Statistical methods for bioimpedance analysis

Abstract This paper gives a basic overview of relevant statistical methods for the analysis of bioimpedance measurements, with an aim to answer questions such as: How do I begin with planning an experiment? How many measurements do I need to take? How do I deal with large amounts of frequency sweep data? Which statistical test should I use, and how do I validate my results? Beginning with the hypothesis and the research design, the methodological framework for making inferences based on measurements and statistical analysis is explained. This is followed by a brief discussion on correlated measurements and data reduction before an overview is given of statistical methods for comparison of groups, factor analysis, association, regression and prediction, explained in the context of bioimpedance research. The last chapter is dedicated to the validation of a new method by different measures of performance. A flowchart is presented for selection of statistical method, and a table is given for an overview of the most important terms of performance when evaluating new measurement technology.

[1]  Thomas P. Ryan,et al.  Modern Regression Methods , 1996 .

[2]  Noor Azina Ismail,et al.  Statistical Methods Used to Test for Agreement of Medical Instruments Measuring Continuous Variables in Method Comparison Studies: A Systematic Review , 2012, PloS one.

[3]  Donald A. Jackson,et al.  How many principal components? stopping rules for determining the number of non-trivial axes revisited , 2005, Comput. Stat. Data Anal..

[4]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[5]  L. Critchley,et al.  A Meta-Analysis of Studies Using Bias and Precision Statistics to Compare Cardiac Output Measurement Techniques , 1999, Journal of Clinical Monitoring and Computing.

[6]  C. McCulloch,et al.  Ethics and sample size. , 2005, American journal of epidemiology.

[7]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[8]  A. Bulgiba,et al.  A Systematic Review of Statistical Methods Used to Test for Reliability of Medical Instruments Measuring Continuous Variables , 2013, Iranian journal of basic medical sciences.

[9]  Sverre Grimnes,et al.  Bioimpedance and Bioelectricity Basics , 2000 .

[10]  William A. Brenneman Statistics for Research (3rd ed.) , 2005 .

[11]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[12]  Luc Devroye,et al.  Distribution-free performance bounds for potential function rules , 1979, IEEE Trans. Inf. Theory.

[13]  Emanuel Schmider,et al.  Is It Really Robust , 2010 .

[14]  J. J. Higgins,et al.  An Investigation of the Type I Error and Power Properties of the Rank Transform Procedure in Factorial ANOVA , 1989 .

[15]  Lawrence Lin,et al.  Overview of Agreement Statistics for Medical Devices , 2007, Journal of biopharmaceutical statistics.

[16]  N. Buderer,et al.  Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. , 1996, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[17]  William A. Brenneman Statistics for Research , 2005, Technometrics.

[18]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[19]  Joel R. Levin,et al.  Multivariate statistics in the social sciences : a researcher's guide , 1985 .

[20]  Rupert G. Miller Simultaneous Statistical Inference , 1966 .

[21]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[22]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Abdul Ghaaliq Lalkhen,et al.  Clinical tests: sensitivity and specificity , 2008 .

[24]  D. B. Owen,et al.  The Power of Student's t-test , 1965 .

[25]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[26]  A. Hrõbjartsson,et al.  Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed. , 2011, Journal of clinical epidemiology.

[27]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[28]  A. Indrayan,et al.  A simple nomogram for sample size for estimating sensitivity and specificity of medical tests , 2010, Indian journal of ophthalmology.

[29]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[30]  D. Altman,et al.  A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. , 1990, Computers in biology and medicine.

[31]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[32]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[33]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[34]  C. Terwee,et al.  When to use agreement versus reliability measures. , 2006, Journal of clinical epidemiology.

[35]  J. Bartlett,et al.  Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[36]  J M Bland,et al.  Statistics Notes: One and two sided tests of significance , 1994 .

[37]  J. Weir Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. , 2005, Journal of strength and conditioning research.