Common Coefficient Of Variation Research Articles

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs.This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine.Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation).A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively.The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

Confidence estimation based on data from independent studies.

The problem of finding confidence intervals based on data from several independent studies or experiments is considered. A general method of finding confidence intervals by inverting a combined test is proposed. The combined tests considered are the Fisher test, the weighted inverse normal test, the inverse chi-square test and the inverse Cauchy test. The method is illustrated for finding confidence intervals for a common mean of several normal populations, common correlation coefficient of several bivariate normal populations, common coefficient of variation, common mean of several lognormal populations, and for a common mean of several gamma populations. For each case, the confidence intervals based on the combined tests are compared with the other available approximate confidence intervals with respect to coverage probability and precision. R functions to compute all confidence intervals are provided in a supplementary file. The methods are illustrated using several practical examples.

Confidence Intervals for Common Coefficient of Variation of Several Birnbaum–Saunders Distributions

The Birnbaum–Saunders (BS) distribution, also known as the fatigue life distribution, is right-skewed and used to model the failure times of industrial components. It has received much attention due to its attractive properties and its relationship to the normal distribution (which is symmetric). Furthermore, the coefficient of variation (CV) is commonly used to analyze variation within a dataset. In some situations, the independent samples are collected from different instruments or laboratories. Consequently, it is of importance to make inference for the common CV. To this end, confidence intervals based on the generalized confidence interval (GCI), method of variance estimates recovery (MOVER), large-sample (LS), Bayesian credible interval (BayCrI), and highest posterior density interval (HPDI) methods are proposed herein to estimate the common CV of several BS distributions. Their performances in terms of their coverage probabilities and average lengths were investigated by using Monte Carlo simulation. The simulation results indicate that the HPDI-based confidence interval outperformed the others in all of the investigated scenarios. Finally, the efficacies of the proposed confidence intervals are illustrated by applying them to real datasets of PM10 (particulate matter ≤ 10 μm) concentrations from three pollution monitoring stations in Chiang Mai, Thailand.

Combining independent tests for a common parameter of several continuous distributions: a new test and power comparisons

The problem of testing a common parameter of several independent continuous populations is considered. Among all tests, Fisher’s combined test is the most popular one and is routinely used in applications. In this article, we propose an alternative method of combining the p-values of independent tests using chi-square scores, referred to as the inverse chi-square test. The proposed test is as simple as other existing tests. We compare the powers of the combined tests for (i) testing a common mean of several normal populations, (ii) testing the common coefficient of variation of several normal populations, (iii) testing the common correlation coefficient of several bivariate normal populations, (iv) testing the common mean of several lognormal populations and (v) testing the common mean of several gamma distributions. Our comparison studies indicate that the inverse chi-square test is a better alternative combined test with good power properties. An illustrative example with real-world data is given for each problem.

Adjusted generalized confidence intervals for the common coefficient of variation of several normal populations

This paper proposes the novel approaches to construct confidence intervals for the common coefficient of variation (CV) of several normal populations using the concepts of the adjusted generalized confidence interval (adjusted GCI) approach and the computational approach. The coverage probability and average length of the proposed approaches were evaluated by a Monte Carlo simulation and compared with that of the existing approaches; the generalized confidence interval (GCI) approach and the adjusted method of variance estimates recovery (adjusted MOVER) approach. The results showed that the coverage probability of the adjusted GCI approach is above the nominal confidence level of 0.95 and better than the other approaches when the sample case is small ( 6) for all sample sizes, except when the sample sizes are very small. However, the coverage probability of the computational approach provides much better confidence interval estimate than the other approaches when the sample case is large ( 10). The proposed approaches and the existing approaches are illustrated using three medical science data.

A Bayesian control chart for a common coefficient of variation

ABSTRACTBy using the medical data analyzed by Kang et al. (2007), a Bayesian procedure is applied to obtain control limits for the coefficient of variation. Reference and probability matching priors are derived for a common coefficient of variation across the range of sample values. By simulating the posterior predictive density function of a future coefficient of variation, it is shown that the control limits are effectively identical to those obtained by Kang et al. (2007) for the specific dataset they used. This article illustrates the flexibility and unique features of the Bayesian simulation method for obtaining posterior distributions, predictive intervals, and run-lengths in the case of the coefficient of variation. A simulation study shows that the 95% Bayesian confidence intervals for the coefficient of variation have the correct frequentist coverage.

A note on combined inference on the common coefficient of variation using confidence distributions

This article considers inference for a common coefficient of variation (CV) shared by several normal populations. The confidence distributions (CD) are used to combine the information about each CV from different sources. A new procedure for constructing a confidence interval for the common CV is developed based on a combined confidence distribution for the inverse of the CV. The new derived CD interval has a theoretical exact frequentist property. Simulation results demonstrate that the new confidence intervals perform very well in terms of empirical coverage probability and average interval length. Finally, the proposed new procedure is illustrated on a real data example.

