Stata 16.0 mp for linux

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Stata 16.0/MP for Linux is a software package designed for statistical analysis, data management, and graphics. It provides a comprehensive set of tools for researchers, analysts, and statisticians to perform a wide range of statistical procedures, including regression analysis, time series analysis, and multivariate techniques. Stata 16.0/MP for Linux is optimized for high-performance computing on Linux-based systems, enabling efficient analysis of large datasets.

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10 protocols using stata 16.0 mp for linux

Eplet Mismatches and dnDSA Formation

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We stratified our population based on dnDSA formation at any point during post-transplant follow-up. Patient characteristics were compared using Mann–Whitney U test for continuous variables, and Chi-square or Fischer’s exact test for categorical variables. We reported the most prevalent eplet mismatches by class. Out of the eplet mismatches to which at least five recipients were exposed, we reported the 30 most prevalent targets of dnDSA formation from both classes. All analyses were performed using Stata 16.0/MP for Linux (College Station, Texas).
Approval to conduct this study was obtained from the Institutional Review Board at Johns Hopkins University School of Medicine.

Charnaya O., Jones J., Philogene M.C., Chiang P.Y., Segev D.L., Massie A.B, & Garonzik-Wang J. (2021). Eplet mismatches associated with de novo donor-specific HLA antibody in pediatric kidney transplant recipients. Pediatric nephrology (Berlin, Germany), 36(12), 3971-3979.

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Comparing Recipient Characteristics Analysis

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To compare recipient characteristics, we used Pearson’s chi-squared tests for categorical variables, and the Kruskal-Wallis test for nonnormally distributed continuous variables. All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Hallett A., Motter J.D., Frey A., Higgins R.S., Bush E.L., Snyder J., Garonzik-Wang J.M., Segev D.L, & Massie A.B. (2021). Trends in Heart and Lung Transplantation in the United States Across the COVID-19 Pandemic. Transplantation Direct, 7(10), e759.

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Comparing Characteristics of Kidney Transplant Recipients

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Baseline characteristics of ILDKT and CLDKT recipients were compared using ANOVA for normally distributed continuous variables, Kruskal‐Wallis rank sum test for non‐normally distributed continuous variables, or the chi‐squared test for binary or categorical variables. For all weighted analyses, a robust, sandwich estimator was used to prevent overestimation of the variance given that the weights were estimated.²⁰, ²¹ Confidence intervals are reported as per the method of Louis and Zeger.²² All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Avery R.K., Motter J.D., Jackson K.R., Montgomery R.A., Massie A.B., Kraus E.S., Marr K.A., Lonze B.E., Alachkar N., Holechek M.J., Ostrander D., Desai N., Waldram M.M., Shoham S., Steinke S.M., Subramanian A., Hiller J.M., Langlee J., Young S., Segev D.L, & Garonzik Wang J.M. (2020). Quantifying infection risks in incompatible living donor kidney transplant recipients. American Journal of Transplantation, 21(4), 1564-1575.

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Comparing ILDKTr and CLDKTr Induction Protocols

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To compare characteristics of ILDKTr and CLDKTr, we used Pearson’s chi-squared tests for categorical variables, ANOVA for normally-distributed continuous variables, and the Kruskal-Wallis test for non-normally-distributed continuous variables. Recipients who received both depleting and non-depleting induction (n=312, 1.9%) were categorized as having depleting induction in Table 1. Unless otherwise specified, multiple imputation by chained equations with 10 imputations over 100 iterations was used to handle missing data (23 (link)). The extent of missing data ranged from 0.7%−25.5%. To account for within center clustering of outcomes, we used a robust sandwich estimator. Confidence intervals are reported as per the method of Louis and Zeger. All analyses were performed using Stata 16.0/MP for Linux (College Station, Texas).

Motter J.D., Jackson K.R., Long J.J., Waldram M.M., Orandi B.J., Montgomery R.A., Stegall M.D., Jordan S.C., Benedetti E., Dunn T.B., Ratner L.E., Kapur S., Pelletier R.P., Roberts J.P., Melcher M.L., Singh P., Sudan D.L., Posner M.P., El-Amm J.M., Shapiro R., Cooper M., Verbesey J.E., Lipkowitz G.S., Rees M.A., Marsh C.L., Sankari B.R., Gerber D.A., Wellen J.R., Bozorgzadeh A., Gaber A.O., Heher E.C., Weng F.L., Djamali A., Helderman J.H., Concepcion B.P., Brayman K.L., Oberholzer J., Kozlowski T., Covarrubias K., Massie A.B., Segev D.L, & Garonzik-Wang J.M. (2021). Brief Communication: Delayed Graft Function and Acute Rejection Following HLA-Incompatible Living Donor Kidney Transplantation. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 21(4), 1612-1621.

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Comparing ILDKT and CLDKT Recipient Characteristics

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Baseline characteristics of ILDKT and CLDKT recipients were compared using ANOVA for normally distributed continuous variables, Kruskal-Wallis rank sum test for non-normally distributed continuous variables, or the chi-squared test for binary or categorical variables. For all weighted analyses, a robust, sandwich estimator was used to prevent overestimation of the variance given that the weights were estimated (20 –21 ). Confidence intervals are reported as per the method of Louis and Zeger (22 ). All analyses were performed using Stata 16.0/MP for Linux (College Station, Texas).

