Stata ic 16

Manufactured by StataCorp

Sourced in United States

Stata/IC 16.1 is a comprehensive data analysis and statistical software package developed by StataCorp. It provides a wide range of tools for data management, statistical analysis, and graphical presentation. Stata/IC 16.1 is a professional-grade solution suitable for a variety of research and analytical tasks.

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Lab products found in correlation

Sas version 9, by SAS Institute (2 mentions) R version 3, by R Project for Statistical Computing (1 mentions) Prism 6 for windows, by GraphPad (1 mentions) Prism 7, by GraphPad (1 mentions) Illustrator cs6, by Adobe (1 mentions) Jmp 12, by SAS Institute (1 mentions) Jmp pro 15, by SAS Institute (1 mentions) R software 4, by R Project for Statistical Computing (1 mentions) Prism 4, by GraphPad (1 mentions) Prism 8, by GraphPad (1 mentions) Prism 9, by GraphPad (1 mentions) Stata, by StataCorp (1 mentions) Spss statistics 25, by IBM (1 mentions) Stata 16, by StataCorp (1 mentions) Spss statistics v23, by StataCorp (1 mentions) Excel 2010, by StataCorp (1 mentions) R version 4, by R Project for Statistical Computing (1 mentions) Spss version 26, by IBM (1 mentions)

208 protocols using stata ic 16

Longitudinal Analysis of PVC Burden

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Linear mixed modeling was performed in Stata/IC 16.0 to analyze the change in the estimated mean for assessments from pretreatment to post treatment and from pretreatment to 3-month and 6-month follow-up, respectively. Effect sizes of within-group changes (Cohen d) were calculated as the mean change between the two compared time points (pretreatment to post treatment, pretreatment to 3-month follow-up, and pretreatment to 6-month follow-up) divided by each measure’s standard deviation at baseline. The 95% CIs for effect sizes were calculated in R [30 ] using bootstrapping with 5000 samples. Data were analyzed in an intention-to-treat design, meaning that all participants were included in the analyses regardless of treatment completion status.
The change in objective PVC burden and in the self-reported PVC burden (ie, indicating PVC symptoms) was analyzed using the ECG measurements from pretreatment to post treatment and from pretreatment to 6-month follow-up in Stata/IC 16.0. Profile analysis with a Poisson generalized estimation equation model and log-link function was used for the incidence rate of objective PVCs and self-reported PVCs by tapping the patch recorder device.

Liliequist B.E., Särnholm J., Skúladóttir H., Ólafsdóttir E., Ljótsson B, & Braunschweig F. (2024). Cognitive Behavioral Therapy for Symptom Preoccupation Among Patients With Premature Ventricular Contractions: Nonrandomized Pretest-Posttest Study. JMIR Cardio, 8, e53815.

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Malnutrition, Weight Loss, and Surgical Outcomes

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Statistical analyses were conducted using Stata/IC 16.0 software (StataCorp, College Station, TX, USA, 2019). Descriptive statistics included measurements such as frequencies, percentages, mean and standard deviation (SD), and median and interquartile range (IQR). Differences in nutritional outcomes between surgical procedures were determined using Fisher’s exact test. Univariate logistic regression analysis was used to determine demographic and clinical factors associated with malnutrition and clinically significant weight loss prior to surgery. Multivariate logistic regression models were developed to determine factors independently associated with malnutrition and unintentional weight loss. Best models were selected using statistical significance threshold of p < 0.05 and goodness of fit

R^{2}

. Multivariate models adjusting for age, surgical procedure, tumour location and tumour stage were developed to determine associations between malnutrition and unintentional weight loss with LOS (continuous outcome, linear regression), and surgical complications (binary outcome, logistic regression).

Deftereos I., Yeung J.M., Arslan J., Carter V.M., Isenring E, & Kiss N. (2021). Assessment of Nutritional Status and Nutrition Impact Symptoms in Patients Undergoing Resection for Upper Gastrointestinal Cancer: Results from the Multi-Centre NOURISH Point Prevalence Study. Nutrients, 13(10), 3349.

