Stata se version 14

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Stata/SE version 14 is a statistical software package developed by StataCorp. It is designed to perform a wide range of data analysis, statistical modeling, and graphical visualization tasks. Stata/SE provides advanced features and capabilities for handling large datasets and complex analyses.

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Lab products found in correlation

Sas version 9, by SAS Institute (4 mentions) Sas version 9, by StataCorp (4 mentions) Spss 22, by IBM (3 mentions) Epidata version 3, by StataCorp (2 mentions) Sas v9, by SAS Institute (2 mentions) Spss statistics version 22, by IBM (2 mentions) Spss version 23, by IBM (2 mentions) Prism 6, by GraphPad (1 mentions) Spss version 22, by IBM (1 mentions) Matlab, by MathWorks (1 mentions) Spss statistics version 24, by IBM (1 mentions) Prism v5, by GraphPad (1 mentions) Stata version 10, by StataCorp (1 mentions) Version 9, by MedCalc (1 mentions) Matlab r2015a, by MathWorks (1 mentions) Statistical software version 18, by MedCalc (1 mentions) Spss version 20, by IBM (1 mentions) R statistical software, by StataCorp (1 mentions) Stata software version 15, by StataCorp (1 mentions) Stata statistical software release 14, by StataCorp (1 mentions)

276 protocols using stata se version 14

Mindfulness-based Cognitive Behavioral Therapy

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Participants were assigned a unique identification number and allocated to either the control or the MiCBT group. Blocked randomization was conducted using Stata (version 14SE) by means of alternating treatment group allocation based on blocks to yield treatment groups with balanced proportions of participants from respective blocks. A sequence of treatments (treatment or control) was created randomly permuted in blocks of size 4. Blocks of size 4 were chosen to ensure a balance of treatments in all group sessions, as groups of size 8 could begin as soon as 16 participants were recruited.
Randomization was also stratified based on three stratification variables, each having two values: K10 score (20–29, mild to moderate; ≥ 30, severe); psychotropic medication (yes/no) and gender (female/male). Therefore, a randomization schedule was generated for each of the 8 strata (2 × 2 × 2):
Stata Version 14 SE generated the randomized schedules for this study with 2 arms and with a sample size of 400, a block size of 4, or 100 blocks of 4. Randomization was conducted by a researcher who was independent from the recruitment, assessment and treatment of participants.

Francis S.E., Shawyer F., Cayoun B., Enticott J, & Meadows G.N. (2022). Group Mindfulness-Integrated Cognitive Behavior Therapy (MiCBT) Reduces Depression and Anxiety and Improves Flourishing in a Transdiagnostic Primary Care Sample Compared to Treatment-as-Usual: A Randomized Controlled Trial. Frontiers in Psychiatry, 13, 815170.

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Cost-Effectiveness of Colorectal Cancer Screening

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We calculated the incremental cost-effectiveness ratio (ICER) as the difference in mean costs (based on the 2000 simulations) between the screened and unscreened cohorts divided by the difference in mean QALYs between the 2 cohorts. We calculated the net monetary benefit (NMB) for no screening, age-based screening, and for the 99 scenarios of risk-stratified screening as the mean QALYs multiplied by a given willingness to pay (WTP) for a QALY, minus the total cost. The screening strategy with the highest NMB for a given WTP was considered the most cost-effective. We calculated incremental NMBs (screening vs no screening) to generate cost-effectiveness acceptability curves, which are a summary of the proportion of times the incremental NMB is positive, ie, that the screening strategy is cost-effective compared with no screening, for a given WTP for a QALY. All future costs and health outcomes were discounted at a rate of 3.5%.¹⁵ The analysis was performed using STATA/SE version 14.0.

Pashayan N., Morris S., Gilbert F.J, & Pharoah P.D. (2018). Cost-effectiveness and Benefit-to-Harm Ratio of Risk-Stratified Screening for Breast Cancer: A Life-Table Model. JAMA Oncology, 4(11), 1504-1510.

