The categorical values are given in percentage, and the quantitative values are given in mean and standard deviation (SD). Conditional logistic regression was used to identify important predictors of progression, taking into account the case–control grouping. Univariable and multivariable analyses were performed. The radiographic measurements all have the potential to deviate from normal in one of two directions (e.g., valgus and varus), and as a result, the effect of each of these measurements on probability of progression are nonlinear. These predictors were therefore categorised into measures inside and outside the acceptable limits for implant position. In this way, the data are analysed as dichotomous variables. The inter- and intra-observer errors for implant alignment were assessed using an intraclass correlation coefficient. All statistical analyses were performed using Stata IC v.12.1 (Stata Corp., College Station, TX, USA).
Stata ic v12
Manufactured by StataCorp
Sourced in United States
Stata IC v12 is a software package developed by StataCorp. It is a general-purpose statistical software that provides a wide range of data analysis and management tools. Stata IC v12 offers various features for data manipulation, regression analysis, time-series analysis, and more.
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Lab products found in correlation
11 protocols using stata ic v12
1
Predictors of Implant Progression
2
Antioxidant Effects on Diesel Exposure
3
Statistical Analysis Using Stata
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Summary statistics were calculated using Stata/IC V.12.1 (StataCorp LP).
4
Evaluating Ethnic-Specific Fetal Growth
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Within each ethnic group, the ability of SGA and LGA (exposure) to predict the outcomes was assessed. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for each of the three charts’ SGA and LGA cut-offs. Receiver operating characteristics (ROC) and area under the ROC (AUROC) were produced to summarise the diagnostic performance of the respective cut-offs. As more than one diagnostic test (BiB vs UK-WHO cut-off; BiB vs GROW, etc) was tested on the same individual, a comparison of AUROCs between charts was made using Stata's correlated ROC method, “roccomp”.
The production of the ethnic-specific centiles/z scores was done using ‘LMSchartmaker Light’ V.2.54.32 GROW centiles were produced using the ‘Customised Centile Calculator’ V.6.7 software from the Perinatal Institute.33 All other analyses were conducted in Stata/IC V.12.1.
The production of the ethnic-specific centiles/z scores was done using ‘LMSchartmaker Light’ V.2.54.32 GROW centiles were produced using the ‘Customised Centile Calculator’ V.6.7 software from the Perinatal Institute.33 All other analyses were conducted in Stata/IC V.12.1.
5
Seasonal Prevalence of Bluetongue and Epizootic Hemorrhagic Disease Viruses
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Data were described using simple descriptive statistics. The non-parametric data have been presented as medians with interquartile ranges (IQRs). The Shapiro–Wilk test was used to test the data for normality, while a Kruskal–Wallis test was used to test the effect of season on the median prevalence of BTV and EHDV. Stata IC v12 (College Station, TX, USA) was used to perform all analyses, with levels of significance set at p < 0.05.
6
Evaluating Contraception Myths and Beliefs
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Participants’ baseline characteristics, and the results for the primary and secondary outcomes, were summarized for all randomized participants according to treatment allocation. Categorical variables were summarized using the number and proportion of participants and quantitative normally distributed variables were presented using the mean and standard deviation (SD).
The mean change in the score on the contraception myths and misconceptions index was treated as a continuous variable, with the comparison between study arms carried out using parametric tests if normality assumptions held. A generalized linear model (GLM) using a normal distribution and identity link was used to compare scores between arms while adjusting for the time (days) in which the follow-up survey was conducted. These analyses were carried out for the ITT and PP populations. All tests were two-sided with a significance level of 5%. Stata/IC v12 (Stata Corp., College Station, TX, USA) was used for analysis.
The mean change in the score on the contraception myths and misconceptions index was treated as a continuous variable, with the comparison between study arms carried out using parametric tests if normality assumptions held. A generalized linear model (GLM) using a normal distribution and identity link was used to compare scores between arms while adjusting for the time (days) in which the follow-up survey was conducted. These analyses were carried out for the ITT and PP populations. All tests were two-sided with a significance level of 5%. Stata/IC v12 (Stata Corp., College Station, TX, USA) was used for analysis.
7
Seasonal Prevalence Analysis
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Data were examined using simple descriptive statistics. Data were tested for normality using the Shapiro–Wilk test. Non-parametric data are presented as median and inter quartile range. The effect of season on prevalence was tested using a Kruskal–Wallis test. All analyses were performed using Stata IC v12 (College Station, TX, USA). The level of significance was set at p < 0.05.
