Comparing Clinical Measurement Methods: A Practical Guide by Bendix Carstensen

By Bendix Carstensen

This booklet presents a pragmatic advisor to research of easy and intricate strategy comparability facts, utilizing Stata, SAS and R. It takes the classical Limits of contract as a place to begin, and provides it in a formal statistical framework. The version serves as a reference for reporting resources of version and for offering conversion equations and plots among tools for useful use, together with prediction uncertainty.

  • Presents a modeling framework for research of information and reporting of effects from evaluating measurements from diversified medical facilities and/or varied tools.
  • Provides the sensible instruments for reading strategy comparability stories besides information on what to document and the way to plot comparability reports and suggestion on acceptable software program.
  • Illustrated all through with laptop examples in R.
  • Supported via a supplementary web hosting an R-package that plays the foremost a part of the analyses wanted within the region.
  • Examples in SAS and Stata for the commonest events also are supplied.
  • Written via an stated professional at the topic, with a protracted status event as a biostatistician in a medical atmosphere and a tune checklist of supplying education at the topic.

Biostatisticians, clinicians, scientific researchers and practitioners excited by study and research of dimension equipment and laboratory investigations will take advantage of this publication. scholars of facts, biostatistics, and the chemical sciences also will locate this publication useful.Content:
Chapter 1 advent (pages 1–3):
Chapter 2 process Comparisons (pages 5–15):
Chapter three Howto… (pages 17–20):
Chapter four equipment with a unmarried dimension on each one (pages 21–47):
Chapter five reflect Measurements (pages 49–65):
Chapter 6 numerous equipment of size (pages 67–69):
Chapter 7 A normal version for procedure Comparisons (pages 71–97):
Chapter eight Transformation of Measurements (pages 99–106):
Chapter nine Repeatability, Reproducibility and Coefficient of version (pages 107–114):
Chapter 10 Measures of organization and contract (pages 115–126):
Chapter eleven layout of approach comparability experiences (pages 127–131):
Chapter 12 Examples utilizing general software program (pages 133–147):
Chapter thirteen The MethComp package deal for R (pages 149–152):

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Extra resources for Comparing Clinical Measurement Methods: A Practical Guide

Example text

7 The plasma volume data from table 2 in [8]. Left: Prediction plot assuming constant difference (limits of agreement). Right: Prediction plot based on the regression of differences on averages assuming constant variance. 5) ) The limits of agreement can be rotated to represent prediction limits under the model with constant difference between the methods, as is shown in the next panel. 8 The plasma volume data from table 2 in [8]: Prediction plot accommodating increasing standard deviation, using the regression of absolute residuals on the averages.

2 A worked example Table 2 in [8] gives a data set with plasma volume as a percentage of normal. frame': 198 obs. of 3 variables: $ meth: Factor w/ 2 levels "Hurley","Nadler": 2 2 2 2 2 2 2 2 2 2 ... $ item: num 1 2 3 4 5 6 7 8 9 10 ... 9 ... wide( plvol ) First we generate a Bland–Altman plot, where we plot both the traditional limits of agreement with the prediction limits for the differences as a function of the averages. These are produced by regressing the differences on the averages, and the drawing the regression line and the lines parallel to this at a distance of twice the residual standard deviation.

1). Using the same model to predict a measurement by method 2 on a new item, 0, say, given a measurement by method 1, y10 , say, goes a follows. The mean of the measurement by method 1 is α1 + μ0 , and the mean of measurement to be predicted is α2 + μ0 = (α2 − α1 ) + α1 + μ0 . The only piece of information on the new item is y10 , so this is the best estimate of α1 + μ0 , and since we have an estimate of α2 − α1 from the calibration data set, the predicted mean is just the sum of these two estimates: ¯ + y10 .

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