Working Paper Article Version 1 This version is not peer-reviewed

Statistical Methods to Support Difficult Diagnoses

Version 1 : Received: 31 May 2021 / Approved: 2 June 2021 / Online: 2 June 2021 (12:14:34 CEST)

A peer-reviewed article of this Preprint also exists.

Pilz, G.F.; Weber, F.; Mueller, W.G.; Schaefer, J.R. Statistical Methods to Support Difficult Diagnoses. Diagnostics 2021, 11, 1300. Pilz, G.F.; Weber, F.; Mueller, W.G.; Schaefer, J.R. Statistical Methods to Support Difficult Diagnoses. Diagnostics 2021, 11, 1300.

Journal reference: Diagnostics 2021, 11, 1300
DOI: 10.3390/diagnostics11071300

Abstract

Far too often, one meets patients who went for years or even decades from doctor to doctor, without getting a valid diagnosis. This brings pain to millions of patients and their families, not to speak of the enormous costs. Often patients cannot tell precisely enough which factors (or combinations thereof) trigger their problems. If conventional methods fail, we propose the use of statistics and algebra to give doctors much more useful inputs from patients. We use statistical regression for independent triggering factors for medical problems, and “balanced incomplete block designs” for non-independent factors. These methods can supply doctors with much more valuable inputs, and can also detect combinations of multiple factors by incredibly few tests. In order to show that these methods do work, we briefly describe a case in which these methods helped to solve a 60 year old problem in a patient, and give some more examples where these methods might be very useful. As a conclusion, while regression is used in clinical medicine, it seems to be widely unknown in diagnosing. Statistics and algebra can save the health systems much money, and the patients also a lot of pain.

Keywords

Diagnosing designs; rare diseases; statistics; regression; block designs

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