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Correlation
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Based on this sample, there appears to be a moderately strong linear relationship between the percentage of children immunized against dpt in a specified country and its under-5 mortality rate. Since r is negative, mortality rate decreases in magnitude as percent immunization increases. Care must be taken when interpreting this relationship, however. An effective immunization program might be the primary reason for the decrease in mortality, or it might be a ramification of a successful comprehensive health care system that is itself the cause of the decrease. The correlation coefficient merely tells us that a linear relationship exists between two variables; it does not specify whether the relationship is cause-and-effect.
Clinical Workflows Supported by Patient Care Device Data
Published in John R. Zaleski, Clinical Surveillance, 2020
Note that in computing correlation, the parameter that is computed is the correlation coefficient which ranges between -1 and 1. A correlation coefficient of -1 between two parameters indicates a relationship between the two in which as one parameter increases, the correlated parameter decreases in direct proportion to the first parameter. In contrast, a correlation coefficient of 1 indicates a direct relationship in which an increase in the first parameter results in a direct increase in the correlated parameter. These two instances are referred to as perfect inverse correlation (i.e., correlation coefficient of -1) and perfect direct correlation (i.e., correlation coefficient of 1), respectively. When the correlation coefficient is computed to be 0, this means no direct relationship or association between any two parameters.
Model Checking in Meta-Analysis
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
In previous chapters, methods have been described to compute and model various outcome or effect size measures, such as risk differences, (log transformed) risk/odds ratios, raw or standardized mean differences, and correlation coefficients. The observed values of such measures may reflect the size of a treatment effect, the degree to which a risk factor is related to the chances of being afflicted by (or the severity of) a particular disease, or more generally the size of group differences. Some measures, such as the correlation coefficient, simply reflect the degree to which two variables of interest are (linearly) related to each other.
Evidence-based evaluation of safety management in port labor outsourcing
Published in International Journal of Occupational Safety and Ergonomics, 2023
Wenchao Wang, Fayi Huang, Jingjing Wang
Set the reference number x0(j) (j = 1, 2, … , n) to be listed, and compare the sequence xi(j) (i = 1, 2, … , n): Calculate the sequence of differences: Calculate the maximum and minimum differences between the poles: Calculate the correlation coefficient: Calculate the correlation degree: The target sequence and several sequences are sorted by correlation degree. The sequence with a large correlation degree is the closest to the target sequence and can be selected as the value λ of the different degree coefficient.
The relationship between ventilatory function and cognitive and behavioral impairment in ALS
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2021
Jaimin S. Shah, Otto Pedraza, Emir Festic, Björn Oskarsson
Multiple linear regression analysis was performed to study the magnitude and significance of the relationship between each variable and the ALS-CBS cognition and behavioral inventory scores. Predictors used in the analysis included age at onset, months of disease duration at the time of evaluation, sex, and NIV usage at the time of evaluation, the bulbar and motor subscores of the ALSFRS-R, and either the FVC or MIP. The bulbar subscore of the ALSFRS-R was used in the analysis to understand the association of bulbar impairment with cognitive and behavioral function. The motor subscore of the ALSFRS-R rather than the total ALSFRS-R was used to avoid collinearity with the respiratory component of the ALSFRS-R and ventilatory function measures. The models were computed separately for FVC and MIP to see if the findings were consistent with different measures of ventilatory function. Standardized coefficients were used to compare the effect size of different predictors. Subjects with missing data were excluded from multiple regression analysis. A p value <0.05 was used to determine significance of correlation coefficients. Statistical analysis was performed using IBM SPMSS (version 25 for Windows, IBM, Armonk, NY).
Health-related quality of life, work productivity and costs related to patients with inflammatory bowel disease in Austria
Published in Journal of Medical Economics, 2020
Evelyn Walter, Sophie-Christin Hausberger, Evelyn Groß, Uwe Siebert
Analysis of associations between HRQoL and work productivity (dependent variable) in the employed population showed that total loss of work productivity, composed of absenteeism and presenteeism, moderately correlated with HRQoL (r = 0.3964, Figure 3(a)). The Pearson correlation coefficient, r, can take on values between −1 and 1. The further away r is from zero, the stronger the linear relationship between the two variables. Presenteeism correlated stronger with HRQoL than absenteeism as represented by correlation coefficients of r = 0.6630 and r = 0.2526, respectively (Figure 3(b,c)). The association of the HRQoL with the total work productivity was significant (p value < 0.0001) (Figure 3(a)). The impact of HRQoL in the total population on the dependent variable daily activities (r = 0.7497) was greater than on paid work. The association of HRQoL with the reduced daily activity was significant (p value < 0.0001, Figure 3(d)).