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Infusion Rate Accuracy Across Altitudes

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Continuous variables with highly skewed distributions were reported as median along with 25th–75th percentiles and range (i.e., lowest and highest values). We performed median (i.e., 50th percentiles) and interquartile (i.e., difference between 75th and 25th percentiles) regressions to examine whether median flow infusion rates and interquartile range differed depending on SIP device and altitude (300 versus 1700 m), respectively. A first-order interaction term was tested for statistical significance to examine whether the accuracy of the devices varied for the different altitudes.
Two-sided P-values less than 0.05 were considered statistically significant. All analyses were performed using Stata SE version 14.0 (Stata Corporation, College Station, TX, USA).

Blancher M., Repellin M., Maignan M., Clapé C., Perrin A., Labarère J., Debaty G, & Viglino D. (2019). Accuracy of low-weight versus standard syringe infusion pump devices depending on altitude. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 27, 65.

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Predictors of Preterm Premature Rupture of Membranes

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The data were coded and entered into EpiData Entry Client version 4.2 and exported to Stata/SE version 14.0 for data cleaning and analysis. Descriptive statistics are presented using mean, standard deviation (SD), and frequency tables. Variables at binary logistic regression with a p-value <0.20 were selected for the multivariable logistic regression model. To assess model fitness, The Hosmer-Lemeshow goodness of fit test was used. Finally, variables with a p-value <0.05 at 95% CI and corresponding AOR were considered statistically significant factors for PROM.

Telayneh A.T., Ketema D.B., Mengist B., Yismaw L., Bazezew Y., Birhanu M.Y, & Habtegiorgis S.D. (2023). Pre-labor rupture of membranes and associated factors among pregnant women admitted to the maternity ward, Northwest Ethiopia. PLOS Global Public Health, 3(3), e0001702.

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Modifiable Health Risks in HC Patients

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Using a cross-sectional analysis of the data, we first described the sociodemographic and health-related characteristics of HC patients. Second, we examined the percentages of various modifiable health risk factors and related counselling and treatment measures for the total sample. We also examined those distributions by race/ethnicity and gender to determine whether there were any significant differences across groups. We conducted design-based F tests to compare the likelihood of having various modifiable health risk factors and related counselling and treatment measures among groups. Third, multivariate logistic regressions were conducted to assess associations between patients’ race/ethnicity and gender and 10 outcome measures, controlling for other patients’ sociodemographic and health-related characteristics. The interactions between variables were checked before entering them into the model. No presence of a significant interaction was found indicating simple effects of independent variable are the same at all levels of the other factors. All independent variables were entered into the regressions simultaneously. Using Stata/SE version 14.0 (StataCorp LP, College Station, TX, USA), statistical analyses were performed while accounting for the complex sampling design of the survey. Two-tailed p-values less than or equal to 0.05 were considered statistically significant.

Liang H, & Beydoun M.A. (2018). Modifiable health risk factors, related counselling, and treatment among patients in health centres. Health & social care in the community, 27(3), 693-705.

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HPV Prevalence and Risk Factors Among MSM and HIV Individuals

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HPV prevalence and questionnaire data were entered and analysed using both Epi Info version 7 (USDHHS, Centers for Disease Control and Prevention) and STATA SE version 14.0 (Stata Corporation, College Station, Texas, USA). Descriptive statistics included frequency tables for categorical variables such as level of education, HR-HPV and multiple HPV and summary measures i.e. mean and standard deviation (SD) or median and inter-quartile range (IQR) for continuous variables such as age. Factors associated with HPV infection, comparing differences between MSM only and MSMW and differences between HIV-positive and HIV-negative participants were assessed with Pearson chi-square test and Fisher’s exact test for categorical variables and Student’s t-test for continuous variables. Factors with a p-value of ≤0.05 in the univariate logistic regression analyses were included in the multiple logistic regression model to determine possible associations with outcome variables. The HPV outcome variables included HPV prevalence rates in this selected study population for any HPV, HR-HPV and multiple HPV at the 3 anatomical sites and their 95 % confidence intervals (CI); crude odds ratios (COR) and adjusted odds ratios (AOR) and its 95 % CI were calculated.

Müller E.E., Rebe K., Chirwa T.F., Struthers H., McIntyre J, & Lewis D.A. (2016). The prevalence of human papillomavirus infections and associated risk factors in men-who-have-sex-with-men in Cape Town, South Africa. BMC Infectious Diseases, 16(1), 440.