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Stata 16.0/MP for Linux Analyses

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All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Boyarsky B.J., Massie A.B., Love A.D., Werbel W.A., Durand C.M., Avery R.K., Jackson K.R., Kernodle A.B., Thomas A.G., Ronin M., Altrich M., Niles P., Trahan C., Hewlett J., Segev D.L, & Garonzik-Wang J.M. (2020). Early Experiences With COVID-19 Testing in Transplantation. Transplantation Direct, 6(7), e572.

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Pediatric Kidney Transplant During COVID-19

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We presented characteristics of pediatric kidney transplant recipients separately in three time periods: January 1 -March 15, 2020 (“Early”); March 16 - April 30, 2020 (“Middle”); and May 1 - June 30, 2020 (“Late”). Continuous variables were presented as median and interquartile range, and categorical variables were presented as counts and proportion. Comparison between groups were tested using Kuskal-Wallis or Mann Whitney-U, as appropriate, for continuous variables and Fisher’s exact test for categorical variables. We used 2015 as reference year to calculate KDPI [22 (link)]. We obtained pediatric kidney waitlist changes or transplant volume by center, month and year from January 1, 2016 to February 28, 2020, and constructed a mixed-effects Poisson regression with a center-level random intercept to obtain expected daily counts by center (monthly counts divided by 31), using methods previously described [23 (link)]. The expected counts of each time period were the sum of expected center-level counts during the corresponding length of time (March 1 to April 30: 47 days; May 1 - June 30: 61 days). We then compared the observed and expected counts of each time period using Chi-square testing. We used an a of 0.05 to define statistical significance. All analyses were performed using Stata 16.0/MP for Linux (College Station, Texas).

Charnaya O., Chiang T.P., Wang R., Motter J., Boyarsky B., King E., Werbel W., Durand C.M., Avery R., Segev D., Massie A, & Garonzik-Wang J. (2020). Effects of COVID19 Pandemic on Pediatric Kidney Transplant in the United States. Research Square.

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Pediatric Kidney Transplant Trends During COVID-19

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We present characteristics of pediatric kidney transplant recipients separately in three time periods: January 1–March 15, 2020 (“Early”); March 16–April 30, 2020 (“Middle”); and May 1–June 30, 2020 (“Late”). Continuous variables were presented as median and interquartile range, and categorical variables were presented as counts and proportion. Comparison between groups were tested using Kuskal–Wallis or Mann–Whitney U tests, as appropriate, for continuous variables and Fisher’s exact test for categorical variables. We used 2015 as reference year to calculate kidney donor profile index (KDPI) [22 (link)]. We obtained pediatric kidney waitlist changes or transplant volume by center, month, and year from January 1, 2016, to February 28, 2020, and constructed a mixed-effects Poisson regression with a center-level random intercept to obtain expected daily counts by center (monthly counts divided by 31), using methods previously described [23 ]. The expected counts of each time period were the sum of expected center-level counts during the corresponding length of time (March 15 to April 30: 47 days; May 1–June 30: 61 days). We then compared the observed and expected counts of each time period using chi square testing. We used an α of 0.05 to define statistical significance. All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Charnaya O., Chiang T.P., Wang R., Motter J.D., Boyarsky B.J., King E.A., Werbel W.A., Durand C.M., Avery R.K., Segev D.L., Massie A.B, & Garonzik-Wang J.M. (2020). Effects of COVID-19 pandemic on pediatric kidney transplant in the United States. Pediatric Nephrology (Berlin, Germany), 36(1), 143-151.

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Sensitivity Analysis of OSA and CHF on Posttransplant Outcomes

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With regard to the potential confounding nature of obstructive sleep apnea (OSA) and congestive heart failure (CHF) on PtPH and posttransplant outcomes, we performed 2 sensitivity analyses to understand how OSA and CHF could affect our inference. In the first analysis, we added OSA into the model used to calculate IPTW, whereas in the second, we added both OSA and CHF. Pretransplant OSA was identified by at least 1 inpatient or 2 outpatient diagnosis codes 30 d apart (ICD-9: 32723; ICD-10: G4733), as it was not reported in the Centers for Medicare and Medicaid Services-2728 form and therefore not available in the USRDS database.
Transplant candidate and recipient characteristics were compared using t-test for normally distributed continuous variables, Wilcoxon rank-sum test for skewed distributed continuous variables, and Fischer’s exact test for binary or categorical variables. Weighted risk ratios and coefficients from multivariable adjustment models were obtained through complete case analysis. Weighted confidence intervals were reported as per the method of Louis and Zeger.^{37 (link)} All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Nguyen M.C., Po-Yu Chiang T., Massie A.B., Bae S., Motter J.D., Brennan D.C., Desai N.M., Segev D.L, & Garonzik-Wang J.M. (2021). Kidney Transplantation Confers Survival Benefit for Candidates With Pulmonary Hypertension. Transplantation Direct, 7(8), e738.

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Statistical Analysis of Experimental Data

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Confidence intervals and P values are 2-sided with an alpha of 0.05. Confidence intervals are reported according to the methods of Louis and Zeger.^{29 (link)} All analyses were performed using Stata 16.0/MP for Linux (College Station, TX).

Bisen S.S., Getsin S.N., Chiang P.Y., Herrick-Reynolds K., Zeiser L.B., Yu S., Desai N.M., Al Ammary F., Jackson K.R., Segev D.L, & Massie A.B. (2022). Patient and Graft Survival After A1/A2-incompatible Living Donor Kidney Transplantation. Transplantation Direct, 8(11), e1388.

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