The distributions of findings were summarised in Excel, while all statistical analyses were performed in Stata 17.0 (StataCorp. 2021. College Station, TX: StataCorp LLC). The difference in prevalence of lambs with footrot between the present and the previous (2009) national screening was assessed using a two-sample test of proportions (using the prtest command in Stata 17.0). Associations between footrot scorings (dependent variable), detections of D. nodosus, F. necrophorum, Treponema spp., individually as well as in combination, and region (independent variables) were investigated using univariable multinomial logistic regression analysis (using the mlogit command in Stata 17.0). To investigate the differences between all categories of the dependent variable, this model was re-run changing the base category until all categories had been compared with each other. Associations between detections of D. nodosus, F. necrophorum and Treponema spp. (all as dependent variables in separate analyses) and region (independent variable) were investigated by univariable logistic regression analysis (using the logit command in Stata 17.0). As only univariable analyses were performed, the fit of the multinomial logistic and the logistic models was not investigated.
Stata 17
Manufactured by StataCorp
Sourced in United States, Austria
Stata 17 is a statistical software package developed by StataCorp. It provides a comprehensive set of tools for data analysis, data management, and statistical modeling. Stata 17 is designed to handle a wide range of data types and offers a variety of statistical methods and algorithms.
Automatically generated - may contain errors
Lab products found in correlation
Stata statistical software release 17, by StataCorp (20 mentions)
Sas 9, by SAS Institute (20 mentions)
Prism 9, by GraphPad (10 mentions)
Stata statistical software, by StataCorp (8 mentions)
Prism 8, by GraphPad (5 mentions)
Amos 24, by IBM (4 mentions)
R software version 4, by R Project for Statistical Computing (4 mentions)
Sas v9, by SAS Institute (4 mentions)
Spss version 22, by IBM (3 mentions)
Spss version 28, by IBM (3 mentions)
Rstudio software, by Posit (3 mentions)
Spss version 20, by IBM (2 mentions)
Java 9, by Oracle (2 mentions)
R version 4, by R Project for Statistical Computing (2 mentions)
Spss version 25, by IBM (2 mentions)
Spss statistics 25, by IBM (2 mentions)
Spss 23, by IBM (2 mentions)
High capacity cdna reverse transcription kit, by Thermo Fisher Scientific (2 mentions)
Sas software version 9, by SAS Institute (2 mentions)
Statview 5, by SAS Institute (2 mentions)
1 292 protocols using stata 17
1
National Footrot Prevalence in Lambs
2
Comprehensive Health Resource Density Analysis
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Microsoft Excel 2019 was used to calculate the comprehensive health resource density index. Stata 17.0 was used in plotting the distribution of average efficiency, MaxDEA 7 Ultra for Bootstrap-dea, and Stata 17.0 for spatial econometric models.
3
Meta-Analysis of Nutritional Factors in Adverse Reactions
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The meta-analysis was performed using STATA 17.0. Systematic evaluation was performed through a meta-analysis of continuous variables (BMI, albumin, prealbumin, hemoglobin, lymphocyte count, and total albumin index) and dichotomous variables (incidence of adverse reactions). Data were analyzed using the STATA 17.0 statistical package (Cochrane Collaboration Software). Data of dichotomous outcomes were expressed as odds ratios with 95% confidence intervals (CIs) and standardized mean difference (SMD). A test of heterogeneity was performed with the I2 test and Q statistic. An I2 value of > 50% or P value of < .05 was assumed to indicate significant heterogeneity. Publication/reporting biases were visually assessed using funnel plots. If there was no observed heterogeneity, then the fixed-effects model was chosen; otherwise, the random-effects model was used.[22 (link),23 (link)]
4
Meta-analysis of treatment outcomes
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Meta-analysis was performed using RevMan 5.3 and Stata 17.0 software. Count data were analyzed by relative risk ratio (RR), and continuous data were analyzed by mean difference (MD) or standardized mean difference (SMD). Also, their combined effect sizes and their 95% confidence intervals (CI) were calculated. Heterogeneity was analyzed using the Q test and I2 test.[34 (link)] If there was no statistical heterogeneity between studies (I2 < 50%, P > .10), a fixed-effects model was used for analysis, otherwise, a random-effects model was used. The same outcome indicators were analyzed in subgroups according to disease type, treatment regimen, and the duration of treatment. Sensitivity analysis was performed using Stata 17.0 software. The publication bias analysis was performed using inverted funnel plots. P < .05 was considered statistically significant.
