Stata 16
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»
Cardiac Damage and Survival Outcomes
Transmasculine Genital Reconstruction Outcomes
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 (
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.
Assessing Medicine Coverage and Mortality
Honey Bee Parasite Exposure Effects
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.
Mosquito Diversity Across Zhejiang Landscapes
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).
Top 5 protocols citing «stata 16»
Comparative Analysis of COVID-19 Waves at SBAH
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 ).
COVID-19 Impact on Mental Health
All analyses were undertaken in Stata 16. Further details about the statistical analysis are included in the Supporting Information.
Analyzing Household Factors and Health Outcomes
Post-COVID-19 Syndrome Epidemiology
Sex Differences in Weight Gain Attempts
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