Stata 15

Manufactured by StataCorp

Sourced in United States, United Kingdom, Austria, Denmark

Stata 15 is a comprehensive, integrated statistical software package that provides a wide range of tools for data analysis, management, and visualization. It is designed to facilitate efficient and effective statistical analysis, catering to the needs of researchers, analysts, and professionals across various fields.

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Lab products found in correlation

Sas 9, by SAS Institute (82 mentions) Stata statistical software release 15, by StataCorp (54 mentions) Prism 8, by GraphPad (28 mentions) Sas version 9, by SAS Institute (22 mentions) Stata statistical software, by StataCorp (18 mentions) R version 3, by R Project for Statistical Computing (13 mentions) Prism 7, by GraphPad (13 mentions) Sas 9, by StataCorp (13 mentions) Prism 9, by GraphPad (11 mentions) Stata, by StataCorp (10 mentions) Sas v9, by SAS Institute (10 mentions) Spss 22, by IBM (7 mentions) Spss 24, by IBM (6 mentions) Spss 25, by IBM (6 mentions) Sas enterprise guide 7, by SAS Institute (6 mentions) Prism 5, by GraphPad (5 mentions) Revman 5, by StataCorp (5 mentions) Spss 23, by IBM (5 mentions) Stata statistical software release, by StataCorp (4 mentions) Stata version 15, by StataCorp (4 mentions)

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Stata (4799 citations) STATA ver. 15.0 (37 citations) Stata 15.1 MP (14 citations) Stata 15.1 statistical software (11 citations) STATA 15.0 MP (11 citations) STATA version 15.0 for Windows (8 citations) Stata/SE 15.0 for Windows (7 citations) Statistics Data Analysis (STATA) software, version 15.1 (2 citations) Intercooled Stata SE 15.0 (2 citations) Stata Statistics Software version 15.0 (2 citations)

4 034 protocols using stata 15

Data Management and Quality Control in Clinical Research

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Data from patient charts and referral logs were double-entered and managed in REDCap [39 (link)], a secure online data capture system with built-in data entry restrictions, data quality tools, and protection of personally identifiable information. Quality control measures have been in place to check the data at various stages on a routine basis using REDCap [39 (link)] and Stata 15 [40 ]. REDCap’s data quality features were utilized to ensure that data was entered within acceptable ranges and in the proper formats [39 (link)]. Additionally, data were checked for consistency and errors using a Stata 15 [40 ] script. All discrepancies have been resolved by checking original paper charts.
Most implementation and process indicators will be measured and presented using descriptive statistics. We will analyze changes in quantitative outcomes where relevant, by evaluating the difference in response to the indicators between pre- and postimplementation. Measurements of differences in continuous data will be assessed using t-tests for data that is normally distributed; otherwise, a non-parametric test will be performed. Categorical data will be compared using a chi-squared test. Quantitative data will be analyzed using Stata 15 [40 ]. All data will be de-identified before analysis begins.

Millar K., Patel S., Munson M., Vesel L., Subbiah S., Jones R.M., Little S., Papageorghiou A.T., Villar J., Wegner M.N., Pearson N., Muigai F., Ongeti C, & Langer A. (2018). INTERGROWTH-21st Gestational Dating and Fetal and Newborn Growth Standards in Peri-Urban Nairobi, Kenya: Quasi-Experimental Implementation Study Protocol. JMIR Research Protocols, 7(6), e10293.

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Meta-analysis of Research Studies

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RevMan 5.3 software (Cochrane, London, UK ) and Stata 15.1 software (StataCorp College Station, Texas, USA) were used for our me-ta-analysis. The risk ratios (RRs) and the weighted mean differences (WMDs) were used to evaluate the effect size of categorical variables and continuous variables, respectively. The 95 % confidence interval (95 % CI) was calculated for each effect size. Heterogeneity tests were used to assess heterogeneity among the included studies. If there was no heterogeneity (I 2 < = 50 %), the overall effect size was evaluated by a fixed effects model. If there was heterogeneity (I 2 > 50 %), the overall effect size was evaluated by a random effects model. The Egger test was performed with Stata 15.1 software to assess publication bias. P < 0.05 was considered to be statistically significant.

Alghannam A.F., Jedrzejewski D., Tweddle M., Gribble H., Bilzon J.L, & Betts J.A. (2016). Reliability of Time to Exhaustion Treadmill Running as a Measure of Human Endurance Capacity. International journal of sports medicine, 37(3).

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Mosquito Density Comparison across Study Arms

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Entomological surveillance data were double entered into CS Pro 7.2 software and cleaned with Stata 15.0 (Stata Corp., College Station, TX, USA).
The mean number of mosquito bites per person per night was calculated for Mansonia spp. and Culex spp. at the household level. The mean density was compared between study arms using a mixed effect generalized linear model with a negative binomial distribution. Collection rounds and clusters were included in the model as random effects. The study arm was included as a fixed effect. An adjusted model, including baseline mean cluster-level mosquito density (either Mansonia or Culex) was also examined. Stata 15.0 software (Stata Corp., College Station, TX, USA) was used for the analyses.

