Stata statistical software release 11

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Stata Statistical Software: Release 11 is a comprehensive, integrated software package for data analysis, graphics, and publication-quality presentations. It provides a wide range of statistical and graphical tools for data management, analysis, and reporting.

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Lab products found in correlation

Stata, by StataCorp (4 mentions) Stata 11, by StataCorp (3 mentions) Stata version 11, by StataCorp (3 mentions) Stata 11 statistical software, by StataCorp (1 mentions) Prism 6, by GraphPad (1 mentions) Prism 4.00 for windows, by GraphPad (1 mentions) Spss statistics for windows version 23, by IBM (1 mentions) Epidata analysis software, by StataCorp (1 mentions) Sas version 9, by SAS Institute (1 mentions) Stata software version 11, by StataCorp (1 mentions) Mathematica 9, by Wolfram (1 mentions) Matlab 2015b, by MathWorks (1 mentions) Igor pro 6, by Wavemetrics (1 mentions)

54 protocols using stata statistical software release 11

Meta-Analysis of IL-1α Polymorphism and Cancer Risk

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The strength of the association between IL-1α -889 C/T polymorphism and cancer risk was estimated by calculating ORs with 95% CIs, based on the genotype frequencies in cases and controls. The pooled ORs were calculated for five models: allele model (T allele vs C allele), dominant model (TT+TC vs CC), recessive model (TT vs TC+CC), homozygous comparison (TT vs CC), and heterozygous comparison (TC vs CC). The chi square-based Q statistic test was employed to test between-study heterogeneity, and heterogeneity was considered significant when P<0.1 for the Q statistic. The fixed effect model was chosen when studies were homogeneous (with P>0.10 for the Q test); otherwise, a random effects model was adopted. The significance of the pooled OR was determined by Z test, and P-value less than 0.05 was considered as statistically significant. Subgroup analyses were carried out to explore the source of heterogeneity among variables, including ethnicity and source of control, respectively. Publication bias was both examined with Begg’s funnel plot24 (link) and Egger’s regression method25 (link) (P<0.05 was considered representative of statistically significant publication bias). All statistics were conducted by using Stata Statistical Software: Release 11.0 (StataCorp, College Station, TX, USA).

Cheng D., Hao Y, & Zhou W. (2014). IL-1α -889 C/T polymorphism and cancer susceptibility: a meta-analysis. OncoTargets and therapy, 7, 2067-2074.

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Predictors of Bone Loss After RYGB

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We report data as mean ± s.d. or median (interquartile range) as appropriate. Repeated measures ANOVA with time-wise comparisons were performed to compare values between the baseline, one and two year’s visits. Spearman’s correlation analysis was performed to assess predictors of bone loss (baseline age and weight as well as changes in weight, lean mass, ratio of lean mass to fat mass and biochemical indices from baseline to two years). As this is the first study exploring the predictors of changes in bone microarchitecture post-RYGB, our intention was to report our findings in a broader context and generate hypotheses. Hence, we did not formally adjust for multiple comparisons, but have interpreted our findings cautiously. A P-value below 0.05 was considered significant. All statistical analyses were performed using Stata Statistical Software release 11.0 (StataCorp LP).

Shanbhogue V.V., Støving R.K., Frederiksen K.H., Hanson S., Brixen K., Gram J., Jørgensen N.R, & Hansen S. (2017). Bone structural changes after gastric bypass surgery evaluated by HR-pQCT: a two-year longitudinal study. European Journal of Endocrinology, 176(6), 685-693.

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Descriptive and Bivariable Analyses Protocol

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Descriptive analyses included proportions for categorical variables and mean and standard deviation or median with interquartile range (IQR [25th and 75th percentiles]) for continuous variables. Bivariable analyses were performed using chi-square and Fisher's exact tests or t test and Mann-Whitney U test, where appropriate. Data analyses were performed using Stata Statistical Software, release 11.0 (Stata Corporation, College Station, Texas, US).

Rowe B.H., Singh M., Villa-Roel C., Leiter L.A., Hramiak I., Edmonds M.L., Lang E., Sivilotti M., Scheuermeyer F., Worster A., Riley J., Afilalo M., Stiell I., Yale J.F., Woo V.C, & Campbell S. (2015). Acute management and outcomes of patients with diabetes mellitus presenting to Canadian emergency departments with hypoglycemia. Canadian journal of diabetes, 39 Suppl 4.

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Cancer Incidence in Lithuanian T2DM Cohort

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We calculated standardized incidence ratios (SIRs) for site-specific and overall cancer as a ratio of observed number of cancer cases in people with T2DM to the expected number of cancer cases in the underlying general Lithuanian population.
Expected number of cancer cases were calculated by multiplication of the exact person-years under observation in the cohort by sex, calendar year, and 5 year age-group specific national incidence rates [7 ]. The person-time of observation was computed from the date of the first entry of T2DM diagnosis in the NHIF database until the cohort exit date. 95% confidence intervals for the SIRs were estimated assuming the number of observed cases follows Poisson distribution.
All statistical analyses were carried out using STATA 11 statistical software (StataCorp. 2009. Stata Statistical Software: Release 11.0. College Station, TX, USA). The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Vilnius Regional Biomedical Research Ethics Committee (Nr. 158200-17-913-423).

Linkeviciute-Ulinskiene D., Patasius A., Zabuliene L., Stukas R, & Smailyte G. (2019). Increased Risk of Site-Specific Cancer in People with Type 2 Diabetes: A National Cohort Study. International Journal of Environmental Research and Public Health, 17(1), 246.