5
Cardiometabolic Multimorbidity and Disability
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Given the ordinal nature of the disability severity levels (i.e. no disability, moderate, and severe) identified from the LCA, weighted ordinal regression was used to model the association of cardiometabolic multimorbidity classes with disability levels on a pooled dataset from the two study countries. Because of the clustered design of the sample, robust variance estimates (Huber-White sandwich estimator) were used for the correction of standard errors to adjust for the correlation among responses within the same household [52 ].
Bivariable ordered logistic regression analysis with levels of disability as the outcome variable was first fitted for each of the multimorbidity classes, followed by a multivariable model adjusting for socio-demographic characteristics namely age, sex, education, employment status, and place of residence. There was no evidence of a violation of the assumption of parallel slopes using the command ‘brant’ in Stata 17.0 (StataCorp LP, Texas, USA). The likelihood ratio test was used to compare the goodness of fit of the models. We used the adjusted odds ratio (aOR) and 95% Confident Interval (CI) to interpret the strength and direction of associations.
All statistical analyses were carried out using Stata 17.0 (StataCorp LP, Texas, USA) and accounted for the complex sampling design used in the SAGE survey.
Bivariable ordered logistic regression analysis with levels of disability as the outcome variable was first fitted for each of the multimorbidity classes, followed by a multivariable model adjusting for socio-demographic characteristics namely age, sex, education, employment status, and place of residence. There was no evidence of a violation of the assumption of parallel slopes using the command ‘brant’ in Stata 17.0 (StataCorp LP, Texas, USA). The likelihood ratio test was used to compare the goodness of fit of the models. We used the adjusted odds ratio (aOR) and 95% Confident Interval (CI) to interpret the strength and direction of associations.
All statistical analyses were carried out using Stata 17.0 (StataCorp LP, Texas, USA) and accounted for the complex sampling design used in the SAGE survey.
6
Bayesian Network Meta-Analysis of Supplements
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Bayesian network meta-analysis (NMA) will be conducted using ADDIS 1.16.8 and Stata 17.0 software to compare the effectiveness of supplement interventions. ADDIS is a network analysis software that uses a Bayesian framework and the Markov chain Monte Carlo (MCMC) method.43 (link) It facilitates prior evaluation and data processing, enabling automatic network meta-analysis of relevant data. Efficacy and adverse reactions will be expressed as odds ratios for count data, while measurement data will be expressed as weighted mean differences. All effect sizes will be reported with a 95% CI. For all data, the node-split model will be initially used for the consistency test. If there is no statistical difference between direct and indirect comparisons (p>0.05), the consistency model will be used. Otherwise, the inconsistency model will be used for analysis.
White44 45 (link) reported that the ‘network’ command will be inputted in Stata 17.0 for data preprocessing, generating network maps and determining the efficacy of intervention measures. In network maps, nodes represent interventions, and each line between two nodes signifies a direct comparison between these two interventions. Studies not connected to the network will be excluded from the network meta-analysis.
White44 45 (link) reported that the ‘network’ command will be inputted in Stata 17.0 for data preprocessing, generating network maps and determining the efficacy of intervention measures. In network maps, nodes represent interventions, and each line between two nodes signifies a direct comparison between these two interventions. Studies not connected to the network will be excluded from the network meta-analysis.
7
Examining Food Insecurity and Obesity
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We imputed data on the key independent variables and covariates using the mi impute command with chained equations in Stata 17. Following recommended practices, we created 20 imputed data sets (Graham et al., 2007 (link)).