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Evaluating Hospital Trauma Care Gaps

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An exhaustive list was compiled of the shortcomings or issues identified in each hospital. For equipment and medication separately, it then thematically analyzed the content of that list to create meaningful categories based on WHO Guidelines for Essential Trauma Care [2 ].
All statistical analyses were conducted using Stata (Software for Statistics and Data Science, STATA/IC 16.0).

Amado V., Couto M.T., Filipe M., Möller J., Wallis L, & Laflamme L. (2023). Assessment of critical resource gaps in pediatric injury care in Mozambique’s four largest Hospitals. PLOS ONE, 18(6), e0286288.

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Survival Analysis of Drug Treatments

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Drug survival was analyzed with the Kaplan–Meier method with regard to overall discontinuation. For each treatment, survival curves were analyzed separately, depending on the treatment line. Patients still using the treatment at the end of the study were censored. Differences in drug survival for each drug in first-line treatment were analyzed using the log-rank test. Predictive factors (age at treatment initiation, gender, and prescribed drug) were analyzed using univariate cox regressions. Pearson's χ² test was performed to test for differences in demography of the patients, treatment effect, and reasons for discontinuation. In second- to fourth-line treatments, the sample sizes were too small (n < 30) to allow for proper statistical evaluations; thus, no χ² test was performed on these groups. Statistical analysis was performed using STATA/IC 16.0 (for the survival analysis, log-rank test, and Cox regression) and R software, version 3.6.3 (for qualitative and quantitative data processing, χ² test, and Fisher's exact test). Measures of central tendency and dispersion are shown as mean ± standard deviation (SD) unless otherwise indicated.

Elsgaard S., Danielsen A.K., Thyssen J.P., Deleuran M, & Vestergaard C. (2021). Drug survival of systemic immunosuppressive treatments for atopic dermatitis in a long-term pediatric cohort. International Journal of Women's Dermatology, 7(5Part B), 708-715.

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Predictive Score for Bacterial Sepsis

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Clinical parameters that were (1) previously been established as independent predictors of sepsis or (2) that the authors (with more than 5 years of clinical experience) considered as clinically relevant or (3) were predictors of bacterial sepsis in the bivariate analysis were included to construct a predictive score. The data were divided into test and train data on the ratio of 80:20. Risk ratios estimated from the multivariable Poisson regression model on the test dataset were rounded and used as weights for the prediction score. We constructed ROC curves along with LRs for segments of the ROC curve for both the training set and test set. The Tripod checklist was used to report the predictive scores.¹⁸All analyses were carried out using STATA IC/16.0. The code for the analysis is present in the appendix.

Paul A., Newbigging N.S., Lenin A., Gowri M., Varghese J.S., Nell A.J., Abhilash K.P., Binu A.J., Chandiraseharan V.K., Iyyadurai R, & Varghese G.M. (2023). Role of Neutrophil Gelatinase-associated Lipocalin (NGAL) and Other Clinical Parameters as Predictors of Bacterial Sepsis in Patients Presenting to the Emergency Department with Fever. Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine, 27(3), 176-182.

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Oral Health Impact in Cardiac Patients

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Data were described descriptively and means (standard deviation), median (first and third quartile), and frequency distributions were calculated; the distribution of the continuous variables was tested by the Shapiro–Wilk test. The data recorded from cohort 2 were compared to the previously published data from cohort 1 [24 (link)]; any differences between the 2 study cohorts were assessed by the Mann–Whitney U test (if not normally distributed) or by the independent t-test (if normally distributed) and chi-squared test was applied for comparison of frequency distributions. In cohort 2, additional comparisons with the same statistical methods were performed between patients continuing with dental treatment at the CCU and those continuing with a general dentist. Any correlations between the OHIP scores and the clinical parameters were assessed by Pearson’s correlation coefficient. Statistical analysis was performed with STATA/IC 16.0 for Mac and a p-value of ≤ 0.05 was considered statistically significant.

Bertl K., Savvidis P., Kukla E.B., Schneider S., Zauza K., Bruckmann C, & Stavropoulos A. (2021). Including dental professionals in the multidisciplinary treatment team of head and neck cancer patients improves long-term oral health status. Clinical Oral Investigations, 26(3), 2937-2948.