Adoha C.J., Sovi A., Yovogan B., Akinro B., Accrombessi M., Dangbénon E., Odjo E.M., Sagbohan H.W., Kpanou C.D., Padonou G.G., Messenger L.A., Agbangla C., Ngufor C., Cook J., Protopopoff N, & Akogbéto M.C. (2023). Efficacy of Pyrethroid–Pyriproxyfen and Pyrethroid–Chlorfenapyr Long-Lasting Insecticidal Nets (LLINs) for the Control of Non-Anopheles Mosquitoes: Secondary Analysis from a Cluster Randomised Controlled Trial (cRCT). Insects, 14(5), 417.

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Meta-analysis of Comparative Treatments

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In this study, STATA 15.0 software was used for traditional meta-analysis and literature quality assessment. The odds ratio (OR) and 95% CI were acted as effect size indicators for dichotomous variables (overall response rate, recurrence rate, and incidence of adverse reactions). Mean difference and 95% CI were regarded as effect size indicators for continuous variables. All the included literature in this study involved pairwise comparisons, without forming a closed loop. The heterogeneity test was mainly determined by I². If there was no heterogeneity among the study results (I² ≤ 50%), the fixed-effect model was used for meta-analysis. If there was heterogeneity among the study results (I² > 50%), the heterogeneity source was further analyzed. After the exclusion of effects exerted by significant clinical heterogeneity, the random-effects model was employed for the meta-analysis. A frequency-based random-effects model, STATA15.0 software was adopted for performing a meta-analysis. In addition, data processing, evidence plots, funnel plots and forest plots were completed in turn. Me publication bias of the involved literature was evaluated with a funnel plot.

Chi K., Yan J., Zhu Y, & Wu J. (2023). Efficacy and safety of oral traditional Chinese medicine combined with conventional anti-osteoporosis drugs for osteoporosis and fractures: A meta-analysis of randomized controlled trials. Medicine, 102(51), e36634.

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Meta-analysis of Heterogeneity Tests

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Heterogeneity test was performed with Stata 15.0 statistical software (Stata Corp., College Station, TX). The bed rest time, body temperature return to normal time and white blood cells return to normal time were combined by standard mean difference (SMD) with 95%CI, while duration of operation, length of hospitalizations, and levels of inflammatory factors were combined by weighted mean difference (WMD) with 95%CI. Q-test and I2-test were used to analyze the heterogeneity of the studies included in this meta-analysis. If P > 0.100 and I²< 50%, it was considered that there was small heterogeneity among the studies, and fixed effect model was chosen; otherwise, random effect model was used to merge SMD with 95%CI[18 (link)]. The pooled relative risk (RR) with 95%CI: Was performed to analyze the risk of complications. Data of the outcomes were recorded for this meta-analysis when three or more trials reported the same outcome. Sensitivity analyses were performed to investigate the robustness of this meta-analysis. Meanwhile, the risk of publication bias was evaluated by Egger’s test, Begg’s test, and funnel plots[19 (link)]. If the heterogeneity shown P < 0.100 and I²> 50%, considered that there was large heterogeneity among the studies. Egger’s test was assessed by using Stata 15.0.

Wang Y., Sun C.Y., Liu J., Chen Y., Bhan C., Tuason J.P., Misra S., Huang Y.T., Ma S.D., Cheng X.Y., Zhou Q., Gu W.C., Wu D.D, & Chen X. (2021). Is endoscopic retrograde appendicitis therapy a better modality for acute uncomplicated appendicitis? A systematic review and meta-analysis. World Journal of Clinical Cases, 9(33), 10208-10221.

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Diagnostic Value of Serum TSH in Thyroid Cancer

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When studying the value of serum TSH concentration in the diagnosis of differentiated thyroid cancer in patients with thyroid nodules, we searched various databases. In our meta-analysis, we calculated the Spearman correlation coefficients of sensitivity logarithm and (1-specificity) logarithm to evaluate whether there is a threshold effect. We plotted a summary receiver operating characteristic (SROC) curve and calculated the area under the curve. We used sensitivity bias analysis to test the robustness of the data. Meta-Disc 1.4, Stata 15.0, and Review Manager 5.3 were used as statistical software for meta-analysis processing. The diagnostic indicators of heterogeneity between studies were analyzed using the r-test. The heterogeneity source was analyzed using meta-regression analysis. The Spearman correlation coefficient was calculated between sensitivity logarithm and (1-specificity) logarithm to evaluate the presence of a threshold effect. The summary receiver operating characteristic (SROC) curve was plotted to obtain the area under the curve and Q+ index. Funnel plots were drawn using Stata 15.0 software to evaluate publication bias. P < 0.05 was considered statistically significant for differences.

Fan X.Y., You W., Chen Y., Nie C.C., Wang X.L., Lei C., Song J, & Luo H.L. (2024). A meta-analysis of the value of serum TSH concentration in the diagnosis of differentiated thyroid cancer in patients with thyroid nodules. Heliyon, 10(2), e24391.

