Stata se version 12

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Stata/SE version 12.0 is a statistical software package designed for data analysis, management, and visualization. It provides a comprehensive set of tools for researchers, analysts, and professionals working with a wide range of data types. Stata/SE is intended to facilitate efficient data manipulation, advanced statistical modeling, and the generation of high-quality graphics.

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Microsoft excel 2007, by StataCorp (2 mentions) Spss version 20, by IBM (2 mentions) Proc glm, by SAS Institute (1 mentions) Spss version 19, by IBM (1 mentions) Stata v 16, by StataCorp (1 mentions) Excel 2010, by StataCorp (1 mentions) Sas version 9, by SAS Institute (1 mentions) Spss version 21, by IBM (1 mentions) Spss version 22, by IBM (1 mentions) Epi info, by StataCorp (1 mentions) Spss version 26, by IBM (1 mentions) Spss version 17, by IBM (1 mentions)

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172 protocols using stata se version 12

Evaluating Flood Impact on ER-GI Visits

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We applied a fixed-effect conditional logistic regression model, standard for case-crossover studies [19] (link), [20] (link), [23] (link), and shown to be unbiased when the time-stratified bi-directional case-crossover design is used for control selection [21] (link), [22] (link). To evaluate whether there were differences in patterns across the state, we conducted separate analyses by county. We also conducted stratified analyses by the following age groups: 0–5; 6–18; 19–64; and over 64 years of age. Results are reported as odds ratios (OR) and associated 95% confidence intervals (CI) and are interpreted as the relative increase in odds of ER-GI visits following a flood. Data management and statistical analyses were conducted using Stata SE Version 12 [24] and conditional logistic regression models were fit using the xtlogit command. Attributable fractions and population attributable factions were calculated as described by Hanley [25] (link). Approximate 95% CIs for population attributable fraction estimates were determined as described by Natarajan et. al.[26] (link) and 95% CIs for attributable fractions were estimated by the delta method using the nlcom command in Stata SE Version 12. Graphics were produced in R version 11 [27] using the ggplot2 package [28] .

Wade T.J., Lin C.J., Jagai J.S, & Hilborn E.D. (2014). Flooding and Emergency Room Visits for Gastrointestinal Illness in Massachusetts: A Case-Crossover Study. PLoS ONE, 9(10), e110474.

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Statistical Analysis Techniques for Research

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The results are expressed as frequencies and percentages for categorical variables, mean ± standard deviation for continuous normally distributed variables, and median (interquartile range) for continuous variables that were not normally distributed. Continuous normally distributed data were compared using two-tailed unpaired t-tests. Continuous non-normally distributed data were compared using the Mann–Whitney tests. Dichotomous and categorical data were compared using the chi-square tests. PS matching was performed using Stata/SE version 12.0 (StataCorp, College Station, TX). Time-to-event analysis was evaluated using the Kaplan–Meier and Cox proportional hazards survival analyses. Statistical analysis was performed using IBM SPSS Statistics software (version 22.0, Armonk, NY) and Stata/SE version 12.0 (StataCorp, College Station, TX). p Values less than .05 were considered statistically significant.

Zhou S.J., Cong Y.K., Han Q.F., Tang W, & Wang T. (2020). Peritoneal dialysis outcomes in patients with nephrotic syndrome: a propensity score-matched cohort study. Renal Failure, 42(1), 684-692.

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Survival Analysis of Burkitt Lymphoma

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Baseline characteristics are summarized using numbers with percentages or medians with interquartile ranges (IQRs). OS was estimated using Kaplan-Meier methods, and survival differences between groups were compared using the non-parametric log-rank test. We focused our analyses on OS as the most reliable clinical endpoint in our setting, particularly given that BL is a highly curable cancer for which relapse is uncommon after 12 months. We additionally chose OS as a more valid outcome given non-standardized criteria for response assessment throughout SSA, and a patient population with frequent abdominal disease that was evaluated serially using relatively crude methods incorporating physical examination and ultrasound. However, to provide more detailed data regarding cause of death, all deaths in our study were separately adjudicated through consensus-centralized review involving multiple study investigators. Risk factors for mortality were assessed using unadjusted Cox proportional hazards models. Follow-up time was calculated from enrolment until death, loss to follow-up or censoring, which occurred on31 August 2015. All analyses were performed using STATA SE version 12.1 (Stata Corp., College Station, TX). Statistical significance was considered at a two-sided α-level of 0.05.

