The significance level was set at 0.05. Statistical analyses were computed using Stata/IC 15.0 (StataCorp LP, College Station, Texas, USA). A paired t-test sample size estimation yielded a group size of ten patients for differences in alpha angle (alpha 0.05, power 0.8). The estimation is based on a minimal clinically important difference of 10° according to Barrientos.24 (link) The differences in alpha angle and ROM were tested for normality using the Shapiro–Wilk test, and were never rejected (p < 0.05). Hence, normality was assumed, and paired t-tests (two-sided) were used to test for differences in alpha angle and ROM. Mean and standard deviation are reported if not stated otherwise. The significance level was chosen at 0.05. Statistical analyses were computed using Stata/IC 15.1 (StataCorp LP, College Station, Texas, USA).
Stata ic 15
Manufactured by StataCorp
Sourced in United States
Stata/IC 15.1 is a data analysis and statistical software package developed by StataCorp. It provides a comprehensive set of tools for data management, analysis, and visualization. Stata/IC 15.1 is designed to handle a wide range of data types and offers a variety of statistical methods and modeling techniques.
Automatically generated - may contain errors
Lab products found in correlation
Stata statistical software release 15, by StataCorp (4 mentions)
Prism 8, by GraphPad (3 mentions)
Sas 9, by SAS Institute (3 mentions)
Prism 5, by GraphPad (2 mentions)
Prism 7, by GraphPad (2 mentions)
R version 3, by R Project for Statistical Computing (2 mentions)
Sas v9, by SAS Institute (2 mentions)
Spss statistics for windows version 27, by IBM (1 mentions)
Stata software, by StataCorp (1 mentions)
Prism 7.0b, by GraphPad (1 mentions)
Matlab, by MathWorks (1 mentions)
Spss pasw 18, by IBM (1 mentions)
Spss statistics 23, by IBM (1 mentions)
Prism 9, by GraphPad (1 mentions)
Stata statistical software, by StataCorp (1 mentions)
Spss statistics for windows version 23, by IBM (1 mentions)
Spss version 23, by IBM (1 mentions)
Spss 24, by IBM (1 mentions)
Sigmaplot 14, by Grafiti LLC (1 mentions)
Spss version 25.0 for windows, by IBM (1 mentions)
285 protocols using stata ic 15
1
Evaluating Alpha Angle and ROM Changes
2
Linking Built Environment to Health
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Descriptive statistics were estimated for built environment characteristics, sociodemographics, and health outcomes. National maps display the geographical distribution of built environment characteristics. We fit adjusted linear regression models to estimate associations between GSV-derived built environment characteristics and health outcomes, controlling for potential confounding variables such as racial/ethnic composition and economic disadvantage. Separate regressions were run for each built environment indicator given low to moderate associations between the built environment indicators that varied from −0.17 for single lane roads and sidewalks to 0.82 for street signs and two or more cars. Stata IC15 (StataCorp LP, College Station, TX, USA) was used for all statistical analyses. This study was approved by the University of Maryland Institutional Review Board.
3
Statistical Analysis of Experimental Data
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Statistical analysis was performed using Stata software (StataIC-15, StataCorp LLC). For categorical data, Fisher exact test was used. Continuous data were plotted to assess normal distribution and, when confirmed, analyzed by t-test. For nonparametric data, Mann-Whitney U test was used. Survival analysis was performed by Kaplan-Meier analysis with statistical difference calculated using the log-rank test. Cox-proportional hazards was used to determine hazard ratios (HRs) and tested using martingale residuals. Tests were 2-sided, and P-values <0.05 were considered statistically significant.
4
Comparison of Particle Emission Rates
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Box-and-whisker plots show the median (red line), interquartile range (blue box), and range (black whiskers). Stata/IC 15 was used to perform Shapiro–Wilk normality test on the particle emission rates for each activity. After log-transformation of the data, mixed-effects linear regression was performed to account for person-level correlations. Considering that we had only one primary random effect (person-to-person variability), all variances were set equal with zero covariances. Post hoc pairwise comparisons were performed and adjusted for multiple comparisons using Scheffe’s method. Scheffe groups are indicated with green letters below each box plot; groups with no common letter are considered significantly different (p < 0.05).
