The role of a Simplified Selvester Score as a predictor of successful fibrinolytics in STEMI
Cut Adeya Adella in Stem Cell Oncology, 2018
All statistical analyses were performed using statistical software, and a p value < 0.05 was considered significant. A receiver operating characteristic curve analysis was used to determine the optimum cut-off values of the Simplified Selvester Score to predict successful fibrinolytic therapy. Clinical, laboratory and procedural data were compared with the use of Student’s t-test or Mann-Whitney U test for continuous variables and the Chi-square or Fisher’s exact test for categorical variables (expressed as counts and percentages). The correlation of the Simplified Selvester Score and troponin T was analysed by a correlation test. Continuous variables were analysed for normal distribution using the Kolmogorov-Smirnov test. To address concerns over confounding variables affecting successful fibrinolytics, we also performed a multivariate logistic regression analysis which significant variables in bivariate analysis before were included. The study protocol was reviewed and approved by our local institutional human research committee.
Utilizing Educational Media of Disaster Mitigation on Earthquake and Tsunami Preparedness for Inpatient Families in Hospital
Teuku Tahlil, Hajjul Kamil, Asniar, Marthoenis in Challenges in Nursing Education and Research, 2020
The results of the data of earthquake and tsunami disaster preparedness variables conducted in 18 wards were nine rooms for each group. Intervention group A disaster mitigation education activities used media in the form of a leaflet and group B used flip chart. The normality tested of the data was carried out through the Kolmogorov-Smirnov test. The results showed that all the data were normally distributed. The category levels of disaster preparedness are as follows: very ready if 80–100, ready 65–79, almost ready 55–64, less ready 40–54 and not ready <40, as shown as the table 2.
Data checking
Antony Stewart in Basic Statistics and Epidemiology, 2018
Although you can visually inspect the data, for example, by using a histogram (Petrie & Sabin, 2009), to check whether it resembles the symmetrical bell-shaped pattern described in Chapter 11, normality is often checked using one of two just previously mentioned tests: Kolmogorov-Smirnov — for large samples (e.g. 50 or more)Shapiro-Wilk — best for sample sizes of less than 50.
Statin adherence in patients with high cardiovascular risk: a cross-sectional study
Published in Postgraduate Medicine, 2023
Yusuf Cetin Doganer, Umit Aydogan, Umit Kaplan, Suat Gormel, James Edwin Rohrer, Uygar Cagdas Yuksel
Statistical analysis was performed using SPSS software (version 12.0; SPSS Inc., Chicago, IL, USA). The dependent variable of the study was determined as the level of medication adherence. The independent variables were socioeconomic characteristics, chronic diseases, drug use characteristics (cholesterol treatment duration, number of drugs, etc.), general health assessment, and depressive symptoms. Data were expressed as mean ± SD and/or percent (%). Descriptive statistics for numerical variables (mean, median, standard deviation, minimum and maximum) and frequency tables were given for categorical variables. The Kolmogorov–Smirnov test was applied to determine whether the normal distribution assumption for continuous variables was provided. Quantitative data were evaluated using an unpaired t-test or the Mann-Whitney U test, as appropriate. A comparison of categorical variables was performed using the chi-square test. p-value <0.05 was considered statistically significant. Group comparisons for each explanatory variable have a single p-value since comparisons between groups were analyzed by One-way ANOVA tests and Chi-square tests.
Association of the positive T wave in lead aVR with short-term mortality in patients with acute pulmonary embolism
Published in Acta Cardiologica, 2020
Nizamettin Selçuk Yelgeç, Mehmet Baran Karataş, Can Yücel Karabay, Yiğit Çanga, Barış Şimşek, Ali Nazmi Çalık, Ayşe Emre
All data were presented as a mean ± SD for parametric variables, as a median (interquartile range) for non-parametric variables and as percentages for categorical variables. Continuous variables were checked for normal distribution assumptions using Kolmogorov-Smirnov test. Differences between patients with a positive TaVR and those without were evaluated using the Kolmogorov-Smirnov test or the Student t-test where appropriate. Categorical variables were tested using Pearson’s χ2 test and Fisher’s Exact Test. Univariate and multivariate logistic regression analysis was performed to investigate the predictors of short-term mortality in the study population. Forward stepwise multivariate regression models using parameters with p < .10 were created to identify independent predictors of mortality. In order to prevent multicollinearity, we did not enter the parameters included in the PESI score in the multivariate logistic regression analysis. Receiver operating curves (ROC) were generated with the calculation of the area under the curve (AUC) to define the cut-off values for T positivity in lead aVR, PESI score, and Right ventricular end‐diastolic diameter (RVEDD). p-values were two-sided, and values < .05 were considered statistically significant. All statistical calculations were performed using Statistical Package for Social Sciences software (SPSS 16.0 for Windows, SPSS Inc., Chicago, Illinois).
Impact of myoinositol with metformin and myoinositol alone in infertile PCOS women undergoing ovulation induction cycles - randomized controlled trial
Published in Gynecological Endocrinology, 2021
Priyanka Prabhakar, Reeta Mahey, Monica Gupta, Rajesh Khadgawat, Garima Kachhawa, Jai Bhagwan Sharma, Perumal Vanamail, Rajesh Kumari, Neerja Bhatla
We carried out data analyses using statistical software STATA version 12.0. Using Kolmogorov-Smirnov test we tested for normality assumptions of continuous variable. Descriptive statistics such as mean, standard deviation (SD) and range values were calculated for normally distributed data. Student’s t-independent test was used for comparison of mean values between the two groups. Median and inter-quartile range (IQR) values were calculated for non-normal/skewed data. Mann-Whitney U-test was used to compare median values. Categorical data was expressed as frequency and percent values and comparison was done using Chi-square/Fishers exact test as appropriate. Risk ratio and 95% confidence limits were calculated. Changes in the parameters from baseline to three months were compared using Students t-paired test within the groups. Changes in qualitative variables from base-line to three months were tested using McNemar test. For all the statistical tests a two sided probability of p < .05 was considered for statistical significance.
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