Se 14

Manufactured by StataCorp

The SE 14 is a lab equipment product offered by StataCorp. It serves as a core function for scientific research and analysis, though its specific intended use is not provided in this factual description.

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Stata/SE 14.2 for Mac Stata/SE V.14 Stata SE version 14.2 for Windows Stata/SE 14.1 for Mac Stata 14.0 SE Stata 14.2/SE for Linux Stata SE ver. 14.0 STATA/SE V.14.0 for Windows STATA/SE version 14.1 statistical software STATA Statistics SE 14

20 protocols using se 14

Predicting Disease Flare with Calprotectin

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Calprotectin levels (in ng/mL) were compared between the groups using Mann-Whitney U tests, and correlations with inflammatory parameters were explored cross-sectionally using Spearman tests. Univariable and multivariable binary logistic regression was performed with disease flare within 12 months as the outcome. Calprotectin levels were log2-transformed and included as a continuous covariate. Area under the receiver operating characteristic curves (AUCs), test characteristics, and predictive values of calprotectin levels for flare were calculated, and the optimum cutoff was determined using the Liu index [14 (link)].
Additionally, logistic regression models were used to evaluate whether the addition of calprotectin to a clinical predictor model for flare significantly improved prediction of flare within 12 months. Clinical predictors of flare with a p value of ≤ 0.1 in univariable models were considered for the multivariable models to discover clinical predictors common to both cohorts, as well as specific to each cohort (as a sensitivity analysis, see Additional file 1: Table S1A-B). Areas under the ROC curve were calculated from the predicted values of the model with and without calprotectin and compared using a chi-square-based test for equality [15 (link)]. All analyses were conducted separately for each cohort, using Stata SE 14.1.

de Moel E.C., Rech J., Mahler M., Roth J., Vogl T., Schouffoer A., Goekoop R.J., Huizinga T.W., Allaart C.F., Toes R.E., Schett G, & van der Woude D. (2019). Circulating calprotectin (S100A8/A9) is higher in rheumatoid arthritis patients that relapse within 12 months of tapering anti-rheumatic drugs. Arthritis Research & Therapy, 21, 268.

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Addiction Treatment Access and Recidivism

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Descriptive statistics were calculated for all study variables. We used random effects logistic regression, accounting for within-county effects, to assess the association of residing in a county with two or more outpatient addiction treatment programs that accept Medicaid to the odds of having a repeat addiction-related emergency service episode. Our model assumed the following form:

P (Y_{i j} = 1 | x_{i j}, U_{0 j}) = \frac{\exp [γ_{00} + γ_{01} x_{1 i j} + U_{0 j}]}{1 + \exp [γ_{00} + γ_{01} x_{1 i j} + U_{0 j}]}

Patient, treatment, and county control variables were included in the regression model to account for individual- and county-level differences. Results are reported as odds ratios. All analyses were conducted using Stata SE 14.1.

Andrews C., Westlake M, & Wooten N. (2018). Availability of Outpatient Addiction Treatment and use of Emergency Department Services Among Medicaid Enrollees.. Psychiatric services (Washington, D.C.), 69(6), 729-732.

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Incidence Rates of Type 1 Diabetes

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Annual crude and specific, by sex and group of age, incidence rates were calculated by dividing the UD cases by the number of residents in Apulia for the period 2009–2013. In order to assess the effects of age, gender, and calendar year, a Poisson regression model was performed by using STATA SE 14.1, considering p values of <0.05 as significant.
Moreover, the completeness of each source (sensitivity) was estimated by dividing the number of T1DM cases observed in each source by the total number of patients in the UD.

Fortunato F., Cappelli M.G., Vece M.M., Caputi G., Delvecchio M., Prato R., Martinelli D, & Workgroup A.C. (2016). Incidence of Type 1 Diabetes among Children and Adolescents in Italy between 2009 and 2013: The Role of a Regional Childhood Diabetes Registry. Journal of Diabetes Research, 2016, 7239692.

