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Stata 16

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Stata 16 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of tools and functionalities for researchers, academics, and professionals working with quantitative data. Stata 16 offers advanced statistical methods, data manipulation capabilities, and flexible programming features to support various analytical tasks.

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Stata 16 has been discontinued by StataCorp and is no longer available for purchase through official channels. StataCorp recommends upgrading to the current version, Stata 17, as the official replacement.

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6 510 protocols using «stata 16»

1

Cardiac Damage and Survival Outcomes

2025
Baseline characteristics are described using frequencies and percentages for categorical data, and mean ± standard deviation for continuous data. The patients were divided according to cardiac damage category, as defined previously. The participant characteristics of the cardiac damage groups were compared using analysis of variance with multiple-testing correction (Bonferroni) for continuous variables or the chi-square test for categorical variables. The Kaplan–Meier method was used to calculate the survival and event rates for the different categories; a comparison of cumulative event rates between these groups was performed using the log-rank test. To evaluate the risk of all-cause mortality and HF admissions, a multivariable Cox proportional hazards analysis was performed. The multivariable adjustment was developed including all the variables with clinical significance and those that had been associated with a higher risk of the combined end-point in the univariate analysis (p ≤ 0.10). All statistical analyses were performed using Stata 16.1 (StataCorp, College Station, TX, USA). p < 0.05 was accepted as statistically significant.
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2

Transmasculine Genital Reconstruction Outcomes

2025
A retrospective review was performed of patients who underwent transmasculine gender affirming surgery from 2021 to 2023 at a single institution, as part of an IRB-approved study (IRB 20-01505). Patients were included if their surgery was a metoidioplasty with scrotoplasty and insertion of testicular prostheses. Patients were excluded from analysis if their surgery was not their index surgery or if data of interest were omitted on record review, such as implant size.
Two senior surgeons performed all testicular prosthesis implantations. Implantation was standardized across all patients with one of two techniques, with Coloplast® Torosa silicone prostheses placed in pockets created by incisions at the top of the labia majora or blunt dissection of the labia majora medially during metoidioplasty. Incisions at the top of the labia majora, labeled a superolateral approach, create dartos pockets in the newly formed scrotum for the implants. The implants are placed superficial to the Martius fat pad and have not typically been anchored in place with a suture (Figure-1A). In the medial approach, the labia majora are dissected and joined in the midline to create the scrotum. Each side is then opened bluntly on the medial aspect to create pockets for the implants. These implants are also placed superficial to the labial fat pads. Medial insertion of implants avoids the need for additional incisions and minimizes scar (Figure-1B).
Demographic variables were collected for each patient, including age, body mass index (BMI), and current or former smoking status. The presence of any comorbid immunocompromising disease, including diabetes, HIV infection, or chronic steroid use, was also measured. The primary outcome of interest was a post-operative complication, such as infection, erosion of the prostheses, implant migration, or pain, that required implant removal. Implant removal was compared with patient factors, including age, body mass index (BMI), smoking status, implant size, and immunocompromised state. For the purposes of analyzing implant removal, each implant was considered an observation since not all patients who underwent removal had bilateral explantation.
Statistics were performed using Stata 16 (StataCorp, College Station, TX). Pearson's chi-squared test was performed to evaluate differences in rates of implant removal between implant technique. Logistic regression was performed to identify patient factors associated with complications requiring implant removal. Statistical test results were deemed significant for p-values less than 0.05. Institutional Review Board approval for this observational study was obtained through our institution's Program for the Protection of Human Subjects.
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3

Assessing Medicine Coverage and Mortality

2025
The analysis utilized Stata (16, StataCorp LLC, College Station, TX), with statistical significance set at a p-value ≤ 0.05. A linear regression model was fitted to assess the hypothesis regarding the relationship between the listing of medicines (measured as the medicine coverage score) and amenable mortality. In this analysis, the risk-standardized death rate from the HAQ dataset was the measure of amenable mortality, and the medicine coverage score served as the independent variable [11 (link)]. The regression results are presented for both unadjusted and adjusted models, with health expenditure and population size as pre-specified covariates that were included because they could be associated with both medicine coverage and amenable mortality.
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4

