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303 protocols using stata v 11

1

Diabetic Retinopathy Prevalence and Diagnostic Accuracy

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Data were entered into the RAAB & DR V.6 software as well as Epi Info and STATA V.11 (statistical analysis software) by data entry operator daily. The data consistency and validity were checked with the help of double entry method (ie, two different data entry operators) as well as by the consistency check menu of the Epi Info and STATA V.11 software. These data were analysed for the prevalence of DR and diabetes (95% CI. The strength of associations was calculated using OR with 95% CI. The data for the DR and maculopathy grading by indirect ophthalmoscopy were entered in the RAAB & DR software.
Furthermore, the grading data of both methods (indirect ophthalmoscopy and fundus photography) were entered in Microsoft Excel. It was used to analyse the sensitivity and specificity of fundus photography to detect DR as compared with indirect ophthalmoscopy as the gold standard. Furthermore, the kappa (κ) statistics was used to assess the reliability of the diagnosis of DR and maculopathy by both the methods, that is, indirect ophthalmoscopy and fundus photography. It was calculated using STATA V.11 software and interpreted as no agreement (κ<0), poor (κ=0–0.19), fair (κ=0.20–0.39), moderate (κ=0.40–0.59), substantial (κ=0.60–0.79) and perfect agreement (κ=0.80–1.0).21 22 (link)
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2

Time Series Analysis of Global Payment System

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The data were analyzed using time series analysis through regression model in the environment of STATA v.11. A time series is a set of statistical data collected at regular and equal intervals (9 (link), 18 (link)). Global payment system was lunched in 1999, but due to there being no recorded data until November 2005, we started to collect data from Nov 2005 to Sep 2015. Data were entered into the model which was composed of 107 time points (13 months missed), because of some structural reform (Health Transformation Plan) and changes in global surgical codes and tariffs and impossibility of tracking and matching data, information related to the second sixth month of 2015 was removed from the time trend data. Finally, the results obtained from the analysis of findings using STATA v.11. can be shown in the form of polynomial curve charts.
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3

Intimate Partner Violence Prevalence

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The data were double-entered and analysis was completed using Stata V.11.29 Descriptive data analysis was performed using the Stata survey module. Final analysis was conducted among completed questionnaires. Prevalence data and 95% CIs were calculated using survey commands to account for clustering at the village level. The design effect due to cluster sampling was assessed using Stata V.11 (physical IPV past 12 months, intraclass correlation coefficients (ICC)=0.04 women; ICC=0.03 men). Bivariate and subgroup comparisons between women and men were calculated using the Wald test, where p<0.05 was considered statistically significant. Weighted Demographic Health Survey data30 from the same study regions were examined to compare the representativeness of the study population against a nationally representative population.
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4

Small Sample Size Analysis

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Data were analyzed in Stata V11 and SPSS. Given the small sample size of our cohort, we used non-parametric tests as well as Tukey’s one-way ANOVA of significance and correlation where a 5% level of significance was accepted as demonstrating differences and associations, respectively. Graphs illustrating these relationships were also generated in Stata V11.
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5

Coagulation Parameters and Mortality

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Data were examined for normality using skewness and kurtosis tests. Summary statistics were determined using STATA (v11.0). Groups were compared using the Student’s t-test and the Chi-squared test, as appropriate (GraphPad Software, v5.0; STATA v11.0). Regression analyses were used to identify significant correlations between coagulation parameters and mortality. A two-tailed P value < 0.05 was considered significant.
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6

Retinal Microvascular Calibre Analysis

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Retinal microvascular calibres were compared using Fisher’s exact test or the student t-test. Odds ratios were used to characterise dichotomous data and an association was considered significant if the odds ratio was >1.00, and the 95% confidence interval did not include 1.00. The data were also examined using linear regression. Statistical analyses were performed using STATA v.11.2 software (Stata Corp, College Station, TX, United States). A p value <0.05 was considered significant and a p value <0.10 was considered a trend.
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7

Predicting Vital Signs Changes in Intubated Infants

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We used mixed linear models in order to predict the changes in heart rate, pulse oxymetry, mean arterial blood pressure (MAP) and TcPCO2 over time based on our observations. Changes in the parameters were modeled using fractional polynomials in order to take into account non-linear time trends. In order to ensure independent observations, only the first episode of intubation was considered for the building of these models if multiple intubations were performed in the same infant at different time points.
A p value below 0.05 was considered significant. All analyses were performed using Stata v11.2 software (Statacorp, Texas, USA).
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8

Cluster-Randomized Clinical Trial Analysis

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Considering feasibility, we compared levels of missing data between intervention and control sites using Fisher's exact test. In order to adjust for age, a multilevel regression model was fitted to each clinical outcome. This is a common approach to cluster-randomised clinical trials, and utilises all data, even if a participant is missing some. Group allocation was purely on the basis of site, forming an intention-to-treat analysis. Interparticipant variability was represented as a random intercept, and age, time and group allocation were included as fixed effects. Group differences were quantified at 6 and 12 weeks, and a composite null hypothesis that both were equal to zero was assessed by Wald tests. This represents no mean difference between groups in how the outcomes change over time. These analyses were conducted in Stata V.11.2 software (StataCorp, College Station, Texas, USA), using command ‘xtmixed’.
Sample size calculations for a future trial were calculated using Stata software (command ‘sampsi’), assuming SDs observed in this study for NEADL and SAQOL, 80% power requirement and a range of putative minimum clinically important differences (MCIDs): NEADL from 2 to 5 in steps of 0.1, and SAQOL from 0.1 to 0.5 in steps of 0.01.
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9

Assessing Severity Criteria and Interventions

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We first compared cases and controls. We then compared groups of patients with and without major interventions. We assessed the relevance of WHO severity criteria by determining whether they were associated with at least one major intervention being performed. We also assessed the relevance of anaemia and thrombocytopenia thresholds by determining whether they were associated with at least one major intervention being performed and/or with any other WHO severity criteria (excluding anaemia and blood product transfusion). A descriptive analysis was performed. Continuous variables were described with medians and IQRs (medians (first; third quartile)), or means and SDs (mean±SD) for variables with a Gaussian distribution. χ 2 test and Fisher's exact tests were used to analyse categorical variables and Wilcoxon rank sum tests were used for continuous data, as appropriate. We considered p values <0.05 to be statistically significant. If the observed rate for controls was 0, we calculated an approximate OR by adding 0.5 to each cell. 11 (link) Thrombocytopenia was analysed dichotomously after setting a relevant threshold by screening sensitivityspecificity pairs by receiver operating characteristics (ROC) curve analysis. Missing data were not imputed. Statistical analyses were performed with Stata V.11.2 software (STATA, College Station, Texas, USA).
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10

Latine Substance Abuse and Discrimination

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Analyses began with simple descriptive analyses, including bivariate associations between substance abuse and independent variables. To determine if the relationship between discrimination and substance abuse held after adjusting for all other covariates, multinomial logistic regression, was used (the baseline category was non-use). Multinomial logistic regression was used instead of ordinal logistic regression since a Brant test showed the parallel regressions assumption was violated. Preliminary analyses using interaction terms showed that gender moderated discrimination; the final models presented here are stratified by gender for a more parsimonious interpretation (multinomial models with interaction terms can be difficult to interpret). Given the number of variables related to immigrant characteristics, collinearity diagnostics were carried out and no problems were identified. Analyses were weighted to be representative of the US Latina/o population (Heeringa, et al., 2004 (link)) and carried out using the Stata (v11) software (StataCorp, 2009 ).
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