Stata se 14.1 for mac

Manufactured by StataCorp

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Stata/SE 14.1 for Mac is a statistical software package developed by StataCorp. It is designed to provide users with powerful data management, analysis, and visualization capabilities on the macOS platform. The software supports a wide range of statistical techniques, including regression analysis, time series analysis, and multivariate analysis. Stata/SE 14.1 for Mac is suitable for researchers, analysts, and professionals working in various fields who require advanced statistical tools for their work.

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4 protocols using stata se 14.1 for mac

Analyzing Exercise Facilities and Cardiometabolic Outcomes

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The analysis was undertaken in three steps. First, key demographic and clinical characteristics of the population were described. Second, Poisson regression models with robust standard errors clustered at the census tract level were applied to estimate the prevalence ratio (PR) for the association between the exposure (exercise facilities) and each outcome (type 2 diabetes, obesity). We initially created an unadjusted model (Model 0), then adjusted the model by age and sex (Model 1), together with population density (Model 2), and finally adjusted by area-level SES (Model 3). Third, to identify any potential effect modification from area-level SES with the availability of exercise facilities, we introduced an interaction term between area-level SES and availability of exercise facilities, using a Wald test to get a p value for each interaction. Results were presented for the whole sample and stratified by sex, using the highest tertile of exercise facilities available as the reference. All analyses were conducted using Stata/SE 14.1 for Mac (StataCorp, College Station, TX, USA).

Cereijo L., Gullón P., Del Cura I., Valadés D., Bilal U., Badland H, & Franco M. (2021). Exercise facilities and the prevalence of obesity and type 2 diabetes in the city of Madrid. Diabetologia, 65(1), 150-158.

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Hemodynamic Optimization Post Cardiac Arrest

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As part of our research protocol we measured MAP immediately after ROSC and hourly thereafter using a noninvasive blood pressure cuff for the first six hours after ROSC. We calculated the mean MAP over the initial six hours after ROSC. We chose the initial six hours as our time frame as it has previously been demonstrated to be the most critical time period for hemodynamic optimization after cardiac arrest.(15 (link)) We prospectively captured all the components of the Sequential Organ Failure Assessment (SOFA) score (i.e. respiratory, coagulation, hepatic, renal, cardiovascular, and neurological) during the first six hours after ROSC to assess severity of illness.(16 (link)) For calculation of the total SOFA score, we excluded the neurological component as described previously,(16 (link)–18 (link)) as well as the cardiovascular component given collinearity with MAP. We prospectively captured vasopressor administration during the first six hours after ROSC and used the SOFA cardiovascular score to quantify vasopressor dosing. We abstracted clinical data from the medical record into Research Electronic Data Capture (REDCap, Vanderbilt University, TN), a secure, internet-based application designed to support data capture for research studies(19 (link)) and exported into Stata/SE 14.1 for Mac, StataCorp LP (College Station, TX, USA).

Roberts B.W., Kilgannon J.H., Hunter B.R., Puskarich M.A., Shea L., Donnino M., Jones C., Fuller B.M., Kline J.A., Jones A.E., Shapiro N.I., Abella B.S, & Trzeciak S. (2019). Association between elevated mean arterial blood pressure and neurological outcome after resuscitation from cardiac arrest: results from a multicenter prospective cohort study. Critical care medicine, 47(1), 93-100.

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Neighborhood Accessibility and SES Effects

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To study the association between accessibility to the nearest exercise facility and area-level SES we used linear mixed models with log transformed distance as the dependent variable and the SES index as the independent variable. This was a three-level model with a random intercept for neighbourhood and for census section. We included the independent variable (SES Index) operationalized as deciles, with the first decile (lowest SES) as the reference, group. To study the relationship between availability of exercise facilities and area-level SES, we used a Zero Inflated Poisson (ZIP) model. We chose a ZIP model instead of a mixed effects Poisson due to the high number of 0’s in the distribution of the dependent variable. We estimated robust standard errors clustered by census section to take into account the intra-census section correlation. We ran all models for all facilities and stratified by type of facility. All analyses were conducted using Stata/SE 14.1 for Mac (StataCorp., College Station, TX, USA).

Cereijo L., Gullón P., Cebrecos A., Bilal U., Santacruz J.A., Badland H, & Franco M. (2019). Access to and availability of exercise facilities in Madrid: an equity perspective. International Journal of Health Geographics, 18, 15.

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Monthly Health Problems and Care Use

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Stata/SE 14.1 for Mac was used for data analysis in the study. The estimated numbers of those who experienced any health problem and/or used each kind of medical care per 1,000 people in an average month was provided with their 95% confidence interval.

Kim Y.S, & Choi Y.J. (2016). The Ecology of Medical Care in Korea. Journal of Korean Medical Science, 31(11), 1684-1688.

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