Stata mp version

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Stata/MP is a software package designed for statistical analysis and data management. It is optimized for multi-core and multi-processor systems, allowing for faster computation compared to the standard version of Stata. Stata/MP provides access to the same analytical and data manipulation capabilities as the standard Stata software.

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Stata MP, version 16.0 statistical software Stata/MP version 15.1 Stata version 16/MP for Windows Stata/MP, version 14.1 (64-bit) Stata MP/IC Version 16 Stata MP software, Version 16.1 STATA MP statistical software version 16 Stata/MP version 17.0 Stata MP Version 12.0® Stata MP Version 11.2

12 protocols using stata mp version

Comparing Patient Characteristics and Impressions of AHHC Services during COVID-19

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First, we compared the patient characteristics (age, sex, first triage color, diagnosis, disease severity, prescription of any drugs, examination by health care personnel wearing PPE, diagnostic tools for COVID-19, and time of medical examination) between questionnaire responders and non-responders. Pearson’s chi-square test was used to compare the proportions of categorical variables between the groups. The t-test was used to compare the time of medical examination, a continuous variable.
Second, we summarized patients’ impressions of AHHC medical services during the COVID-19 pandemic stratified by patient characteristics.
All statistical analyses were conducted using Stata/MP version 14 (Stata Corp., College Station, TX, USA), and the level of significance was set at p < 0.05.

Morita K., Inokuchi R., Jin X., Ishikawa M, & Tamiya N. (2021). Patients’ impressions of after-hours house-call services during the COVID-19 pandemic in Japan: a questionnaire-based observational study. BMC Family Practice, 22, 184.

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Hypertension Risk Factors: Anthropometric Analysis

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Categorical variables of general characteristics were presented as frequencies and percentages, whereas continuous variables were expressed as mean values and standard deviation (SD). Differences between the groups were evaluated using a one-way ANOVA for continuous and Chi-square for categorical variables. The odds ratio and 95% confidence interval were calculated through multinomial logistic regression analyses to determine the association of prehypertension and hypertension with anthropometric and body composition indices.
Relevant confounders were selected based on an extensive literature search. Firstly, their clinical and pathophysiological association with the desired outcome and exposures was assessed using univariate regression models. Then, statistically significant covariates, which have clinical implications, were included in the multivariable logistic regression models. Analytical models were set as model crude; model 1: adjusted for age; model 2: adjusted for age and gender; model 3: adjusted for age, gender, marital, education, job, physical activity, smoking, income, kidney stone, diabetes, BMI, TG, TC, LDL, HDL. All analyses were done in Stata MP (version 17). P-value < 0.05 was taken as statistically significant for all analyses.

Khaleghi M.M., Jamshidi A., Afrashteh S., Emamat H., Farhadi A., Nabipour I., Jalaliyan Z., Malekizadeh H, & Larijani B. (2023). The association of body composition and fat distribution with hypertension in community-dwelling older adults: the Bushehr Elderly Health (BEH) program. BMC Public Health, 23, 2001.

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Statistical Analyses in Biomedical Research

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Statistical analyses were performed using Stata/MP version 14.1 (StataCorp., College Station, TX, USA), GraphPad Prism version 7.00 for Mac (GraphPad Software, La Jolla California, USA) and R version 3.4.1 (R Core Team, Vienna, Austria) statistical software. Pearson's chi-square test and Fisher's exact test were employed for comparison of categorical clinicopathologic variables. Survival analysis was constructed by Kaplan-Meier plots and log-rank test. A Cox proportional hazard regression model adjusted for other prognostic covariates was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Student's t-test, the Mann-Whitney Wilcoxon test and the Kruskal-Wallis test were utilized to compare continuous variables and ordered categorical variables. A two-sided P-value less than 0.05 was considered significant unless otherwise stated.