Stanley C.C., Westmoreland K.D., Heimlich B.J., El-Mallawany N.K., Wasswa P., Mtete I., Butia M., Itimu S., Chasela M., Mtunda M., Chikasema M., Makwakwa V., Kaimila B., Kasonkanji E., Chimzimu F., Kampani C., Dhungel B.M., Krysiak R., Montgomery N.D., Fedoriw Y., Rosenberg N.E., Liomba G.N, & Gopal S. (2016). Outcomes for paediatric Burkitt lymphoma treated with anthracycline-based therapy in Malawi. British journal of haematology, 173(5), 705-712.

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Food Choice: Healthier vs. Less Healthy

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Hypothesis 1 (Increasing the number of less healthy food items has a larger effect than increasing the number of healthier food items):
This was analysed via logistic regression (using Stata SE version 12.1) predicting choice of a healthier food option, with dummy variables indicating the availability and cognitive load conditions as the key predictors. For availability, the two healthier & two less healthy choices condition was the reference group, with two dummy variables for the other availability conditions indicating (1) increase in healthier options and (2) increase in less healthy options. For cognitive load, a dummy variable indicating high load was used. Control variables included socioeconomic status, gender, age and hunger.

Pechey R, & Marteau T.M. (2018). Availability of healthier vs. less healthy food and food choice: an online experiment. BMC Public Health, 18, 1296.

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Epidemiology of Cancers in HIV-Positive Patients

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Data were entered into the KCH Malawi cancer registry database, as well as the AIDS Malignancy Clinical Trials Consortium surveys for AIDS-related malignancies (lymphoma, cervical cancer, and Kaposi's sarcoma). The data were extracted from the database into Microsoft Excel. Baseline characteristics were summarised using frequencies with percentages or means with standard deviations (SDs). We compared differences in distribution between each risk factor variable, according to cancer site and stage, using Fisher's exact test. We estimated the odds of being HIV-positive with a particular cancer diagnosis using a logistic regression model to assess risk factors for common types of cancer. We considered a P-value of 0.05 or less to be significant. All analyses were performed using Stata SE version 12.1 (StataCorp, College Station, TX, USA)

Moses A., Mwafongo A., Chikasema M., Kafantenganji L., Stanely C., Chimzukira E., Kampani C., Krysiak R., Gopal S., Rosenberg N.E., Shores C.G, & Hosseinipour M.C. (2017). Risk factors for common cancers among patients at Kamuzu Central Hospital in Lilongwe, Malawi: A retrospective cohort study. Malawi Medical Journal, 29(2), 136-141.

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Biomarker Prediction of NAFLD

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We tested normality using the Shapiro-Wilk test and Q-Q plots. We used Wilcoxon rank sum test for between group comparisons. Chi-squared test or Fisher's exact test was used for inferences on proportions.
Given the dichotomous outcome of L/S ratio we tested if biomarkers predicted NAFLD using logistic regression. Since variable YKL-40 was severely skewed, we log-transformed it before including it as continuous covariates in the model. Variables identified as potential confounders and those with a P value less than 0.2 in the univariable analysis were included in the multivariable logistic regression model. A backward stepwise approach was used to determine the most parsimonious model for the serum biomarkers, with a pre-specified P value less than 0.10. The Hosmer-Lemeshow goodness-of-fit test was used to assess calibration of the final model. The model discrimination was evaluated using the receiver operating characteristic Area under the Curve (AUC).
Data are expressed as median (interquartile range, IQR) or odds ratio (95% confidence interval), unless otherwise stated. A two-sided P-value less than 0.05 was considered significant. All analyses were performed with STATA/SE version 12.1 (StataCorp LP, College Station, TX).