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Binary Classification of Research Protocol Evaluation

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To better understand the results, a conversion was performed to allow binary classification into “satisfactory” and “unsatisfactory”, “complete” and “adequate” or “excellent” and “good” were combined into “satisfactory”; “incomplete” and “insufficient” or “questionable” and “bad” were combined into “unsatisfactory”. The proportion of “satisfactory” and “unsatisfactory” responses was calculated for each reader and parameter. The proportion of inter-reader agreement in a four-level score and a combined two-level score were also calculated for each parameter. All data analysis was performed with STATA Statistical Software, Release 11.0 (College Station, TX, USA).

Wang Z., Hansis E., Chen R., Duran R., Chapiro J., Sheu Y.R., Kobeiter H., Grass M., Geschwind J.F, & Lin M. (2016). Automatic bone removal for 3D TACE planning with C-arm CBCT: Evaluation of technical feasibility. Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy, 25(3), 162-170.

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Analyzing Animal Health with Biomarkers

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Several logistic regression models were set up to assess the relationship between the animal’s health status and the genetic, immunoproteomic, or immunohistochemical patterns. A logistic regression model was also used to investigate the relationship between the proteomic pattern, genetic profile, and animal age. Sex and breed were entered in the model as confounding variables. The distribution of IBA and GFAP values for different grades of Aβ deposition was described by boxplots. The association of IBA and GFAP values with Aβ deposition grading was analysed using a Kruskal-Wallis test. Statistical data analysis was performed using STATA 11 (StataCorp. 2009. Stata Statistical Software: Release 11. College Station, TX, StataCorp LP).

Vallino Costassa E., Fiorini M., Zanusso G., Peletto S., Acutis P., Baioni E., Maurella C., Tagliavini F., Catania M., Gallo M., Faro M.L., Chieppa M.N., Meloni D., D’Angelo A., Paciello O., Ghidoni R., Tonoli E., Casalone C, & Corona C. (2016). Characterization of Amyloid-β Deposits in Bovine Brains. Journal of Alzheimer's Disease, 51(3), 875-887.

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Serum Albumin and Mortality Outcomes

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The baseline characteristics and clinical variables of the study population were grouped according to 24 months albumin rate reached at 3.5 g/dL and summarized as frequency (percentage) or mean (standard deviation). The difference between groups was estimated using independent two-sample t-test, chi-squared test, or Fisher’s exact test. The Cox proportional hazard regression model was used to determine the association between different albumin categories by using 24-month serum albumin measurements and all-cause mortality. A P-value of < 0.05 was considered statistically significant. Stata version 11.0 (StataCorp. 2009. Stata Statistical Software: Release 11. College Station, TX: StataCorp LP.) was used for all statistical analyses.

Hao N., Cheng B.C., Yang H.T., Wu C.H., Lei Y.Y., Chao M.C., Wang P.Y., Kuo L.C., Moi S.H., Yang C.H, & Chen J.B. (2019). Time-varying serum albumin levels and all-cause mortality in prevalent peritoneal dialysis patients: a 5-year observational study. BMC Nephrology, 20, 254.

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Genetic Associations with Tuberculosis

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Statistical analyses were performed using Stata Statistical Software: Release 11 (StataCorp LP, College Station, TX). Function ‘pwld’ was used to calculate R² measurements of linkage disequilibrium between polymorphisms. SNPs were assessed for association with tuberculosis using “genassoc”¹⁵.

Seshadri C., Thuong N.T., Yen N.T., Bang N.D., Chau T.T., Thwaites G.E., Dunstan S.J, & Hawn T.R. (2014). A Polymorphism in Human CD1A is Associated with Susceptibility to Tuberculosis. Genes and immunity, 15(3), 195-198.

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Predictors of Abnormal Myocardial Perfusion Imaging

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All continuous variables are shown as mean and standard deviation, and all
categorical variables as absolute values and percentages. Normal distribution of
continuous variables was tested by Shapiro-Wilk and Shapiro-Francia tests.
Unpaired Student’s t test was used to compare the means of continuous variables
with normal distribution, and the chi-square test used for analysis of binominal
variables. A p-value<0.05 was considered statistically significant.
The association of clinical variables, type of the test stress, and left
ventricular function with abnormal SPECT-MPI was analyzed by univariate logistic
regression, followed by multivariate analysis. The respective odds ratio (OR)
and 95% confidence intervals were also calculated.
All analyses were performed using a specific software, the Stata Statistical
Software, Release 11 (College Station, TX: StataCorp LP).

Dippe Jr. T., da Cunha C.L., Cerci R.J., Stier Jr. A.L, & Vítola J.V. (2019). Study of Myocardial Perfusion in Obese Individuals without Known Ischemic Heart Disease. Arquivos Brasileiros de Cardiologia, 112(2), 121-128.

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Exploring PTSD Treatment Associations

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Unadjusted and adjusted logistic regression modeling was performed to examine associations between ISEL-12 score and the odds of receiving treatment for PTSD. Separate models were conducted for each type of treatment (hospitalization, emergency department visit, outpatient visit, or psychiatric medication), in addition to an overall model examining any type of treatment. Taylor series linearization was used to take into account the complex survey design of the NESARC. The adjusted models adjusted for PTSD symptom count and for sociodemographic variables that have previously been found to be associated with PTSD (6 (link)). Logistic regression calculates odds ratios (ORs) as the measure of strength of association, and 95% confidence intervals (CIs) are presented to aid interpretation. All analyses were conducted using Stata Version 11 [StataCorp. Stata Statistical Software: Release 11. College Station, TX: StataCorp LP; 2009].

Sripada R.K., Pfeiffer P.N., Rauch S.A, & Bohnert K. (2014). Social support and mental health service use among individuals with PTSD in a nationally-representative survey. Psychiatric services (Washington, D.C.), 66(1), 65-71.

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