8
Barriers to Disclosure Intervention Evaluation
9
Predictors of Functional Outcomes in Juvenile Idiopathic Arthritis
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Continuous covariates were expressed in terms of their mean and SD. Categorical covariates were described by frequency distribution.
Comparisons between groups of the covariates and the outcomes were evaluated using univariated linear regression for continuous response variables and univariated logistic regression for binary response variables. After assessing the differences, multivariate logistic or linear regression models were used to examine the association, adjusted for ILAR category, of a range of demographic and clinical variables with the following outcomes: HAQ, JADI-A and JADI-E as continuous variables and disease activity as a dichotomous variable. In order to compare the outcomes before and after biological era, we used multivariate logistic or linear regression analysis adjusted for ILAR category and disease duration.
In order to obtain the predictor models, we used three multivariable linear regression models for the continuous outcomes (HAQ, JADI-A, JADI-E) and one multivariate logistic regression model for the dichotomous outcome, by a stepwise selection method.
Missing data were interpreted as random missing data. In all analyses, significance level was set at 0.05.
All analyses were performed using Stata IC V.12 (StataCorp 2011. Stata Statistical Software: Release 12. College Station, Texas: StataCorp LP).
Comparisons between groups of the covariates and the outcomes were evaluated using univariated linear regression for continuous response variables and univariated logistic regression for binary response variables. After assessing the differences, multivariate logistic or linear regression models were used to examine the association, adjusted for ILAR category, of a range of demographic and clinical variables with the following outcomes: HAQ, JADI-A and JADI-E as continuous variables and disease activity as a dichotomous variable. In order to compare the outcomes before and after biological era, we used multivariate logistic or linear regression analysis adjusted for ILAR category and disease duration.
In order to obtain the predictor models, we used three multivariable linear regression models for the continuous outcomes (HAQ, JADI-A, JADI-E) and one multivariate logistic regression model for the dichotomous outcome, by a stepwise selection method.
Missing data were interpreted as random missing data. In all analyses, significance level was set at 0.05.
All analyses were performed using Stata IC V.12 (StataCorp 2011. Stata Statistical Software: Release 12. College Station, Texas: StataCorp LP).
10
Accuracy of Optical Diagnosis of T1 Cancers
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The primary endpoint was assessment of accuracy of optical diagnosis of T1 cancers by colonoscopy experts, general gastroenterologists, and gastrointestinal fellows.
Normally distributed data were described with the mean and standard deviation. Sensitivity and specificity and their 95 % confidence intervals (CI) were calculated from the cases with benign/malignant histopathology results separately. Test results for optical diagnosis were used as a dependent variable and level of experience (experts, generals gastroenterologists and GI fellows) was used as a covariate as dummy variables. The positive predictive value (PPV) and negative predictive value (NPV) and their CI were calculated for cases with benign/malignant optical diagnosis separately. Results of histopathology were used as a dependent variable and level of experience was used as a covariate as dummy variables. For fellows, this was also described according to their level of experience (in years of training). The calculation was done by using logistic regression parameter estimates from generalized estimating equation (GEE) with exchangeable correlation structure 18 (link). SPSS for Windows software version 22 (SPSS Inc, Chicago, Ill) and STATA/IC V12 (Statacorp: College Station, Texas, USA) were used for analysis.
Normally distributed data were described with the mean and standard deviation. Sensitivity and specificity and their 95 % confidence intervals (CI) were calculated from the cases with benign/malignant histopathology results separately. Test results for optical diagnosis were used as a dependent variable and level of experience (experts, generals gastroenterologists and GI fellows) was used as a covariate as dummy variables. The positive predictive value (PPV) and negative predictive value (NPV) and their CI were calculated for cases with benign/malignant optical diagnosis separately. Results of histopathology were used as a dependent variable and level of experience was used as a covariate as dummy variables. For fellows, this was also described according to their level of experience (in years of training). The calculation was done by using logistic regression parameter estimates from generalized estimating equation (GEE) with exchangeable correlation structure 18 (link). SPSS for Windows software version 22 (SPSS Inc, Chicago, Ill) and STATA/IC V12 (Statacorp: College Station, Texas, USA) were used for analysis.
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