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Risk Factors for Injurious Falls in Adults

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We first summarized the baseline characteristics of participants using means and standard deviations (SDs) or frequencies and percentages. We compared participants who did with those who did not experience any injurious fall using t-tests or Wilcoxon rank-sum tests for continuous variables and Chi-squared or Fisher’s exact tests for categorical variables.
Subsequently, we performed conditional logistic regression, using injurious fall as the dependent variable. We began with univariate regression and independent variables with p < 0.10 were included in the multivariate regression models. We performed two multivariate conditional logistic regressions, using the number of anti-hypertensive medication classes or any change in anti-hypertensive medication as the exposure variables respectively. Associations with p < 0.05 in the multivariate regressions were regarded as statistically significant. To examine whether the results were consistent in parsimonious models, we also performed sensitivity analyses by including variables with p < 0.10 into both forward and backward stepwise regressions. We specified p > 0.10 for removal from the model and p < 0.05 for addition to the model. We examined all final models for collinearity using variance inflation factor (VIF). All analyses were performed using STATA SE version 14.0 (StataCorp College Station, Texas).

Banu Z., Lim K.K., Kwan Y.H., Yap K.Z., Ang H.T., Tan C.S., Fong W., Thumboo J., Lee K.H., Ostbye T, & Low L.L. (2018). Anti-hypertensive medications and injurious falls in an older population of low socioeconomic status: a nested case-control study. BMC Geriatrics, 18, 195.

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Logistic Regression Analysis of Emergent Procedures

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Stata SE version 14.0 (College Station, TX) was used to analyze the odds of having an emergent procedure for each variable in a logistic regression analysis. The logistic regression was used to analyze the odds of undergoing an emergent procedure when holding age, gender, health insurance, race and language constant. To run a logistic regression model all variables must be nominal and all categorical variables must have a 0/1-coding scheme for each variable outcome (i.e. African American, Hispanic, Asian) where the most occurrences are considered the baseline (Males, primarily insured by Medicaid, Caucasians, and English speakers). For the logistic regression we compared all variable outcomes to the most common occurrence. This model assumes the log odds of age, gender, health insurance, race and language are linearly associated with emergent procedures. It also assumes that the model is neither over- nor under-fitted. To assure all assumptions were true, categorical variables were recoded as individual outcome variables with 1 if satisfied and 0 if not. A sixth variable, Department of Human Services (DHS) status, was recorded but only two patients were classified as being under DHS care. This variable was dropped and removed from the model. Observations with missing values were completely dropped from review (n =160).

Andrews A., Franklin L., Rush N., Witts R., Blanco D, & Pall H. (2017). Age, Gender, Health Insurance, and Race Associated with Increased Rate of Emergent Pediatric Gastrointestinal Procedures. Journal of pediatric gastroenterology and nutrition, 64(6), 907-910.

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Vape Shop Visitation and Substance Use

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Analyses were conducted using Stata/SE version 14.0, and data were weighted to offset non-response bias. First, prevalence estimates of vape shop visitation were estimated in the young adult sample. Crude logistic regression models were then used to examine associations of vape shop visitation with demographics, tobacco use, and substance use correlates. Next, all correlates that were associated with e-cigarette use in prior studies^{9 (link)–11 (link)} were entered into a multivariable logistic regression model, controlling for all other variables. Due to small sample sizes, we collapsed daily and non-daily use of tobacco, alcohol, marijuana, and other drugs into past 30-day use in these models.

Rose S.W., Cohn A.M., Pearson J.L., Johnson A.L., Rath J.M, & Villanti A.C. (2016). Visited a vape shop? Prevalence and correlates from a national sample of U.S. young adults. Tobacco prevention & cessation, 2(Suppl), 4.

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Mortality Outcomes in Indigenous Patients

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For median length of stay, we treated interhospital transfers as a single admission. We calculated all-cause death at 30 days and 90 days after record date. We assessed differences in 30-day deaths by Indigenous status by using age group–adjusted and sex-adjusted logistic regression. We performed all analyses by using Stata SE version 14.0 (StataCorp LLC, https://www.stata.com).

Wright C.M., Moorin R., Pearson G., Dyer J., Carapetis J, & Manning L. (2022). Invasive Infections Caused by Lancefield Groups C/G and A Streptococcus, Western Australia, Australia, 2000–2018. Emerging Infectious Diseases, 28(11), 2190-2197.

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