Liu X.Y., Ma D., Xu X.E., Jin X., Yu K.D., Jiang Y.Z, & Shao Z.M. (2018). Genomic Landscape and Endocrine-Resistant Subgroup in Estrogen Receptor-Positive, Progesterone Receptor-Negative, and HER2-Negative Breast Cancer. Theranostics, 8(22), 6386-6399.

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Prevalence of Undiagnosed Diabetes Mellitus

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A format prepared in Microsoft Excel spreadsheet was used to extract the appropriate details from each original report. For further review, the extracted data was exported to STATA/MP version 16.0 software. The random effects model with Der Simonian-Laird weights was used to assess the pooled estimate of prevalence of undiagnosed DM, IFG, and its related factors^{19 (link)}. The Cochrane Q-test and I² statistics were used to screen for statistical heterogeneity^{20 (link)}. Subgroup analysis based on the mean age of the participants was used to reduce the variance of point estimates between primary studies. A sensitivity analysis was also performed to see how single studies affected the pooled estimate. A funnel plot and Egger's statistical test were used to search for publication bias (small study effect). The existence of a small study effect is treated by non-parametric trim and fill analysis using the random effects model when the p-value is less than 0.05²¹. The pooled effect was expressed as an odds ratio to classify variables linked to the outcome variable.

Yitbarek G.Y., Ayehu G.W., Asnakew S., Chanie E.S., Bayih W.A., Feleke D.G., Amare T.J., Teshome F., Teshome A.A., Arage G., Ayele F.Y., Gebremariam A.D., Engidaw M.T, & Tiruneh S.A. (2021). Undiagnosed diabetes mellitus and associated factors among adults in Ethiopia: a systematic review and meta-analysis. Scientific Reports, 11, 24231.

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Systematic Review and Meta-Analysis

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We conducted a PRISMA-compliant systematic review and meta-analysis. We ran an electronic search of PubMed, Cochrane CENTRAL, Scopus and Web of Science to identify the relevant published studies. Data were extracted and pooled as standardised mean difference (SMD) or risk ratio (RR) using StataMP version 17 for macOS.

Mohamed A., Negida A, & Shaboub A. (2024). Minimally invasive versus conventional sternotomy for aortic valve replacement: A systematic review and meta-analysis. Clinical Medicine, 23(Suppl 6), 58-59.

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Comparative Analysis of HIV-1 Viral Load Assays

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Only valid results were used for analyses. VL data were transformed into log₁₀ copies/mL. Statistical analysis was performed using Stata/MP version for Mac.
In the correlation analysis, only the VL data from patients who had quantitative values in both Aptima Assay and Abbott RT were included for analysis. The correlation was determined by simple linear regression with generation of Pearson’s correlation coefficient (r) as well as Bland–Altman analysis [28 (link)] to calculate the bias and the limit of agreement between assay results. The agreement between the assays for the plasma and DBS sample types were determined at the clinical decision point of 1000 copies/mL using a contingency table.

Mwau M., Schaffer S., Kimani H., Kasiano P., Ogolla F., Ajema E., Adoyo S., Nyairo E., Saleri N, & Nair S.V. (2022). Comparison of the performance of Aptima HIV-1 Quant Dx Assay with Abbott RealTime HIV Assay for viral load monitoring using plasma and Dried Blood Spots collected in Kenya. PLoS ONE, 17(8), e0269838.

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Statistical Analysis of Clinical Data

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Calculations were performed using Stata/MP version 13.0 for Mac (StataCorp LP). Variables were checked for skewness and kurtosis to determine normality. Clinical and demographic features are presented as medians [interquartile range] and means (± standard deviation) for non‐parametric and parametric data respectively. Differences between continuous parametric variables were examined with the t test; the Wilcoxon rank‐sum test or the Wilcoxon‐Mann‐Whitney test were used for non‐normally distributed continuous and ordinal variables, while differences between dichotomous variables were evaluated with the χ² test or the Fishers exact test (Tables 1 and 2). P‐values throughout the results were two‐sided. Logistic regression was performed on clinically and statistically significant variables as part of a multivariate analysis.