5
Indigenous Lung Function Characteristics
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Continuous parameters were tested for normality via the Shapiro Wilks distribution test, with body mass index (BMI) and smoking pack years displaying a non-parametric distribution, and other continuous variables approximating normal distribution. Non-parametric parameters were presented as medians (interquartile ranges (IQR)), normally distributed parameters as means (95% confidence intervals (CIs)), and categorical parameters as numbers (%). Clinical characteristics were compared between Indigenous and non-Indigenous patients by 2-tailed Student's t-test for normally distributed parameters. Equality of medians test was used for non-parametrically distributed parameters and 2-tailed proportions z-test for categorical parameters. LFPs were compared between Indigenous and non-Indigenous patients by 2-tailed Student's t-test. For both matched and unmatched cohorts Hedges G effect size was calculated for FVC % predicted, FEV1% predicted and FEV1/FVC ratio and reported as G (95% CI).25 (link) Hedges G effect size was classified as small (0.2 < 0.5), moderate (0.5 < 0.8) or large (>0.8). All data were analysed in STATA IC 15 (StataCorp, Texas) and alpha set to 0.05 throughout.
6
Knee Injury Progression Analysis
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All data are presented as mean ± standard error of the mean (SEM). Statistical analyses were conducted using two-way ANOVAs (experimental group as one and time after IA injection as second factor). Regarding the data of the changes in right knee transverse diameter and the mechanical nociceptive threshold, repeated measurements were done on the same set of experimental animals, therefore repeated measures ANOVAs were used. The two-way and repeated measures ANOVAs were followed, if applicable, by Bonferroni post hoc tests for multiple comparisons (Stata/IC 15; Stata Corp LP, TX, USA). P-values < 0.05 were considered statistically significant.
7
Exploring Perceptions of Research Protocol Attributes
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Demographics were described using descriptive statistics and frequencies. Data was compared by subscales and teaching sites. An individual raw mean score was calculated for each item.11 The mean score for each item can vary between 1-5. A mean score of two or less indicates negative perception of the attribute and problems, scores between two and three indicate an area which can be improved while a score above four represents a positive perception of the attribute.11 An Exploratory Factor Analysis revealed four subscales. A mean score was also calculated for the subscales11 ,12 by teaching sites. The subscale scores reported are mean scores instead of sum scores as this makes comparison of scores between subscales easier.13 (link) Analysis of Variance (ANOVA) was used to compare mean score over teaching sites. Data was analyzed using Stata/IC 15 (Stata Corporation, Inc. College Station, TX, UAS). P-values of <0.05 were considered statistically significant.
8
Diagnostic Performance Comparison of Workstation and Tablet
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Statistical analysis was performed using Stata IC15 (StataCorp, 2017, College Station, TX, USA). All values, unless otherwise stated, are given as mean and standard deviation (SD). The Wilcoxon signed-rank test was used to compare mean reading times between readers, rounds and sessions. Furthermore, stepwise forward linear modeling was performed for correlations between reading times, readers, sessions and hardware. Sensitivity (Se) and specificity (Sp) were determined as measures of diagnostic performance. McNemar’s test was conducted to compare Se and Sp between workstation and tablet readings. Intrarater agreement between devices was assessed using percentage of agreement and kappa statistic. A p-value less than 0.05 was considered statistically significant except for reading times, which were compared using the Wilcoxon signed-rank test with a significance level of 0.016 (0.05/3 reader groups after Bonferroni’s adjustment).
9
COVID-19 Seroprevalence Epidemiological Analysis
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First, we examined seroprevalence for IgM and IgG antibodies. We then performed descriptive analysis of COVID-19 seroprevalence against several categories of variables, i.e., demographic, health-related, and epidemiological. Crude univariable logistic regression was performed to test the unadjusted associations of variables with the odds for seroprevalence. Then, all available variables representing demographic, epidemiological, and other behavioural characteristics that may play a role in COVID-19 prevalence and had a p value < 0.10 were included in the multiple logistic models. We performed a chi-squared test for each association. The study is reported according to the STROBE statement for observational studies [24 (link)]. Analyses were performed using Stata IC 15, StataCorp, TX, United States.
10
Reliability and Validity of Hindi LMUP
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The analytical strategy was based on Classical Test Theory which underpinned the development of the LMUP and has been employed by subsequent evaluations such as that of Hall et al. [20 (link), 21 (link)]. This strategy included assessment of (1) acceptability and targeting, (2) reliability, and (3) validity. In this study it was also possible for us to carry out an analysis of the measurement properties of the Hindi LMUP across three groups of women according to their time from conception. The analysis was conducted in Stata/IC 15 (StataCorp, College Station, TX).
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