Honey Bee Parasite Exposure Effects

2025
All statistical analyses were performed using STATA16, whereas figures were created using NCSS20. The Shapiro–Wilk’s test and the Levene’s test were used to determine the distribution of the data and assess model residues and the homogeneity of variances. Accordingly, the appropriate statistical tests were chosen. To assess the relationship between the explanatory variable (i.e. parasite exposed) and the dependent variables (i.e. total living sperm and HPG-size) linear regression models were applied using the function regress, where individual bees were considered independent units and start mass was included as covariate. Additionally, to determine differences between treatments, generalized logistic or linear (regression) models (GLMs) and general linear models (GLM) with random intercepts were fit using the functions melogit, meglm and glm. Individual bees were considered independent units; parasite exposure, sex and the interaction term (i.e. parasite × sex) were included as the explanatory (fixed) terms; N. ceranae spore levels as well as start mass were included as covariates. For each model, a stepwise backward elimination approach was applied to determine the model of best fit. Best fit models were chosen by comparing every multi-level model with its single-level model counterpart with a likelihood ratio (LR) test and comparing different models with the Akaike information criterion (AIC) using the functions lrtest and estat ic, respectively. Whenever one of the fixed terms revealed significant, Bonferroni multiple-pairwise comparison tests (bmct) were performed using the function mcompare(bonferroni) [47 ]. If sex-specific differences were revealed, drones and workers were separated to facilitate analyses. Whenever appropriate, the means ± s.e. or medians ± 95% confidence intervals (CI) are given in the text of the result section.
To account for differences in start mass and to enable sex-specific comparisons, consumption and spore counts were analysed using the relative values. Based on the analysis of residuals, the variables start mass (g), consumption (g g−1 bee−1), spore counts (spore bee− 1), total living sperm (thousands) and HPG diameter (µm), were modelled using a GLMM with either a Gaussian, Gamma or Poisson distribution (electronic supplementary material, table S1). Counter transforming the outcome variables if non-parametrically distributed, we opted for using Gamma distribution that provided good fits (i.e. normality of the residuals) [45 (link)]. Survival times for individuals were fitted using the function mestreg for multilevel survival models with a Weibull distribution and displayed using Kaplan–Meier survival curves. Survival was calculated by using cumulative survival rates [%] 12 days post-experiment initiation. Data distributions, and the applied models including fixed and random effects used to test for treatment effects of each variable can be found in the electronic supplementary material, table S1.
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5

Mosquito Diversity Across Zhejiang Landscapes

2025
From April to November 2023, we collected data on the number of female mosquitoes of each species captured by mosquito trapping lights in each habitat across Zhejiang Province, China. Excel 2016 was used to summarize the data. Statistical analysis includes two sections. Initially, to investigate whether the mosquito population composition would differ across different landscapes, habitats, or months, the chi-square (χ2) test was employed to perform a single-factor statistical analysis, utilizing SPSS 15.0 software. A P-value of less than 0.05 was deemed to indicate a statistically significant difference. The Row × Column Split Method was employed to conduct pairwise comparisons between groups. P-values were adjusted using the Bonferroni correction, which divided the original alpha (α, e.g., 0.05) by the number of comparisons to ensure a more conservative threshold for statistical significance. The test-level corrected α’ values were determined as follows: 0.003 for landscapes, 0.005 for habitats, and 0.002 for months. In this analysis, a P value less than the corresponding α’ value was considered to indicate a statistically significant difference.
Subsequently, regression models were conducted to examine the relationship between multiple variables and the number of female mosquitoes of each species captured by mosquito trapping lights. All multivariable generalized linear models were run using Stata 16.0 software. Before establishing the regression model, we used the “sum” instruction to conduct the dispersion trend analysis of the count data for the number of distinct mosquito species caught by mosquito trapping lights in all habitat sites in Zhejiang between April and November 2023, the results were presented using descriptive statistical indicators, including mean ( ), standard deviation (s), minimum (Min.) and maximum (Max.). When data were over-dispersed, such as when the standard deviation was greater than the mean, this indicated that negative binomial regression models should be used for establishing multivariate regression of count data, rather than using Poisson’s regression models. Next, we performed models with the “nbreg” instruction, factors that were found to have a statistically significant effect in the single-factor statistical analysis were included in the model as independent variables, and the number of each female mosquito species captured by mosquito trapping lights in each habitat was used as dependent variable. Alpha tests were used to certify for overdispersion of the count data again, when the 95% confidence interval of the Alpha value did not cover 0, it was indicated that negative binomial regression models should be used rather than Poisson’s regression. Then, we evaluated the impact of each variable on the number of each female mosquito species captured by mosquito trapping lights in each site using the positive or negative signs of the regression coefficients (β) and the value of the incidence rate ratios (IRR).
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Top 5 protocols citing «stata 16»

