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Correlation
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
The Spearman rank correlation coefficient may also be thought of as a measure of the concordance of the ranks for the outcomes x and y. If the 20 measurements of percent immunization against dpt and under-5 mortality rate in Table 16.2 happened to be ranked in the same order for each variable – meaning that the country with the ith largest percent immunization also has the ith largest mortality rate for all values of i – then each difference di would be equal to 0, and
A Corpus Based Quantitative Analysis of Gurmukhi Script
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
Gurjot Singh Mahi, Amandeep Verma
The symbol is used to represent the calculated Person correlation coefficient. and are mean values of variables and , respectively, whereas is the number of values in the dataset. The value of always ranges between , i.e., . Spearman’s rank correlation coefficient () is one of the non-parametric statistical parameters, stated in Formula 12.8. The symbol is used to describe the calculated value for the Spearman rank correlation.
Fully Exploiting the Benefits of Protons *
Published in Harald Paganetti, Proton Therapy Physics, 2018
Peter van Luijk, Marco Schippers
The most elementary test of the model is whether ranking of patients according to their predicted risk corresponds to ranking them according to the incidence of the complication. To this end, Spearman’s rank correlation coefficient can be used [12]. Though a high correlation coefficient indicates that the model can be used to optimize the plan based on this model, it also does not test whether the probabilities are quantitatively correct. Treatment optimization based on risks of multiple endpoints (e.g., tumor control, xerostomia, and dysphagia), however, requires quantitatively correct NTCP values. Biases in the individual models would inadvertently influence plan selection [13]. Therefore, in addition to testing the ability of a model to rank plans with respect to a specific endpoint, checking the accuracy of the NTCP values obtained from the model is desired.
Real World Presentation and Treatment Outcomes with a Predominant Induction Chemotherapy Based Approach in Nasopharyngeal Carcinoma: A Sixteen Year Report from a Teaching Hospital in India
Published in Cancer Investigation, 2023
Ramana Gogi, Aparna Sharma, Atul Sharma, BK Mohanti, Raja Pramanik, Suman Bhasker, Ahitagni Biswas, Alok Thakar, Amit Chirom Singh, Kapil Sikka, Rajeev Kumar, Sanjay Thulkar, Sudhir Bahadur
Data was collected on a pre-designed proforma. All the normally distributed data were represented as mean ± SD and non-normally distributed data as median with interquartile range. The association between two categorical variables was assessed by using chi-square test. The mean comparison between two independent groups for normally distributed data was done by using Student’s T-test and for non-normally distributed data it was done by Mann-Whitney test. In normally distributed data Pearson correlation coefficient and in non-normally distributed data, Spearman rank correlation coefficient was used between two continuous variables. Time to event analysis (OS and PFS) was analyzed using Kaplan-Meier survival curve estimate. Univariate and multivariate cox-regression analysis was done to identify independent predictors. All p-values of <0.05 were considered significant. All statistical computation was performed using STATA software version 13.0.
Objective assessment of eyelid position and tear meniscus in facial nerve palsy
Published in Orbit, 2022
Alicia Galindo-Ferreiro, Victoria Marqués-Fernández, Hortensia Sanchez-Tocino, Silvana A. Schellini
Data were transferred to an Access spreadsheet (Microsoft Corp., Redmond, WA, USA) for statistical analysis. Univariate analysis was performed using Statistical Package for Social Sciences (SPSS 24; IBM Corp., New York, NY, USA). The Shapiro–Wilk test was used to assess the distribution of data. Descriptive statistics were calculated such as the mean ± standard deviation or median and interquartile range (IQR) values. If there was no normal distribution of data, the frequencies and percentage proportions were calculated for qualitative variables. Nonparametric analysis for related samples was performed for statistical validation. For multiple continuous variables that were not normally distributed, the Kruskal Wallis and U de Mann–Whitney p-value were calculated for validation of intergroup and intragroup differences, respectively. Spearman’s rank correlation coefficient was used to evaluate the relationship between variables. A P-value less than 0.05 was considered statistically significant.
Serum uric acid and arterial lactate levels in patients with obstructive sleep apnea syndrome: the effect of CPAP treatment
Published in Postgraduate Medicine, 2021
Konstantinos Bartziokas, Andriana I. Papaioannou, Aikaterini Haniotou, Evangelia Nena, Konstantinos Kostikas, Paschalis Steiropoulos
Categorical data are presented as n (%), whereas numerical data are presented as mean ± standard deviation (SD) when normally distributed and as median (interquartile ranges) when skewed. Comparisons for categorical data were performed with chi-square tests, whereas comparisons for numerical data were performed with unpaired t-tests for normally distributed data and with Mann–Whitney U-tests for skewed data. Correlations were performed with Spearman’s rank correlation coefficient. Multivariate analysis was performed in order to test which sleep parameters could serve as independent predictors of serum LA and serum UA levels, after adjustment for age, sex, and BMI. Statistical analysis was performed with GraphPad Prism 5 (GraphPad Software Inc, La Jolla, CA, USA) and MedCalc 9 (MedCalc Software, Mariakerke, Belgium). Statistical significance was set at a p value of <0.05.