Inferences on the common coefficient of variation

The coefficient of variation is often used as a measure of precision and reproducibility of data in medical and biological science. This paper considers the problem of making inference about the common population coefficient of variation when it is a priori suspected that several independent samples are from populations with a common coefficient of variation. The procedures for confidence interval estimation and hypothesis testing are developed based on the concepts of generalized variables. The coverage properties of the proposed confidence intervals and type-I errors of the proposed tests are evaluated by simulation. The proposed methods are illustrated by a real life example.

Impact of a common CV evaluation scheme on overall laboratory performance: 8-year experience of a large national proficiency testing program in Japan

In proficiency testing (PT), it is difficult to set rational criteria for determining target values and allowable limits. In Japan, the largest national PT program has for the past 8 years used an evaluation scheme based on a "common coefficient of variation" (cCV) to overcome this difficulty. The cCV, the average within-method variation, is derived by one-way analysis of variance from data of peer groups after excluding those with large variations. A unit interval used for grading participants is obtained by multiplying the cCV by the peer-group mean which is computed using an iterative method featuring repeated removal of a large portion of the tails of the distribution and reinflation of the standard deviation (SD) to compensate for the trimming. The evaluation scheme gives a relatively better grade to those participants in peer groups with small CVs, compared to use of individual peer-group CVs. The scheme has expedited the transition to more reproducible methods, resulting in a notable overall improvement in laboratory performance. The cCV evaluation scheme is uniformly applicable to any analyte measured quantitatively. The iterative method, an integral part of the scheme, effectively precludes bias in the evaluation.

Robustness of the Chen-Dougherty-Bittner procedure against non-normality and heterogeneity in the coefficient of variation.

Chen, Dougherty, and Bittner [Y. Chen, E. R. Dougherty, and M. L. Bittner, J. Biomed. Opt. 2(4), 364-374 (1997)] provided the derivation of a probability density function (PDF) for a signal ratio from a DNA microarray. This PDF is potentially useful for testing whether a pair of signals from the same gene has a common mean. The derivation of the PDF assumes the normality of all signal distributions and a common coefficient of variation (CV) for all signals within a microarray. The testing procedure requires the calculation of a common confidence interval for a microarray, based on a maximum likelihood estimator of the "common" CV, and the determination of whether or not a ratio for a particular gene falls within this interval. This study used Monte Carlo techniques and demonstrated that the procedure is robust to violations of normality and also to constancy in the coefficients of variation. A closer examination of the dynamics of the procedure found that the robustness was the result of offsetting effects. The size of the confidence interval was increased as a result of higher estimates of the common CV, as the actual CV pattern became heterogeneous. This effect mitigated the inflation in the size of the ratio as a result of increasing CV heterogeneity. These findings suggest that the Chen-Dougherty-Bittner procedure may be used even if underlying assumptions do not hold.

Glutathione Concentration and Distribution in Cervical Cancers and Adjacent Normal Tissues

Intracellular glutathione (GSH) has been shown to play an important role in modulating the cellular response to many cytotoxic agents, including radiation and chemotherapeutic drugs. In this study, individual glutathione levels from contiguous blocks of cervical cancer tissue were determined in order to investigate the intratumor GSH distribution and variation, as well as the difference between tumor and its normal counterpart. The GSH content of a squamous cell carcinoma of the cervix was twofold higher than its normal counterpart, but there was no obvious difference in a case with adenoid cystic carcinoma. The tumor margin adjacent to normal tissue has a higher GSH content than the tumor center in a large tumor. Although the intertumor variation in GSH is different in these two tumors, a common coefficient of variation, 26, is observed, which may be used to estimate the range of tumor GSH if it can only be determined by a biopsy.

A Related Problem in Bioavailability/Bioequivalence Studies — Estimation of the Intrasubject Variability With a Common CV

AbstractThe problem of characterizing the intrasubject variability in bioavailability/bioequivalence studies when the intrasubject variability differs from subject to subject is considered. To account for the heterogeneity of the intrasubject variability, a conditional random effects model that assumes a common coefficient of variation (CV) for each subject is proposed. This common CV is used to assess the intrasubject variability. Several procedures are developed for estimating the common CV. The proposed estimators are compared asymptotically. An extensive simulation study is conducted to study their finite sample performances.

Product Correlation when Original Variables are Jointly Distributed Multivariate Normal: a Comparison With Sum Correlation

Product correlation was studied under the condition that the original variables are jointly distributed multivariate normal with equal coefficients of variation. Dependent upon the value of the common coefficient of variation, product correlation was shown to be a quantity ranging between two extremes: (a) sum correlation; and (b) either the value zero or an unfamiliar function of the intercorrelations among the original variables. Product correlations calculated by systematically varying the common coefficient of variation and the common intercorrelation among all original variables revealed the similarity between product correlation and sum correlation for many data applications.