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Evaluating Hypertension Intervention Outcomes

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Data were entered with Epidata 3.1. Normality test was performed on continuous data. Those that followed the normal distribution was reported as mean±SD and others were described by median and interquartile range. Categorical data were reported in frequency and proportion. Two sample t-test and paired samples t-test were performed to compare MA score. Chi-square test was used to determine whether characteristics differed significantly between the control group and the intervention group. Furthermore, multilevel modeling with an interaction term was applied to account for clustering and to assess the treatment effects. A time dummy was created to indicate the time trend in treatment and control group, and an interaction term between the intervention and time dummy was generated to represent the net effect of intervention impact on patient outcomes. The analysis was adjusted for gender, age, education, ethnicity, marital status, antihypertensive medication history, and other chronic medication-taking history. Intention-to-treat analysis was also used to examine the intervention effect. P<0.05 was considered statistically significant. Statistical analysis was performed in Stata SE 14.1.

Shen Y., Wang T., Gao M., Zhu X., Zhang X., He C., Li Y, & Sun X. (2019). Effectiveness of low-cost reminder package combined with case-based health education to improve hypertensive patients' medication adherence: a clustered randomized controlled trial. Patient preference and adherence, 13, 1083-1092.

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Glycosylation Patterns in Rheumatoid Arthritis

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In the cross‐sectional cohort, the percentage of V‐domain glycosylation between RA patients and FDRs was compared by Mann‐Whitney U test. In the longitudinal cohort, levels of V‐domain glycosylation over time were compared between FDRs who developed RA and those who did not, using linear mixed models with a random intercept and slope. A multivariable Cox proportional hazards regression analysis was performed, with RA diagnosis as outcome and V‐domain glycosylation level as predictor. We used V‐domain glycosylation at the first moment of sampling, dichotomized as above or below the group median to draw Kaplan‐Meier survival curves and estimate risk of developing RA. Receiver operating characteristic curve (ROC) regression was used to calculate diagnostic properties. All analyses in the longitudinal cohort included adjustment for age and sex and were conducted using Stata SE 14.1. All reported percentage values refer to IgG ACPA V‐domain glycosylation levels and not to relative changes. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated.

Hafkenscheid L., de Moel E., Smolik I., Tanner S., Meng X., Jansen B.C., Bondt A., Wuhrer M., Huizinga T.W., Toes R.E., El‐Gabalawy H, & Scherer H.U. (2019). N‐Linked Glycans in the Variable Domain of IgG Anti–Citrullinated Protein Antibodies Predict the Development of Rheumatoid Arthritis. Arthritis & Rheumatology (Hoboken, N.j.), 71(10), 1626-1633.

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Characterizing Patient Profiles and Re-Surgery Rates

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Descriptive analysis was employed to characterize patient profiles at their first inpatient care. Continuous variables were presented as mean, standard deviation, median, inter-quartile range whereas categorical variables were displayed in frequencies and percentages. A re-surgery function was developed to illustrate the probability of re-surgery with the time interval between the first and second surgery. Patients without re-surgery were censored at the end of 2017. Kaplan-Meier method was used to establish the re-surgery function. Based on the function, cumulative re-surgery rates and their 95% confidence intervals were estimated at different time points, specifically, 3 months, 6 months, 1 year, and 2 years. The calculation of the time interval to re-surgery is shown in the following formula:

Time inteval = admission date of 2 nd visit - discharge date of 1 st visit

Non-parametric statistical tests were used for the costs, days (Mann-Whitney Test for two groups, and Kruskal-Wallis Test for three or more groups) and re-surgery rate (Log-rank Test and Wilcoxon Test) comparison, α = 0.05 was used as the significant level for all comparisons.
All analyses were conducted using Stata SE 14.

Yang D., Zhang Y., Ma X., Huo L., Li L, & Gao Y. (2020). Resources utilisation and economic burden of percutaneous vertebroplasty or percutaneous kyphoplasty for treatment of osteoporotic vertebral compression fractures in China: a retrospective claim database study. BMC Musculoskeletal Disorders, 21, 255.

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Evaluating Mental Health Treatment Impact

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Descriptive statistics were used to analyse the data using Stata SE 14. Socio-demographic characteristics were described as proportions and means as appropriate, for the full sample and compared between: (a) those who had planned discharge v. those who were dropouts, and (b) those who completed follow-up assessment v. those who were lost to follow-up, using χ² test or t test, as appropriate. Paired t tests were used to analyse the preliminary impact of the treatment by comparing average GHQ-12 and WHODAS 2.0 scores pre- and post-treatment. The association between change in GHQ-12 and WHODAS 2.0 scores (reduced score v. increased/no change in score) and the socio-demographics and process indicators was calculated as odds ratios (OR) using univariate logistic regression. All variables associated with a change in these scores at p < 0.1 on univariate logistic regression were included in a multivariate model and variables were then excluded one by one until all remaining variables were independently associated with the outcome at p < 0.05.

