Data were entered in Microsoft Excel 2016, and analysed using Stata IC version 13 statistical package (Texas, USA). Frequencies and percentages were computed for categorical variables and group comparisons done using the chi-squared test (or Fisher’s exact test where appropriate). Basic logistic regression models were used to investigate factors associated with non-adherence to anti-diabetic medication. Candidate predictor variables which were significant in the basic models were included in the multivariable regression model. Statistical significance was set at p-value < 0.05.
Excel 2016
Manufactured by StataCorp
Sourced in United States
Excel 2016 is a spreadsheet software application developed by Microsoft. It provides a grid-based interface for organizing, analyzing, and visualizing data. Excel 2016 includes tools for performing calculations, creating charts and graphs, and automating various data-related tasks.
Automatically generated - may contain errors
Lab products found in correlation
Stata 16, by StataCorp (4 mentions)
Stata version 13, by StataCorp (3 mentions)
Stata version 15, by StataCorp (3 mentions)
Stata 15, by StataCorp (3 mentions)
Stata statistical software version 14, by StataCorp (2 mentions)
Stata 17, by StataCorp (2 mentions)
Stata version 16, by StataCorp (2 mentions)
Stata, by StataCorp (2 mentions)
Stata v 15, by StataCorp (2 mentions)
Stata 13, by StataCorp (2 mentions)
Stata 14, by StataCorp (2 mentions)
Stata v 13, by StataCorp (2 mentions)
Stata mp 13, by StataCorp (1 mentions)
Stata statistical software version 12, by StataCorp (1 mentions)
Stata version 12, by StataCorp (1 mentions)
Stata se 15, by StataCorp (1 mentions)
Stata 12, by StataCorp (1 mentions)
Spss v25, by IBM (1 mentions)
Stata mp, by StataCorp (1 mentions)
Stata mp 14, by StataCorp (1 mentions)
49 protocols using excel 2016
1
Factors Influencing Diabetic Medication Adherence
2
Determinants of Dissatisfaction with Local Medical Services
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Multilevel mixed-effect models were applied to explore the determinants for dissatisfaction with local medical services from the individual’s perspective. These determinants in Table 1 were specified as the fixed effect, and the community where participants lived was a random effect. The odds ratios (ORs) with 95% confidence limits (CLs) in multilevel mixed-effects logistic regression (model 1) demonstrated the risk of occurring dissatisfaction among various characteristics, and the coefficients in multilevel mixed-effects linear regression with 95% CLs (model 2) showed the extent to which participants’ dissatisfaction was associated with various characteristics. The statistical results and figures were processed with STATA statistical software version 14.0 (StataCorp LP, College Station, TX, USA) and Excel 2016, respectively. A two-tailed p-value of <0.05 was considered statistically significant.
3
Prevalence and Trends in BSA Use
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Data were extracted from the database of the NIPH and all eight prevalence registrations were analyzed together. During the study period, no new guidelines or recommendations were issued.
The use of BSA is presented as proportion of all prescriptions and as proportion of all patients.
The data are categorical and presented as numbers and percentages. Confidence intervals (CI) for proportions was calculated using binomial distribution. Statistical comparisons were performed using a chi-square test with p-value <0.05 for significance. For analyses we used Microsoft Excel 2016 and Stata version 15.1.
The use of BSA is presented as proportion of all prescriptions and as proportion of all patients.
The data are categorical and presented as numbers and percentages. Confidence intervals (CI) for proportions was calculated using binomial distribution. Statistical comparisons were performed using a chi-square test with p-value <0.05 for significance. For analyses we used Microsoft Excel 2016 and Stata version 15.1.
4
Statistical Analysis of Clinical Data
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Clinical and epidemiological data were organized using Excel
2016 and the analysis was done using Stata/MP
13.0. For categorical variables, Fisher's exact test was used and the
results are presented in tables of absolute and relative frequencies followed by
the corresponding p-value. For continuous variables, normal distribution was
tested using Shapiro-Wilk normality test, if the normal distribution was
observed, an unpaired t-test (Student t-test) was executed and results presented
by mean ± SD, otherwise, the Wilcoxon rank-sum (Mann-Whitney) test was
used and the results are presented by median and interquartile intervals. We
assumed a confidence interval of 95% (CI 95%, p < 0.05) for all statistical
tests.