Farag M., Jack G.S., Papa N., Wong L., Bolton D.M, & Lenaghan D. (2021). Which has more complications?—Shockwave lithotripsy versus endoscopic treatment of renal calculi with 1‐year follow‐up in an Australian population. BJUI Compass, 2(4), 275-280.

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Predicting Outcomes in Otic Ventilation Tube Insertion

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The relationship between clinical factors (age, sex, history of OME or repeated AOM, duration of retention, and whether VT was removed spontaneously or intentionally) and outcomes (OME recurrence or persistent TM perforation) was analyzed using multivariate logistic regression analyses with the statistical software program Stata/MP, version 14.0. For continuous predictors (age and tube retention period), the OR represents the increase in odds of the outcome with every single-unit (month) increase in the input variable. A survival curve was constructed according to the Kaplan-Meier method and compared using the log-rank test. For the Kaplan-Meier analyses, the event was defined as spontaneous VT extrusion, and intentional removal was treated as censored. Pearson w 2 test was used to determine the difference between the outcomes between 2 groups divided by the cutoff value. Statistical significance was set at P < .05.

Takai S., Nomura K., Oda K., Ozawa D., Irimada M., Ikeda R., Kakuta R., Katori Y, & Ohyama K. (2023). Clinical Factors Associated With the Outcomes of Long-Term Middle Ear Ventilation Tube Insertion in Pediatric Patients. Ear, nose, & throat journal, 102(10).

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Predictors of PTSD Trajectory in the Canadian Forces

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Analyses were conducted using STATA/MP (Version 16).³⁸ Weights calculated by Statistics Canada were applied to all inferential analyses to ensure representativeness of all Canadian Forces members in 2002. Bootstrapping was used as a standard error estimation technique to account for the survey’s complex sampling design. Weighted cross-tabulations were used to examine the prevalence of each variable among the 4 PTSD courses. A series of multinomial regressions adjusted for sociodemographic variables were conducted to examine the associations between each potential baseline predictor, interim correlate, and persistent/recurrent, remitted, and new onset PTSD courses relative to no lifetime PTSD. To understand factors associated with persistence relative to recovery from PTSD, the remitting and persistent/recurrent PTSD courses in the multinomial models were also compared.

Mota N., Bolton S.L., Enns M.W., Afifi T.O., El-Gabalawy R., Sommer J.L., Pietrzak R.H., Stein M.B., Asmundson G.J, & Sareen J. (2021). Course and Predictors of Posttraumatic Stress Disorder in the Canadian Armed Forces: A Nationally Representative, 16-Year Follow-up Study: Cours et prédicteurs du trouble de stress post-traumatique dans les Forces armées canadiennes: une étude de suivi de 16 ans nationalement représentative. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 66(11), 982-995.

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Livestock Ownership and Child Anemia

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The data were downloaded from measure DHS as SPSS file, cleaned, and exported to STATA/MP version 16.0 for analysis. We used the household weight (hv005) to deal with the survey weights in the PSM which is the inverse of its household selection probability multiplied by the inverse of the household response rate in the stratum. The matched frequencies and percentages were calculated.
The mean and mean differences in hemoglobin concentration were calculated using paired t‐test and independent t‐test, respectively. The association between livestock ownership and anemia was computed using chi‐square statistics. The relative risks (RR) with a 95% CI were computed using binomial regression model.
PSM was used to determine the effect of livestock ownership on child anemia. Since DHS data are cross‐sectional in nature, the inference is difficult without statistical adjustments. The PSM method allows for the design and analysis of observational data while accounting for the randomization issue via baseline variables (Austin, 2011 (link)). The propensity score is thus the probability of treatment assignment conditional on observed baseline characteristics.

Zerfu T.A., Abera A., Duncan A., Baltenweck I, & McNeill G. (2023). Ownership of small livestock species, but not aggregate livestock, is associated with an increased risk of anemia among children in Ethiopia: A propensity score matching analysis. Food Science & Nutrition, 11(9), 5157-5165.

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