Garg A., Agrawal R., Velleman R., Rane A., Costa S., Gupta D., Dsouza E., Jambhale A., Sabnis A., Fernandes G., Bhatia U, & Nadkarni A. (2022). Integrating assisted tele-psychiatry into primary healthcare in Goa, India: a feasibility study. Global Mental Health, 9, 26-36.

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Epidemiological Analysis of Dengue Infections

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A descriptive analysis of the sample characteristics was performed using the chi-square test and Fisher’s exact test to compare categorical variables, and the Mann–Whitney test for continuous variables.
The dengue incidence per 1000 person-years was calculated as: 1000 × infections/(sum of the follow-up at-risk period for each individual/365.2).
A Cox proportional hazards model was used for the estimation of hazards related to a recent infection by DENV during the follow-up period. In the Cox proportional hazards model, failures corresponded to all individuals who were diagnosed with a recent infection during the follow-up period between 2014 and 2016, with 2014 (follow-up 3) as time zero. Censored data included all individuals who were not diagnosed with any type of infection during follow-up. The outcome variable was follow-up time (2014–2016) for censored data, and for failures, it was the elapsed time from inclusion in the study in 2014 to the first infection.
The analysis was controlled for confounders variables included: age and location (Fig. 3).Figure 3

Directed acyclic graph (DAG).

Besides, standard errors were adjusted by 75 clusters of the sampling cohort, in which each cluster was formed by houses located 50 m around the house of a dengue case reported in 2011. The STATA SE 14 software was used for the statistical analysis.

Amaya-Larios I.Y., Martínez-Vega R.A., Diaz-Quijano F.A., Sarti E., Puentes-Rosas E., Chihu L, & Ramos-Castañeda J. (2020). Risk of dengue virus infection according to serostatus in individuals from dengue endemic areas of Mexico. Scientific Reports, 10, 19017.

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Evaluating ENCM Intervention Effects

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The data were analyzed using Stata SE 14 [23 ]. Descriptive statistics were used to summarize the characteristics of study groups and describe the measurement results. A mixed model analysis was used to estimate the differences between an ENCM intervention group and a control group related to changes in the identified IFSMT proximal and distal outcome variables. Over the four waves of observation time, there were 12% total missing values on outcome variables. To properly handle missing data and to make valid statistical inference with minimal bias, we employed multiple imputation (mi) methods [30 ]. All statistical analyses were conducted based on these multiply imputed data with the STATA multiple imputations (mi) estimate modules. To correctly specify our panel data, we used a STATA random-effects GLS regression model procedure for estimating the time effects on outcome variables controlling for the background characteristics measured at baseline.

Zabler B., Tsai P.Y., Fendrich M., Cho Y., Taani M.H, & Schiffman R. (2018). Effect of a nurse case management intervention for hypertension self-management in low-income African Americans. Contemporary clinical trials, 71, 199-204.

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Calf Mortality Risk Factors Analysis

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Survival of calves at the time period was calculated using a followup life table/survivorship curve (Stata SE 14) . The proportion of calves dying up to 1 year was compared for calves of different breeds, sex, varying birth weight, year of birth, age of calf, and calves born at different seasons and locations. Additionally, the effect of age of the dam, categorized by parity classes, on the mortality rate of calves was determined using Wilcoxon (Gehan) Kaplan-Meier curve, wherein a log-rank statistical model was used for comparison of proportions for single factor and Cox proportional hazards model (Cox, 1972) for multiple factors or variables. In all the analyses for both growth and mortality, the significance level was set at 0.05.
The purpose of the model was to evaluate simultaneously the effect of several factors on survival. In other words, it should allow us to examine how specified factors influence the rate of a particular event happening, e.g., death, at a particular point in time. This rate was commonly referred to as the hazard ratio. Predictor variables (or factors) are usually termed covariates in the survival analysis.
In summary, HR = 1: No effect HR < 1: Reduction in the hazard HR > 1: Increase in Hazard.

Gessesse T., Dagnew Y., Abegaz S., & Tesfa A. (2021). Growth Performance and Survival Rate of Fogera and Their Crossbred Calves at Government Ranches in Ethiopia. Frontiers in Animal Science, 2.

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