scholarly journals Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient

Data in Brief ◽  
2017 ◽  
Vol 14 ◽  
pp. 291-294 ◽  
Author(s):  
Paolo Rocco ◽  
Francesco Cilurzo ◽  
Paola Minghetti ◽  
Giulio Vistoli ◽  
Alessandro Pedretti
Keyword(s):  
Confidence Interval ◽  
Correlation Coefficient ◽  
Simulation Data

scholarly journals MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Joshua Shur ◽  
Matthew Blackledge ◽  
James D’Arcy ◽  
David J. Collins ◽  
Maria Bali ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Coefficient Of Variation ◽  
In Silico ◽  
Image Acquisition ◽  
Texture Feature ◽  
Texture Features ◽  
Concordance Correlation Coefficient ◽  
Concordance Correlation ◽  
Simulation Data ◽  
Mri Features

Abstract Purpose To evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study. Materials and methods Separate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability. Results Eighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%. Conclusion Most MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.

Effects of Scaling on the Correlation Coefficient: Additional Considerations

1978 ◽  
Vol 15 (2) ◽  
pp. 304-308 ◽  
Author(s):  
Warren S. Martin
Keyword(s):  
Normal Distribution ◽  
Correlation Coefficient ◽  
Correlation Coefficients ◽  
Bivariate Normal Distribution ◽  
Information Loss ◽  
Simulation Data ◽  
Pearson Product Moment Correlation ◽  
Bivariate Normal ◽  
Amount Of Information ◽  
Restricted Number

Distortion in the Pearson product moment correlation due to a restricted number of scale points is evaluated in two ways. First, a simulation of the bivariate normal distribution is used to estimate the effects of varying the number of scale points on the product moment correlation. This procedure demonstrates a substantial amount of information loss. Second, other correlation coefficients and some methods to correct for this loss are discussed and related to the simulation data.

scholarly journals Equivalency between the shock index and subtracting the systolic blood pressure from the heart rate: an observational cohort study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yohei Kamikawa ◽  
Hiroyuki Hayashi
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Cohort Study ◽  
Confidence Interval ◽  
Systolic Blood Pressure ◽  
Correlation Coefficient ◽  
Observational Cohort Study ◽  
Regression Equation ◽  
Shock Index ◽  
Observational Cohort

Abstract Background Although the shock index is known to predict mortality and other severe outcomes, deriving it requires complex calculations. Subtracting the systolic blood pressure from the heart rate may produce a simple shock index that would be a clinically useful substitute for the shock index. In this study, we investigated whether the simple shock index was equivalent to the shock index. Methods This observational cohort study was conducted at 2 tertiary care hospitals. Patients who were transported by ambulance were recruited for this study and were excluded if they were aged < 15 years, had experienced prehospital cardiopulmonary arrest, or had undergone inter-hospital transfer. Pearson’s product-moment correlation coefficient and regression equation were calculated, and two one-sided tests were performed to examine their equivalency. Results Among 5429 eligible patients, the correlation coefficient between the shock index and simple shock index was extremely high (0.917, 95% confidence interval 0.912 to 0.921, P < .001). The regression equation was estimated as sSI = 258.55 log SI. The two one-sided tests revealed a very strong equivalency between the shock index and the index estimated by the above equation using the simple shock index (mean difference was 0.004, 90% confidence interval 0.003 to 0.005). Conclusion The simple shock index strongly correlated with the shock index.

Confidence assessment and interval prediction for multi-input model via grey system theory

2018 ◽  
Vol 8 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Haoliang Wang ◽  
Xiwang Dong ◽  
Qingdong Li ◽  
Zhang Ren
Keyword(s):  
Confidence Interval ◽  
Correlation Coefficient ◽  
Calculation Method ◽  
Reference Sample ◽  
Prediction Method ◽  
Calculation Methods ◽  
Content Type ◽  
Interval Prediction ◽  
Confidence Value ◽  
Reference Samples

Purpose By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems. Design/methodology/approach In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true. Findings The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results. Originality/value Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.

Note on a Confidence Interval for the Squared Semipartial Correlation Coefficient

2008 ◽  
Vol 68 (5) ◽  
pp. 734-741 ◽  
Author(s):  
James Algina ◽  
Harvey J. Keselman ◽  
Randall J. Penfield
Keyword(s):  
Confidence Interval ◽  
Correlation Coefficient ◽  
Semipartial Correlation

scholarly journals Equivalency between the shock index and subtracting the systolic blood pressure from the heart rate: An observational cohort study

2020 ◽  
Author(s):  
Yohei Kamikawa ◽  
Hiroyuki Hayashi
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Cohort Study ◽  
Confidence Interval ◽  
Systolic Blood Pressure ◽  
Correlation Coefficient ◽  
Observational Cohort Study ◽  
Regression Equation ◽  
Shock Index ◽  
Observational Cohort

Abstract Background: Although the shock index is known to predict mortality and other severe outcomes, deriving it requires complex calculations. Subtracting the systolic blood pressure from the heart rate may produce a simple shock index that would be a clinically useful substitute for the shock index. In this study, we investigated whether the simple shock index was equivalent to the shock index.Methods: This observational cohort study was conducted at 2 tertiary care hospitals. Patients who were transported by ambulance were recruited for this study and were excluded if they were aged < 15 years, had experienced prehospital cardiopulmonary arrest, or had undergone inter-hospital transfer. Pearson’s product-moment correlation coefficient and regression equation were calculated, and two one-sided tests were performed to examine their equivalency.Results: Among 5,429 eligible patients, the correlation coefficient between the shock index and simple shock index was extremely high (0.917, 95% confidence interval 0.912 to 0.921, P < .001). The regression equation was estimated as sSI = 258.55 log SI. The two one-sided tests revealed a very strong equivalency between the shock index and the index estimated by the above equation using the simple shock index (mean difference was 0.004, 90% confidence interval 0.003 to 0.005).Conclusion: The simple shock index strongly correlated with the shock index.

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