Cho S.J., Echevarria G.C., Lee Y.I., Kwon S., Park K.Y., Tsukiji J., Rom W.N., Prezant D.J., Nolan A, & Weiden M.D. (2014). YKL-40 is a Protective Biomarker for Fatty Liver in World Trade Center Particulate Matter-Exposed Firefighters. Journal of molecular biomarkers & diagnosis, 5, 1000174.

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Evaluating Shared Decision-Making Intervention

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We used descriptive statistics to summarize demographic characteristics of survey
respondents. We used the paired t test to compare pre- and
postintervention scores for the PPOS and the Shared Decision Making Belief
Scale, after confirming that assumptions of normality were not violated. The
Wilcoxon signed-rank test was used to compare pre- and postintervention
responses for other Likert-type scale items. Pre- and postintervention rates of
decision aid usage were compared using the chi-squared test. All analyses were
performed using the statistical software Stata SE, version 12.1 (Stata Corp.,
College Station, Texas).

Ye S., Leppin A.L., Chan A.Y., Chang N., Moise N., Poghosyan L., Montori V.M, & Kronish I. (2018). An Informatics Approach to Implement Support for Shared Decision Making for Primary Prevention Statin Therapy. MDM Policy & Practice, 3(1), 2381468318777752.

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Comparing Treatment Effects on Neurological Outcomes

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Descriptive statistics of quantitative variables are expressed as median, 25th and 75th percentiles.
Due to low sample size in each group and to the not normally distributed variables under examination, the two groups were compared using Mann–Whitney statistic test for quantitative variables and Fisher Exact test for qualitative variables.
To assess the association between the difference in the number of pathological tests and the type of treatment a multiple linear regression model was fitted using the following covariates: EDSS change, mFIS, MSQoL, MADRS, number of relapse in the previous year, steroid consumption, sex, and age at T12.
All statistical analyses were performed using STATA/SE version 12.1 software (STATA/SE, 2011).

Mattioli F., Stampatori C., Bellomi F., Danni M., Compagnucci L., Uccelli A., Pardini M., Santuccio G., Fregonese G., Pattini M., Allegri B., Clerici R., Lattuada A., Montomoli C., Corso B, & Capra R. (2015). A RCT Comparing Specific Intensive Cognitive Training to Aspecific Psychological Intervention in RRMS: The SMICT Study. Frontiers in Neurology, 5, 278.

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Factors Influencing Brand vs. Generic PPI Prescribing

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Analyses were performed using Stata SE version 12.1 (College Station, TX) survey commands that account for the complex survey design and sample weights. We present both unweighted and nationally weighted estimates of numbers of visits that included either a brand or generic PPI prescription. Chi-square tests were used to compare the proportion of visits with brand name PPI prescriptions versus generic PPI prescriptions for each patient or physician measure. Physician and patient characteristics associated with generic versus brand PPI prescriptions were retained for the multivariable model of PPI prescribing if univariate significance was P < 0.1.
To better approximate relative risk [10 (link)], Poisson regression was used to determine the incidence rate ratio (IRR) for generic versus brand PPI prescribing [11 (link)]. Estimates of the effects of physician practice characteristics are modeled while simultaneously controlling for year of visit and physician and patient characteristics potentially associated with brand versus generic PPI prescriptions.

Gawron A.J., Feinglass J., Pandolfino J.E., Tan B.K., Bove M.J, & Shintani-Smith S. (2015). Brand Name and Generic Proton Pump Inhibitor Prescriptions in the United States: Insights from the National Ambulatory Medical Care Survey (2006–2010). Gastroenterology Research and Practice, 2015, 689531.

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Statistical Analysis of Research Data

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The data were expressed as mean ± standard deviation. The one-way ANOVA and Scheffe’s multiple range test (p < 0.05) were used to define the significant difference between groups. The correlation between variables was determined by Pearson’s product-moment correlation coefficient (r-value). Stata/SE (version 12.1) has been used for the statistical analysis and the generation of resulting figures.

Nguyen M.T., Kim J., Lee H., Won S., Kim Y., Jung J.A., Li D., To X.H., Huynh K.T., Le T.V., Israr B., An H.J, & Kim J. (2020). A Comparison of Vitamin and Lutein Concentrations in Breast Milk from Four Asian Countries. Nutrients, 12(6), 1794.

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