%0 Journal Article
%T A survey of statistics in three UK general practice journal
%A Alan S Rigby
%A Gillian K Armstrong
%A Michael J Campbell
%A Nick Summerton
%J BMC Medical Research Methodology
%D 2004
%I BioMed Central
%R 10.1186/1471-2288-4-28
%X Hand search of three UK journals of general practice namely the British Medical Journal (general practice section), British Journal of General Practice and Family Practice over a one-year period (1 January to 31 December 2000).A wide variety of statistical techniques were used. The most common methods included t-tests and Chi-squared tests. There were few articles reporting likelihood ratios and other useful diagnostic methods. There was evidence that the journals with the more thorough statistical review process reported a more complex and wider variety of statistical techniques.The BMJ had a wider range and greater diversity of statistical methods than the other two journals. However, in all three journals there was a dearth of papers reflecting the diagnostic process. Across all three journals there were relatively few papers describing randomised controlled trials thus recognising the difficulty of implementing this design in general practice."Diagnosis is the keystone of good medical practice"[1]General practitioners (GPs) are primarily diagnosticians [2] yet it appears that diagnosis remains their Achilles heel[3]. The problem has its origins in a misunderstanding of the differences of the five Ps (patients, pathologies, presentations, prevalences and predictive values) in hospital practice compared to primary care[4]. Decisions made by GPs are different from those made by hospital clinicians. The precise diagnostic labels may be less important than deciding on an appropriate course of action. Hence, diagnoses are often framed in terms of binary decisions; treatment versus non-treatment, disease versus non-disease, referral versus non-referral, and serious versus non-serious for example[4].From a statistical viewpoint the binary decision making process has a lot of appeal. For example, the use of the na£żve Bayes' discriminant function (and from it the derivation of likelihood ratios) is appropriate. Proponents of Bayes' argue for its simplicity and ease of int
%U http://www.biomedcentral.com/1471-2288/4/28