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Cervical Cancer Methylation Signatures

Cited 2 times

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This study was designed to have a 90% power to detect a 10% difference in DNA methylation (%) between successive categories of cytology. From the literature, locus-specific promoter methylation levels (%) for NILM, LSIL/HSIL and cervical cancer have ranged from 0–5%, 15–30%, and 30–60%, respectively (Lai et al., 2008 (link); Wentzensen et al., 2009 (link); Siegel et al., 2015 (link)). To detect a 10% difference in methylation levels using a one-sided test set at α = 0.05 and β = 0.10 with an allocation ratio of 1, a total accrual target of N = 306 and n = 153 per group was required. The quota sampling strategy assured adequate representation from each cytological grade. Additional samples were collected to compensate for potential sample inadequacy and laboratory errors.
Data were summarized using means (95% CI), medians (IQR), and proportions. For hypothesis testing, Wilcoxon rank sum and Kruskal-Wallis tests were used for non-parametric, numerical, or ordinal data. Categorical data were compared using the chi-square test. Correlation between ordinal variables was determined by Spearman’s rho. p-values < 0.05 were considered statistically significant. Statistical analyses were performed using STATA/IC 16.0 (StataCorp LP).

Shen-Gunther J., Xia Q., Stacey W, & Asusta H.B. (2020). Molecular Pap Smear: Validation of HPV Genotype and Host Methylation Profiles of ADCY8, CDH8, and ZNF582 as a Predictor of Cervical Cytopathology. Frontiers in Microbiology, 11, 595902.

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Exploratory Outcomes Assessment Protocol

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Descriptive characteristics are reported with mean and SD and relative numbers when appropriate. Paired t-tests were used to determine differences between baseline and follow-up for all observed exploratory outcomes to provide estimates around the observed changes. All statistical analyses were performed in STATA/IC 16.0 (StataCorp. 2019. Stata Statistical Software: release 16. College Station, TX: StataCorp LLC).

Henriksen P., Junge T., Bojsen-Møller J., Juul-Kristensen B, & Thorlund J.B. (2022). Supervised, Heavy Resistance Training Is Tolerated and Potentially Beneficial in Women with Knee Pain and Knee Joint Hypermobility: A Case Series. Translational Sports Medicine, 2022, 8367134.

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Merging CSFV and BVDV Data Analysis

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Data were entered and organized into an Excel spreadsheet (Microsoft, Sacramento, California, USA). The CSFV data were merged with the BVDV data, by farm name and animal name. Descriptive statistics of cross-tabulations were carried out using STATA/IC 16.0 (StataCorp LLC, College Station, Texas, USA).

Muasya D., Leeuwen J.V., Gitau G., McKenna S., Heider L, & Muraya J. (2022). Evaluation of antibody and antigen cross-reaction in Kenyan dairy cattle naturally infected with two pestiviruses: Bovine viral diarrhea virus and classical swine fever virus. Veterinary World, 15(5), 1290-1296.

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Perioperative Mortality Risk Comparison

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We first performed a descriptive comparison between patients with “OM Not Needed” and “OM Not Recommended” to assess differences in patient factors and outcomes. We included demographic factors such as age and sex, along with other factors such as number of comorbidities and functional status. Data are reported as n (%) for categorical variables and median (interquartile range) for continuous variables. We used a chi-square test to compare categorical variables. If groups were small (<5), then a Fisher’s exact test was used. A Wilcoxon rank-sum (Mann-Whitney two-sample statistic) was used to compare continuous variables. All calculations were performed using Microsoft Excel (Redmond, WA) or Stata IC 16.0 (StataCorp, College Station, TX).
We then described, for the entire population, along with the OM Not Needed, OM Not Recommended, and palliative care subgroups, the projected risk of 30-day perioperative mortality of “low risk” and “high risk” operative management using predicted outcomes from the SRC, alongside the observed outcomes of our nonoperatively managed patient cohort. No statistical comparisons were performed for this descriptive evaluation.
This study was approved by our hospital’s Institutional Review Board which included a waiver of consent.

Deverakonda D.L., Kishawi S.K., Lapinski M.F., Adomshick V.J., Siff J.E., Brown L.R, & Ho V.P. (2022). What if We Don’t Operate? Outcomes of Nonoperatively Managed Emergency General Surgery Patients. The Journal of surgical research, 284, 29-36.

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