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Study Design and Methods
Published in Lisa Chasan-Taber, Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
In the case of objective measures such as biomarker assessments, provide the laboratory coefficient of variation. Additionally specifying how you will assess quality control in the laboratory will also serve to reassure the reviewer of the validity of your biomarker assessment. Example quality control measures involve including blinded quality control samples in assays and blinding the laboratory personnel to participant characteristics (e.g., to case and control status in the context of a case-control study).
Pulmonary Performance in Laboratory Animals Exposed to Toxic Agents and Correlations with Lung Disease in Humans
Published in Jacob Loke, Pathophysiology and Treatment of Inhalation Injuries, 2020
We begin by characterizing the normal CO2 response in terms of other parameters besides VT (or ΔP) and f. This will then provide a basis for comparisons with responses obtained during and following exposure to airborne chemicals and further characterize the two types of abnormal responses described. As mentioned earlier, the measurements of V̇, VT, ΔP, and f were conducted using the head chamber apparatus. By using this apparatus, two variables can be examined simultaneously for a given breath. Thus, it was possible to plot flow-volume (V̇-VT) and pressure-volume loops (ΔP-VT) (Schaper et al., 1985). Results of such measurements are shown in Figure 9 for one control animal breathing air, then during CO2 challenge. Also shown in Figure 9 is the relationship between ΔP and V̇ at 0.5 VT, which can be used to obtain a measure of resistance (R) (Bargeton and Barres, 1955; Johanson and Pierce, 1971; Schaper et al., 1985). Table 2 gives the numerical results for numerous ventilatory parameters such as inspiratory/expiratory airflows (V̇I/V̇E), time of inspiration/expiration (TI/TE), area of ΔP-VT loops, and resistance (R) both during air and CO2. Areas of ΔP-VT loops were examined since they are substantially proportional to flowresistive work (Bargeton and Barres, 1955; Jaeger and Otis, 1964). The data in Table 2 for air breathing and CO2 challenge were collected from 12 animals and each value in the table represents the mean of this group. The coefficient of variation is also given for each mean value.
Quality Control and Quality Assurance
Published in Niel T. Constantine, Johnny D. Callahan, Douglas M. Watts, Retroviral Testing, 2020
Niel T. Constantine, Johnny D. Callahan, Douglas M. Watts
The coefficient of variation (C.V.) is determined by dividing the standard deviation by the mean, and then multiplying by 100. The value obtained is a reflection of the relative dispersion of O.D. values around the mean, and is useful as an indicator of reproducibility of control values or samples that are repeated in duplicate. The formula below is unitless and is expressed as a percent.
Orthotic shorts for improving gait and walking in multiple sclerosis: a feasibility study
Published in Disability and Rehabilitation, 2023
Nicola Snowdon, Sionnadh McLean, Hilary Piercy, Matthew A. Brodie, Jon Wheat
The GAITRite system used was the GAITRite 3.8, comprising a 5.18 m long walkway. The GAITRite provides excellent reliability for assessing most spatiotemporal gait parameters in MS [24]. Because step width is more variable, reliable assessment requires multiple passes of the GAITRite mat [25]. Each participant completed four passes of the mat at each test, providing a mean of 24 steps per test (SD 4.3; range 16–31). Participants were asked to walk at a comfortable but purposeful pace. They commenced walking 2 m before the start of the mat and finished each walk 2 m after the end of the mat. Mean values for gait speed were downloaded from the GAITRite software. Values of step length, step width. and stride time were downloaded for each step or stride and used to calculate means and variability. Variability was expressed as coefficient of variation and standard deviation.
Robust analogs to the coefficient of variation
Published in Journal of Applied Statistics, 2022
Chandima N. P. G. Arachchige, Luke A. Prendergast, Robert G. Staudte
The coefficient of variation (CV), defined to be the ratio of the standard deviation to the mean, is the most commonly used method of measuring relative dispersion. It has applications in many areas, including engineering, physics, chemistry, medicine, economics and finance, to name just a few. For example, in analytical chemistry, the CV is widely used to express the precision and repeatability of an assay [42]. In finance, the coefficient of variation is often considered useful in measuring relative risk [35] where a test of the equality of the CVs for two stocks can be performed to compare risk. In economics, the 3,9]]. Other examples use the CV to assess the homogeneity of bone test samples [23], assessing the strength of ceramics [20] and as a summary statistic to describe the development of age- and sex-specific cut off points for body-mass indexing in overweight children [10].
Response to Dr. Sadler’s comments
Published in Scandinavian Journal of Clinical and Laboratory Investigation, 2020
Anders Kallner, Astrid Petersmann, Matthias Nauck, Elvar Theodorsson
If we understand Dr. Sadler correctly, he objects to our lumping together the results within the reference interval and estimating the uncertainty in that interval, leaving the evaluation of the remaining measured concentrations to a number of randomly selected intervals. We see this as a strength of the approach and believe it, possibly, is of clinical value to define the uncertainty of results in critical intervals. The reference interval is a critical interval where a correct estimate of the uncertainty would be of importance, as exemplified by estimating the ‘minimal difference’ or ‘reference change value’ [7] in the diagnostic process. We demonstrate that the uncertainty of some of the studied measurands do not always vary homoscedastically. Therefore, sometimes, the coefficient of variation might be a better tool to describe the variation within a measurement interval. A reasonable conclusion of our findings is that the conventional description or definition of analytical goals as a single estimate of the coefficient of variation is not logical or scientifically motivated [8].