1

Comparative Analysis of COVID-19 Waves at SBAH

The Steve Biko Academic Hospital (SBAH) is an 800 bed tertiary academic hospital to which is attached the 240-bed Tshwane District Hospital and the University of Pretoria's Health Sciences Faculty. Sections of the hospital, including ICU, high care and general wards were repurposed for the management of adult and paediatric COVID-19 patients. This included areas in the Emergency Units, labour wards and theatres. All clinical departments provided both staff and services to the COVID-19 areas as required.
At the beginning of the pandemic in March 2020, a national hospital admissions surveillance system (DATCOV) was established by the National Institute of Communicable Diseases (NICD). Hospital level data were extracted from the COVID-19 hospital surveillance system for patients admitted to the Steve Biko Academic Hospital (SBAH) from 4 May 2020 to 16 December 2021. These hospital records were reviewed for a comparison between patients admitted during the Omicron wave and previous waves. All patients were included in the sample.
466 records from DATCOV of patients admitted during the Omicron wave were compared to all 3962 records of patients admitted during three previous waves over a period of 18 months. In addition, a snapshot analysis of 98 records of patients occupying COVID-19 beds in the hospital at peak bed occupancy were reviewed for severity of illness, primary indication for admission, oxygen supplementation level, self-reported vaccination and prior COVID-19 infection status. These data were entered into the internal hospital information system. Oxygen supplementation levels for 588 patients admitted to the hospital during the first wave were reviewed (Boswell et al., 09 December 2021 ).
The record files of 21 deceased patients for the period 14 November through 16 December 2021 were requested from the hospital registries and reviewed for cause of death.
Hospital COVID-19 bed occupancy was obtained from daily statistics captured by the Nursing Services Manager responsible for bed management at the facility.
Data for the city and province-wide cases, deaths and hospital admissions were provided by the NDOH (National Department of Health 2021 ) and the NICD (National Institute for Communicable Diseases 2021 ). Data analysis was done using Excel and STATA 16. Data smoothing was performed using LOWESS in STATA 16 (Stata/IC 16 2020 ).
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2

COVID-19 Impact on Mental Health

Population prevalence rates with 95% confidence intervals (CIs) were estimated for mental health assessment parameters and experiences of COVID‐19 and restrictions, adjusted for differences in selected socio‐demographic characteristics (state, socio‐economic position decile, sex, age) between the respondents and the Australian population at September 2019.4The characteristics of respondents with experiences of COVID‐19 and related restrictions, and associations between mental health assessment parameters and experiences of COVID‐19 and restrictions were assessed by multiple logistic regression, adjusted for selected socio‐demographic characteristics.
All analyses were undertaken in Stata 16. Further details about the statistical analysis are included in the Supporting Information.
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3

