STATA version 13.1 and SPSS (version 24, IBM, USA) were used for the analysis. Descriptive statistics were summarized as measures of central tendency (mean, median) and dispersion (standard deviation and inter-quartile range). Statistical associations for proportional data with dichotomous outcomes were tested using chi-squared test. For continuous data, Shapiro-Wilk test was used to assess the normality of data and then appropriate parametric (independent samples T test) or non-parametric tests (Mann-Whitney rank sum test) were used. Statistical significance was set at p < 0.05 with appropriate adjustments for multiple comparisons with a Bonferroni correction. Diagnostic tests were compared using two methods; a) with RT-PCR as the gold standard comparator and b) Bayesian latent class model (BLCM) analysis which assumes no gold standard. Sensitivity, specificity, positive predictive (PPV), negative predictive value (NPV) for each diagnostic test was calculated [20 ].
Stata version 13
Manufactured by IBM
Sourced in United States
Stata version 13 is a statistical software package developed by StataCorp. It provides a comprehensive set of tools for data management, analysis, and visualization. Stata 13 includes features for regression analysis, time series analysis, multilevel modeling, and more. It is commonly used in various fields, including economics, social sciences, and health research.
Automatically generated - may contain errors
11 protocols using stata version 13
1
Diagnostic Test Comparison for Disease
2
Quantitative Self-Report Outcomes Analysis
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All self-report outcome measures were quantitative and were summarised by their mean and standard deviation. Independent sample ‘t’ tests were conducted to investigate potential differences between programme completers and drop-outs at baseline. For each outcome measure, multilevel linear mixed-effects regression models were used to estimate the mean at baseline (T1) and the mean change from baseline to each of the subsequent time-points. Mixed-effects models use all available data at each time-point rather than data from individuals assessed at all times. We included a random intercept in the models for the individual participants. These intercepts adjust for random heterogeneity in each outcome measure between subjects. Data were analysed using Stata version 13.1 and IBM SPSS Statistics 23.0 for Windows.
3
Statistical Analysis of Intellectual Disability
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Data were analysed using Stata (version 13) and Statistical Product and Service Solutions (SPSS, version 21). For categorical data, comparisons between patients in these two settings were completed using cross-tabulation and χ2-tests. For continuous (‘scale’) data, comparisons were made using a non-parametric test (Mann–Whitney), because a number of variables deviated from an approximately normal distribution. Descriptive statistics for the whole sample, and comparing patients in the intellectual disability group and patients in the non-intellectual disability group are reported. The significance level for differences was set at P<0.05. In studies that involve multiple testing, the likelihood of a type I error (i.e. concluding that a significant difference is present when it is not) increases with the number of tests involved, and an adjustment to this threshold is often considered appropriate.11 (link) However, it has been argued that the decision as to whether to correct or not should depend on the circumstances of the study.12 (link),13 (link) In this case the decision was made that such a correction would be inappropriate. This is justified because the current study is exploratory; and it was considered important to avoid a type II error (i.e. concluding that a significant difference is not present when in fact it is).
4
Factors Influencing Contraceptive Use
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Descriptive and inferential analyses were done. The descriptive analysis reflected results on women empowerment indicators and the proportion using contraceptives. Multinomial logistic regression was employed for the inferential analysis and this is because the outcome variable was nominal in nature with three outcomes (not using contraceptive, using short acting contraceptive and using long-acting contraceptive). The results for the multinomial logistic regression were presented as adjusted odd ratios (aOR) along with the respective 95% confidence intervals (CIs) signifying precision, using SPSS. The sample weight (wt) was used to account for the complex survey (svy) design and generalizability of the findings. All the analyses were done with Stata version 13 and SPSS version 25.
5
Predictors of Tuberculosis Knowledge and Stigma
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The data were analyzed using statistical software STATA version 13 and statistical package for social science (SPSS) version 22. The Multivariable logistic regression was used to identify the predictive variables of the overall TB knowledge and perceived stigma. The significant differences in the two group values were evaluated using a significance level of 0.05 and 95% confidence interval.
6
Prescribing Problems Among GP Trainees
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The pooled prevalence of all the prescribing problems identified across the 10 GPs-in-training were recorded. The PRACtICe study had also found it useful to report prescribing problems at the level of British National Formulary (BNF) chapter.3
Statistical analysis was performed with Stata (version 13) and SPSS (version 26). Categorical data were summarised with frequency counts and percentages, means and standard deviations (SD) were calculated for continuous variables (mean ± SD).
