosis*; D006801:Humans; D012015:Reference Standards; D012044:Regression Analysis; D012680:Sensitivity and Specificity