We first present sample characteristics. We then tested whether food insecurity status and food environments were associated with HEI-2015 scores and obesity using ordinary least squares (OLS) regression and logistic regression. In Model 1, we tested the independent associations of food insecurity and food environment with HEI-2015 scores and obesity. In Model 2, we included the interactive effect of food insecurity and food environment on the outcomes. In models predicting HEI-2015 scores, we controlled for sociodemographic characteristics. Models predicting obesity additionally controlled for health behaviors (i.e., physical activity, smoking, diet quality). Sample weights provided by the HRS were applied in all analyses to account for the complex survey design and sample composition. Analyses were conducted using Stata 17.
We first present sample characteristics. We then tested whether food insecurity status and food environments were associated with HEI-2015 scores and obesity using ordinary least squares (OLS) regression and logistic regression. In Model 1, we tested the independent associations of food insecurity and food environment with HEI-2015 scores and obesity. In Model 2, we included the interactive effect of food insecurity and food environment on the outcomes. In models predicting HEI-2015 scores, we controlled for sociodemographic characteristics. Models predicting obesity additionally controlled for health behaviors (i.e., physical activity, smoking, diet quality). Sample weights provided by the HRS were applied in all analyses to account for the complex survey design and sample composition. Analyses were conducted using Stata 17.
8
Investigating Ketone Effects on Cardiac Output
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The SD of CO (primary end point) is 0.8 L/min.4 By enrolling 12 patients, a relative difference in CO of 20% can be detected with a power of 90% and a 2‐sided significance level of 5%. For the niacin part of the study, 10 patients completed the study. Post‐hoc power for this part of the study was 88.5%, based on the given parameters of 10 participants, a 20% difference in CO, and an SD of 0.8 L/min. Randomization was made in STATA 17 (StataCorp, USA) by the investigator. Participants were blinded to the randomization order. Data were inspected for normal distribution as required. Baseline data (Table 1 ) are presented as mean±SD if normally distributed or median (interquartile range) if not normally distributed. Paired 2‐tailed t test was used to compare the effect of the interventions with that of placebo. A 2‐sample t test was used to compare the means of continuous variables between the 2 groups: ketone/placebo with the preceding aspirin and ketone/placebo without aspirin, whereas Fisher exact test was utilized to compare the categorical variables between the 2 groups. Differences are presented as mean with 95% CI. A P‐value <0.05 was considered statistically significant. Data analyses were performed with STATA 17 (StataCorp, USA). All figures were created with Prism 8 (Graphpad Software, LLC).
9
Peanut SLIT Desensitization Kinetics
10
Evaluating Growth Performance and Salmonella Prevalence
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Growth performance was calculated both on a deads and removals-included and excluded basis. Data were analyzed as a complete block design using ANOVA (Stata 17; Statacorp, College Station, TX) with a pen as the experimental unit. The model included the fixed effects of treatment and block. Categorical data (removals, mortality, carcass grade distributions, and liver and lung scores) were analyzed using logistic regression (binreg; Stata 17), with treatment and block included in the model as fixed effects.
Salmonella counts were determined based on mean plate counts and dilution factor were log-transformed to achieve normality and analyzed using the PROC GLIMMIX procedure of SAS (SAS v 9.4, SAS Inst. Inc., Cary, NC) with the fixed effects of treatment, time, and the interaction. The pen was utilized as the experimental unit, and quantification data are reported as log CFU/g. Prevalence data were analyzed as binomial proportions in PROC GLIMMIX of SAS and reported as a percentage of positive samples within each treatment. For all data, α = 0.05, and P-values between 0.05 and 0.10 were discussed as trends.
Salmonella counts were determined based on mean plate counts and dilution factor were log-transformed to achieve normality and analyzed using the PROC GLIMMIX procedure of SAS (SAS v 9.4, SAS Inst. Inc., Cary, NC) with the fixed effects of treatment, time, and the interaction. The pen was utilized as the experimental unit, and quantification data are reported as log CFU/g. Prevalence data were analyzed as binomial proportions in PROC GLIMMIX of SAS and reported as a percentage of positive samples within each treatment. For all data, α = 0.05, and P-values between 0.05 and 0.10 were discussed as trends.
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