Statistical analysis was performed with Stata (version 13) and SPSS (version 26). Categorical data were summarised with frequency counts and percentages, means and standard deviations (SD) were calculated for continuous variables (mean ± SD).
7
Validity Assessment of EQ-5D and SF-6D
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The psychometric validity of the EQ-5D and SF-6D was assessed in comparison to each other and clinical indicators using standard criteria. There is no gold standard for the assessment of validity, so therefore the validity of measures are assessed in relation to each other, and guided by clinically specific indicators. Statistical analyses were performed using Stata version 13 and SPSS version 21.
8
Determinants of PTSD and Depression Co-occurrence
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Data analyses were conducted using Stata version 13 and SPSS version 21.0 for Windows. The dependent variable was classified into four categories: “none” (participants who had neither PTSD nor depression), “depression only” (participants who had depression only), “PTSD only” (participants who had PTSD only), and “co-occurrence” (participants with both PTSD and depression). Independent variables included the socio-demographic and lifestyle variables, trauma exposure, social support, domestic violence, displacement, and life events.
Characteristics of the study sample across the four diagnostic categories were summarized using descriptive statistics and compared using ANOVA, chi-square, and Fisher's exact tests as appropriate. The prevalence of co-occurrence was computed, as well as the prevalence of PTSD among individuals with depression and the prevalence of depression among individuals with PTSD.
Multinomial logistic regression was conducted to assess the unadjusted and adjusted associations between the dependent variable and the set of independent variables. Variables that were significant at the bivariate level were included in the stepwise multivariable regression model. Odds ratios and their 95% confidence intervals were reported. Significance level was set at α=0.05.
Characteristics of the study sample across the four diagnostic categories were summarized using descriptive statistics and compared using ANOVA, chi-square, and Fisher's exact tests as appropriate. The prevalence of co-occurrence was computed, as well as the prevalence of PTSD among individuals with depression and the prevalence of depression among individuals with PTSD.
Multinomial logistic regression was conducted to assess the unadjusted and adjusted associations between the dependent variable and the set of independent variables. Variables that were significant at the bivariate level were included in the stepwise multivariable regression model. Odds ratios and their 95% confidence intervals were reported. Significance level was set at α=0.05.
9
Breast Cancer Biomarker Expression Analysis
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Data collected were analyzed by using STATA version 13.0 (IBM Statistics, Chicago, USA). For checking errors and missing data, frequency tables and crosstabs were used. Continuous variables were presented in mean ± standard deviation (SD). Categorical variables were summarized in proportion. Chi-square statistical test was used to determine the association of expression of HRs (ER and PR) and HER2 with clinicopathological characteristics (age, tumour grade and histological types). Logistic regression analysis was used to determine the predictors of ER, PR and HER2 expression. Odds ratio (OR) at 95% confidence interval (CI) was used to measure the risk of not expressing the breast markers. A two tailed p < 0.05 was considered significant.
10
Perinatal Outcomes and Redox/Inflammatory Markers
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Statistical analyses were performed using Stata version 13.0 software and IBM Statistical Package for the Social Sciences (SPSS) software 20.0 (SPSS Inc., USA), adopting α = 0.05. To compare the socioeconomic, obstetric, and nutritional status characteristics of the studied groups, a chi-square test was performed. To evaluate normality, the Lilliefors test was used. Then, a visual graphical analysis was performed with a QQ plot, and it was decided to use nonparametric investigations due to violations of normality. After this, Wilcoxon–Mann–Whitney tests were performed. Finally, the results of the biomarkers evaluated in this study were related to the perinatal variables (birth weight, gestational age, the Apgar scores in the 1st and 5th min, HC, CC/HC, length at birth, and birth complications) through the multinomial and Poisson regression, adjusting for maternal age, origin, education, family income, gestational BMI, black race, primigravida, mode of delivery, and gestational age. In addition, in order to investigate if there were significant interactions between the different redox/inflammatory markers and PE for each of the outcomes, the interaction term biomarkers∗PE was also included in each outcome regression model, considering p < 0.05 as significant.
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