Literature DB >> 18059094

Statistics in biomedical laboratory and clinical science: applications, issues and pitfalls.

John Ludbrook1.   

Abstract

This review is directed at biomedical scientists who want to gain a better understanding of statistics: what tests to use, when, and why. In my view, even during the planning stage of a study it is very important to seek the advice of a qualified biostatistician. When designing and analyzing a study, it is important to construct and test global hypotheses, rather than to make multiple tests on the data. If the latter cannot be avoided, it is essential to control the risk of making false-positive inferences by applying multiple comparison procedures. For comparing two means or two proportions, it is best to use exact permutation tests rather then the better known, classical, ones. For comparing many means, analysis of variance, often of a complex type, is the most powerful approach. The correlation coefficient should never be used to compare the performances of two methods of measurement, or two measures, because it does not detect bias. Instead the Altman-Bland method of differences or least-products linear regression analysis should be preferred. Finally, the educational value to investigators of interaction with a biostatistician, before, during and after a study, cannot be overemphasized. (c) 2007 S. Karger AG, Basel.

Mesh:

Year:  2008        PMID: 18059094     DOI: 10.1159/000109583

Source DB:  PubMed          Journal:  Med Princ Pract        ISSN: 1011-7571            Impact factor:   1.927


  8 in total

1.  Accuracy of linear intraoral measurements using cone beam CT and multidetector CT: methodological mistake: author response.

Authors:  R Patcas
Journal:  Dentomaxillofac Radiol       Date:  2013-02-14       Impact factor: 2.419

2.  The chicken-and-egg debate about statistics and research.

Authors:  Luca Bertolaccini; Andrea Viti; Alberto Terzi
Journal:  J Thorac Dis       Date:  2014-09       Impact factor: 2.895

3.  Quantification of [18F]UCB-H Binding in the Rat Brain: From Kinetic Modelling to Standardised Uptake Value.

Authors:  Maria Elisa Serrano; Mohamed Ali Bahri; Guillaume Becker; Alain Seret; Frédéric Mievis; Fabrice Giacomelli; Christian Lemaire; Eric Salmon; André Luxen; Alain Plenevaux
Journal:  Mol Imaging Biol       Date:  2019-10       Impact factor: 3.488

4.  Genetic linkage of oxidative stress with cardiometabolic traits in an intercross derived from hyperlipidemic mouse strains.

Authors:  Daniela T Fuller; Andrew T Grainger; Ani Manichaikul; Weibin Shi
Journal:  Atherosclerosis       Date:  2019-12-03       Impact factor: 5.162

5.  Whole blood viscosity assessment issues III: Association with international normalized ratio and thrombocytopenia.

Authors:  Ezekiel Uba Nwose; Nathan Cann; Eugene Butkowski
Journal:  N Am J Med Sci       Date:  2010-07

6.  Whole blood viscosity assessment issues V: Prevalence in hypercreatinaemia, hyperglycaemia and hyperlipidaemia.

Authors:  Ezekiel Uba Nwose
Journal:  N Am J Med Sci       Date:  2010-09

7.  Basic biostatistics for post-graduate students.

Authors:  Ganesh N Dakhale; Sachin K Hiware; Abhijit T Shinde; Mohini S Mahatme
Journal:  Indian J Pharmacol       Date:  2012 Jul-Aug       Impact factor: 1.200

8.  Expression of VEGF-A, Otx homeobox and p53 family genes in proliferative vitreoretinopathy.

Authors:  Claudio Azzolini; Ilaria Stefania Pagani; Cristina Pirrone; Davide Borroni; Simone Donati; Muna Al Oum; Diana Pigni; Anna Maria Chiaravalli; Riccardo Vinciguerra; Francesca Simonelli; Giovanni Porta
Journal:  Mediators Inflamm       Date:  2013-10-21       Impact factor: 4.711

  8 in total

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