2016 and the analysis was done using Stata/MP
13.0. For categorical variables, Fisher's exact test was used and the
results are presented in tables of absolute and relative frequencies followed by
the corresponding p-value. For continuous variables, normal distribution was
tested using Shapiro-Wilk normality test, if the normal distribution was
observed, an unpaired t-test (Student t-test) was executed and results presented
by mean ± SD, otherwise, the Wilcoxon rank-sum (Mann-Whitney) test was
used and the results are presented by median and interquartile intervals. We
assumed a confidence interval of 95% (CI 95%, p < 0.05) for all statistical
tests.
5
Validated Tools for Chest X-Ray Screening
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Data collection tools were pretested to ensure reliability and validity, questionnaires were translated into local language (Runyankole). Legibility was ensured by daily auditing of the questionnaires. Chest x-rays were taken by a radiologist and interpreted properly. Radiographic films were attached to the respective questionnaires to avoid misplacement. Privacy and confidentiality were ensured by conducting interviews in the closed clinic and all data collected kept in a lock and password protected computer. After collection, data was arranged, coded and entered into the computer using the EXCEL 2016 then imported to STATA version 13.0 (Statacorp, College station, USA) for analysis.
6
Determinants of Organ Donation Willingness
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A database was established using EpiData 3.1 software (developed by EpiData Association, Odense. Denmark) and data was double entered to ensure data quality. Categorical variables were summarised using number and percentages. Distributions of basic characteristics by residence, and attitude towards organ donation were compared using Chi-square tests. Logistic regression modelling was applied to identify the determinants of participants' willingness to organ donation, controlling for other confounding factors. Odds ratios (ORs) with 95% con dence intervals (CIs) were derived from logistic regression modelling. The statistical results and gures were processed with STATA statistical software version 12.0 (StataCorp LP, College station 77845, USA) software and Excel 2016, respectively. A two-tailed p value < 0.05 was considered statistically signi cant.
7
Relative Recall Across Databases
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Relative recall for each of the included database was calculated separately and for each of the included SR. Relative median recall for each database was calculated for all included SRs combined. Cumulative median relative recall was estimated for searches in MEDLINE, EMBASE and CENTRAL adding databases in descending order (based on how often the databases was searched in the SRs published by CMSG). Data managing was performed using Microsoft Excel 2016 and data analysis was performed using STATA version 13.1 (StataCorp, College Station, Texas) software package.
8
Gastrointestinal Parasites Prevalence Assessment
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The collected data were entered and coded on Microsoft Excel 2016 spreadsheet, and analysis was done using STATA, version 14. Proportions were used to determine the magnitude of gastrointestinal parasites. Results were described in tables and explained briefly. Chi square and binary logistic regression procedures were used to show the presence and absence and the magnitude of association of risk factors with the prevalence of gastrointestinal parasites. Odds ratios were calculated to show the degree of difference on prevalence of parasites between different groups of a factor. For all the analyses, confidence level and p value were declared as 95% and 0.05, respectively.
9
Sociodemographic Predictors of Stress
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Descriptive analyses were conducted to investigate categorical and continuous sociodemographic variables and stress levels. Two hundred fifty-seven participants was calculated as the sample size, using G*Power, assuming that the number of predictors was fifteen, at 0.05 alpha level, 0.99 power, and 0.15 middle effect size. All independent variables with a p-value < 0.2 were added to the multiple linear regression analysis to control for any statistically insignificant confounding variables [30 ,31 (link)]. All data were encoded and entered into Microsoft Excel 2016, and statistical analyses were performed using STATA 16.
10
Comparison of GE Tracers in Dysmotility
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Data from each study participant were collected using Microsoft excel 2016, and were analyzed using STATA 13.1 package, MP – parallel edition (StataCorp LP, College Station, Texas, USA).
The results of variables such as sex, esophageal dysmotility, significant GER, PA, spleen, liver, and bone marrow uptake were presented as frequencies and percentages. Age was represented as the mean and SD.
A two-sample t-test was used to analyze the difference between the group means of the GE of the two tracers.
The results of variables such as sex, esophageal dysmotility, significant GER, PA, spleen, liver, and bone marrow uptake were presented as frequencies and percentages. Age was represented as the mean and SD.
A two-sample t-test was used to analyze the difference between the group means of the GE of the two tracers.
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