Analyzing Household Factors and Health Outcomes

For each survey, we modelled the association between house types and the odds of each health outcome, adjusting for prespecified variables (S3 Text). We used exact conditional logistic regression to enable associations to be estimated within geographical clusters, minimising confounding due to intercluster variation in disease risk and other factors. Because all children within geographical clusters are surveyed at a similar time point, conducting our analysis within clusters enabled comparisons to be made between children exposed to the same disease transmission seasons and prevention programmes. This approach was consistent with the underlying survey design. The mean cluster size was 25 children (range: 1–238 children). The analysis was restricted to children aged 0–5 years because of the availability of biomarker data for this age group only. All variables were included as categorical variables (including integer age), allowing nonlinearities to be modelled, except wealth score, which was included as a continuous variable. We explored nonlinear parameterisations of wealth using restricted cubic and B-splines, but these had no benefit over the linear model in terms of estimate concordance. We also explored sparsity promoting l1 penalised versions, but these had no benefit over unpenalised models. We preprocessed surveys to ensure that (1) missingness was not >10% for any variable, (2) data with no outcome variation in a given strata were removed, (3) constant covariates were removed, and (4) there were at least 100 observations after processing. Individual survey odds ratios (ORs) were combined to produce a summary OR using random-effects meta-analysis [25 (link)]. We opted for a two-stage approach to the analysis (whereby each survey was individually analysed and then individual survey ORs were pooled) over a one-stage approach [26 (link)] to maximise data use and ensure a consistent analysis because not all outcomes and covariables were collected by all surveys. Analyses were conducted in Stata16 (StataCorp, Texas) and R version 3.6.0 (R Core Team, Vienna).
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4

Post-COVID-19 Syndrome Epidemiology

Patients were divided into two groups (with or without post-COVID-19 syndrome) at the time of interview. Absolute values, percentages, mean and median (standard deviation (SD) or interquartile range (IQR)) were calculated. Categorical variables were compared using the chi-squared test or Fisher's exact test, while continuous variables were compared using a Student t-test or Mann–Whitney U test for two groups, one-way ANOVA or the Kruskal–Wallis test for more than two groups, according to the Shapiro–Wilk test establishing whether data were normally or non-normally distributed. A multivariable logistic regression was performed to explore variables associated with post-COVID-19 syndrome, estimating the odds ratios (OR; 95% CI). All clinically or microbiologically relevant variables or those which were significant at p ≤ 0.10 in univariable analysis were included, taking into account potential collinearities (e.g. the severity of acute COVID-19 and the ICU admission). Given that only 231 patients performed the serological follow-up at the time of interview, univariable and multivariable logistic regression was performed on this sub-group; moreover, this sub-group was compared with the remaining population (Tables S3–S5). Seronegative patients and those with seroreversion of IgM and IgG were excluded from the serological follow-up at the time of interview, but were included in the overall count only to evaluate the presence of IgG antibodies against SARS-CoV-2 6 months after the onset. Analyses were performed by STATA 16.
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5

Sex Differences in Weight Gain Attempts

Descriptive comparisons between males and females were calculated using Pearson’s chi-square tests for categorical variables and independent samples t-tests for continuous variables. Generalized estimating equations (GEE) (24 ) logistic models were used in combination with regression standardization (25 (link)) to estimate adjusted prevalence estimates by sex and age group. GEE logistic models were also used to assess associations with weight gain attempts and muscle-enhancing behaviors. All models used Add Health’s pre-constructed sample weights to provide nationally representative estimates (16 ,26 ); incorporated robust standard errors with clustering by school, which also captures clustering within persons across waves (27 ); and adjusted for age, race/ethnicity, BMI, percentage of the federal poverty line at baseline, highest parental education at baseline, participation in team sports at baseline (28 (link)–30 (link)) and wave. Age effects were nonlinear, as shown by the addition of a quadratic term (p<0.001 in males); to address this, age was categorized (11–13, 14–16, 17–19, 20–22, and >23 years). Analyses were stratified by sex given the different rates of weight gain attempts (13 (link)) and muscularity-oriented disordered eating behaviors (6 (link)) in males and females. Data analysis was performed using STATA 16.0.
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