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Meta-Analysis Statistical Techniques

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The data were analyzed with Meta-disc 14.0 and Stata 15.0. The threshold effect was determined with Meta-disc 14.0. All of the heterogeneity degrees were assessed by the heterogeneity index (I²). The random effects model was adopted in the case of I² > 50%. Conversely, the fixed effects model was applied for the purpose of analyzing indicators such as sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR,) and area under the curve (AUC). The publication bias of the included literature (≥ 10 articles) was examined by Deeks' test on Stata 15.0.

Liu D., Sun L.M., Liang J.H., Song L, & Liu X.P. (2022). Diagnostic accuracy of ≥ 16-slice spiral computed tomography for local staging of colon cancer: A systematic review and meta-analysis. World Journal of Clinical Cases, 10(19), 6483-6495.

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Pharmacogenomics of Acenocoumarol Dosing

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Data analysis was performed with STATA 15.0^® software. The Shapiro–Wilk test was used to determine whether the sample had a normal distribution for the continuous variable weekly therapeutic dose (WTD; mg/week), that is, the acenocoumarol dosage at which patients were in the therapeutic range. The ladder command from STATA 15.0^® was used to choose the best normal distribution expression. Finally, a linear regression analysis was performed among genetic (SNPs) and non-genetic variables with the logarithm of the WTD in the therapeutic range (2.0–3.0), incorporating adjustment variables (p-value > 0.05). The performance of the algorithm was evaluated by calculating the adjusted coefficient of determination (R²) that represents the variability explained by the model. Hardy–Weinberg equilibrium (HWE) was tested for all SNPs using chi² test.

Roco A., Nieto E., Suárez M., Rojo M., Bertoglia M.P., Verón G., Tamayo F., Arredondo A., Cruz D., Muñoz J., Bravo G., Salas P., Mejías F., Godoy G., Véliz P, & Quiñones L.A. (2020). A Pharmacogenetically Guided Acenocoumarol Dosing Algorithm for Chilean Patients: A Discovery Cohort Study. Frontiers in Pharmacology, 11, 325.

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Lumefantrine Pharmacokinetics in ACT/ART Patients

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Plasma concentrations of lumefantrine were analyzed using noncompartmental pharmacokinetic analysis (NCA), employing the trapezoidal rule with cubic splines. Observed lumefantrine concentrations below the lower limit of quantification (0–14 days, maximum concentration (C_max), time to maximum concentration (t_max), and terminal elimination half-life (t_1/2). We used Stata 15.0 for the NCA and to compare log-transformed PK parameters. Geometric mean ratios with 95% confidence intervals (CI) are presented. To test for significant differences in PK parameters between each ACT/ART group and the ART-naive group, parametric evaluation of the log-transformed PK parameters was done using analysis of variance (ANOVA) (α = 0.05). Fisher's exact test was used to compare proportions of participants across the study groups with day 7 concentrations that were above a value known to predict treatment response by day 28 and of safety parameters across the different ACT/ART groups in comparison to the ART-naive group. Data summaries and graphics were all performed in Stata 15.0.

Banda C.G., Dzinjalamala F., Mukaka M., Mallewa J., Maiden V., Terlouw D.J., Lalloo D.G., Khoo S.H, & Mwapasa V. (2018). Impact of Efavirenz-, Ritonavir-Boosted Lopinavir-, and Nevirapine-Based Antiretroviral Regimens on the Pharmacokinetics of Lumefantrine and Safety of Artemether-Lumefantrine in Plasmodium falciparum-Negative HIV-Infected Malawian Adults Stabilized on Antiretroviral Therapy. Antimicrobial Agents and Chemotherapy, 62(11), e01162-18.

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Noncompartmental Pharmacokinetic Analysis of AQ/DESAQ

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Plasma concentrations of AQ/DESAQ were analyzed using noncompartmental pharmacokinetic analysis (NCA), employing the trapezoidal rule with cubic splines. Observed AQ/DESAQ concentrations below LLOQ were treated as missing data except for the predose concentration, which was imputed to 0 if below LLOQ. For each study participant, the following PK parameters were computed: AUC_0–28, maximum concentration [C_max], time to maximum concentration [T_max], and terminal elimination half-life [t_1/2]. We used Stata 15.0 for the NCA and to compare PK parameters. The two-sample Wilcoxon rank sum (Mann-Whitney U test) was used to test any significant differences in PK parameters between each ACT/ART arm and the control arm (α = 0.05). Geometric means and their 95% confidence intervals are reported. Fisher's exact test was used to compare proportions of participants across the study groups with day 7 concentrations that were above a value known to predict treatment response by day 28, and of safety parameters across the different ACT/ART groups in comparison to the ART-naive group. Data summaries and graphics were all performed in Stata 15.0.

Banda C.G., Dzinjalamala F., Mukaka M., Mallewa J., Maiden V., Terlouw D.J., Lalloo D.G., Khoo S.H, & Mwapasa V. (2018). Pharmacokinetics and Safety Profile of Artesunate-Amodiaquine Coadministered with Antiretroviral Therapy in Malaria-Uninfected HIV-Positive Malawian Adults. Antimicrobial Agents and Chemotherapy, 62(7), e00412-18.

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