Stata 17

Manufactured by IBM

Stata 17.0 is a comprehensive software package for data analysis, statistical modeling, and graphics. It provides a wide range of tools for data management, analysis, and presentation. Stata 17.0 supports various data types and can be used for a variety of statistical techniques, including regression analysis, time series analysis, and multilevel modeling.

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Lab products found in correlation

Spss version 26, by IBM (1 mentions) Spss version 14, by IBM (1 mentions)

Close spelling variants of this product

Stata (23 citations) Stata Statistical Software: Release 17 (2 citations)

5 protocols using stata 17

Diagnostic Accuracy of P-LACT in Clinical Practice

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Descriptive statistics are given as mean [95% CI] or median [IQR]. Comparisons across groups were made using Fisher’s exact test and ANOVA Kruskal–Wallis test where appropriate. Sensitivity, specificity, and positive- and negative likelihood ratios as well as the percentage correctly classified were calculated for P-LACT values in the range between 1 and 10 mmol/L. Finally, predicted probabilities [95% CI] of the occurrence of the defined endpoint were calculated for each of these values using logistic regression analysis.
Missing values are reported in the results section of the manuscript according to the STROBE guideline [11 ]. A p value < 0.05 was regarded as statistically significant. Statistical analyses were conducted using Stata 17.0 and SPSS 26.0.

Griggs J.E., Lyon R.M., Sherriff M., Barrett J.W., Wareham G, & ter Avest E. (2022). Predictive clinical utility of pre-hospital point of care lactate for transfusion of blood product in patients with suspected traumatic haemorrhage: derivation of a decision-support tool. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 30, 72.

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Analyzing Health Impacts of Long COVID

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The rank-sum test of two independent samples and the Kruskal–Wallis test were used to analyze the differences in health status scores between the long COVID cases and non-long COVID cases. The frequencies and proportions of categorical variables were calculated, along with their corresponding health status scores while continuous variables were categorized into quartiles, and the frequencies and proportions within each quartile were reported along with the respective health status scores. The factors influencing the health status score were analyzed by using linear regression models. A bias-corrected non-parametric percentile bootstrap method was used to verify the mediating effect of lifestyle on the impact of long COVID on the health status of patients. To minimize the potential influence of confounding factors, propensity score matching was employed. Covariate selection was carried out using probit regression, and the propensity score was calculated through probit regression as well. Subsequently, 1:1 nearest neighbor matching was conducted between the long COVID and non-long COVID populations among the 3,165 individuals, ensuring a balanced comparison between the two groups while addressing potential biases. All analyses were performed by using STATA17.0 and SPSS27.0 statistical software, and P<0.05 was considered statistically significant.

Miao Y., Ren R., Shen Z., Li Y., Zhang W., Bai J., Zhu D., Zhang J., Guo D., Tarimo C.S., Liu R., Zhao Q., Hu J., Zhou X, & Dong W. (2024). Quantifying the Adverse Effects of Long COVID on Individuals’ Health After Infection: A Propensity Score Matching Design Study. Risk Management and Healthcare Policy, 17, 701-713.

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Normality Testing and Descriptive Analysis

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Data were captured and coded in Microsoft Excel and exported to STATA 17.0 and SPSS version 26 for analysis. Numerical data were explored for normality using the Shapiro–Wilk test. Numerical data were not normally distributed and were therefore summarised using the median and interquartile range (IQR). Frequency tables, graphs, and percentages were used to summarise categorical variables. Where data were missing, data were analysed using complete case analyses.

Chitha N., Ntsele N., Mabunda S.A., Funani I., Swartbooi B., Mnyaka O., Thabede J., Tshabalala R., Pulido-Estrada G.A., Nomatshila S, & Chitha W. (2023). Exploring the Information Sources Consulted by Doctors at the Point of Care in Four Selected South African Referral Hospitals. Healthcare, 12(1), 8.

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Preliminary Analysis of Intervention Outcomes

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All analysis will be guided by the principle of intention to treat. There will be a preliminary analysis of intervention outcomes. Point estimates and 95% confidence intervals will be calculated using adjusted means from the analysis and used to estimate standard deviations and effect sizes for continuous data. Means and standard deviations of response rates for count data and proportions in each category will be provided. These will be used to confirm the sample size calculation for a definitive study. Dependent on the recruitment within each diagnosis group, we may consider presenting preliminary results within diagnosis groups. Exploratory analysis will be performed to determine the most appropriate model of analysis for a definitive RCT, including consideration of the further possible covariates and factors to be included in an analysis model. All quantitative analysis will be completed using Stata 17, SPSS v25 and R version 3.

Suárez-González A., John A., Brotherhood E., Camic P.M., McKee-Jackson R., Melville M., Sullivan M.P., Tudor-Edwards R., Windle G., Crutch S., Hoare Z, & Stott J. (2023). "Better Living with Non-memory-led Dementia": protocol for a feasibility randomised controlled trial of a web-based caregiver educational programme. Pilot and Feasibility Studies, 9, 172.

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Diagnostic Performance of US and MRI

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US and MRI reports were compared against the reference standard (histology, surgery, or follow-up) on a per-even and per-lesion basis for each follow-up event. ROC curves for each modality and in combination were calculated. In addition, sensitivity and specificity with 95% confidence intervals (CIs) were estimated. ROC curves and standard diagnostic performance indicators were also estimated (positive predictive value (PPV), negative predictive value (NPV), the likelihood ratio for positive results (LH+), accuracy, and post-test probability (post-P). A p-value < 0.05 (2-sided) was considered statistically significant. Logistic multiple regression was performed to exclude or identify the effect of independent variables (e.g., patient age, sex, reporting radiologist) on diagnostic accuracy. Commercially available software (MedCalc Software Ltd. Diagnostic test evaluation calculator. https://www.medcalc.org/calc/diagnostic_test.php (Version 20.009; accessed 2 July 2021, SPSS version 14, Chicago, Ill and STATA 17) were used for analysis.

Bignotti B., Rossi F., Signori A., Solari N., Spina B., Martinoli C, & Tagliafico A.S. (2022). Magnetic Resonance Imaging or Ultrasound in Localized Intermediate- or High-Risk Soft Tissue Tumors of the Extremities (MUSTT): Final Results of a Prospective Comparative Trial. Diagnostics, 12(2), 411.

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