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R version 3.6.1 is a free and open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, and is highly extensible.

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88 protocols using r version 3

Genetic Markers and Prostate Cancer

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Demographic and clinico-pathologic associations by study cohort, race, and ERG status were analyzed using the following methods of categorical analysis. ERG status was compared to tumor location and clinical characteristics after combining all three study cohorts (MCC, TCC-consortium, and UPHS). Multivariable logistic regression was used to calculate adjusted odds ratios (aORs) and 95% confidence intervals for the association between ERG status, tumor location, race, and pGS. All multivariable models were also stratified by study cohort to account for unmeasured confounding associated with sample selection. Furthermore, ERG-dependent differences in mRNA expression of the 173 selected genes between the anterior and posterior tumors were analyzed within the ERG^negative and ERG^positive tumor subtypes. Differential log2 median expressions of selected genes were analyzed using Wilcoxon rank sum tests. A false discovery rate (FDR) adjustment for multiple comparisons was performed for each gene. All analyses were conducted in R version 3.5.0 and SAS version 9.4 (SAS Institute, Cary, NC).

Yamoah K., Lal P., Awasthi S., Naghavi A.O., Rounbehler R.J., Gerke T., Berglund A.E., Pow-Sang J.M., Schaeffer E.M., Dhillon J., Park J.Y, & Rebbeck T.R. (2020). TMPRSS2-ERG fusion impact anterior tumor location in men with prostate cancer. The Prostate, 81(2), 109-117.

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Serum BDNF Levels and NAFLD Severity

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Statistical analyses were performed using JMP ver. 14.2.0 software (SAS Institute, Cary, NC, USA) and R version 3.5.0. statistical software (R Foundation for Statistical Computing, Vienna, Austria). Two-tailed Student's t-tests and Tukey-Kramer's HSD tests were to for compare continuous variables showing normal distribution. Wilcoxon rank sum tests and Steel-Dwass tests were used to compare continuous variables with lognormal distributions. The Jonckheere-Terpstra test was used as the trend test. Ordinal logistic regression analysis was performed with to examine relationship between serum BDNF levels and the severity of NAFLD. In this model, NAFLD severity is treated as a three-step ordinal variable of normal, mild, and severe. The explanatory variables set to serum BDNF levels and confounders including age, sex, hemoglobin A1c (HbA1c), systolic body pressure (SBP), smoking history, BMI, and serum triglyceride (TG) levels. These factors are thought to affect both NAFLD and BDNF levels. Values of p less than 0.05 were considered significant.

Hattori Y., Yamada H., Munetsuna E., Ando Y., Mizuno G., Fujii R., Tsuboi Y., Ichino N., Osakabe K., Sugimoto K., Ishikawa H., Ohashi K, & Suzuki K. (2022). Increased brain-derived neurotrophic factor in the serum of persons with nonalcoholic fatty liver disease. Endocrine journal, 69(8).

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Socioeconomic Factors and Oxidative Stress

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Descriptive statistics were used to summarize participant sociodemographic characteristics. Linear regression models were used to calculate crude estimates and 95% confidence intervals (CI) for the associations between SES indicators, psychosocial stress and each oxidative stress biomarker pregnancy average. Adjusted estimates were also obtained from linear regression models in which psychosocial stress was the exposure. QQ-plots were examined for each model to check linear regression assumptions, including linearity, normality, and homoscedasticity. Beta estimates were converted to percent difference in oxidative stress biomarker concentration in association with SES indicators and psychosocial stress. Tests for linear trend across tertiles were conducted using the Cochrane Armitage test.²⁶ SES indicators retained in adjusted models changed point estimates by ≥10%.
Missing data for psychosocial stress measures and covariates (<10% for each imputed variable) was imputed using multiple imputation via chained equations (MICE), which was implemented using the ‘mice’ package in R. Oxidative stress biomarker concentrations were not included as predictors in the imputation procedure. All analyses were conducted in R Version 3.5.0 and SAS 9.4 (Cary, NC).

Eick S.M., Meeker J.D., Brown P., Swartzendruber A., Rios-McConnell R., Shen Y., Milne G.L., Velez-Vega C., Rosario Z., Alshawabkeh A., Cordero J.F, & Ferguson K.K. (2019). Associations Between Socioeconomic Status, Psychosocial Stress, and Urinary Levels of 8-iso-prostaglandin-F2α During Pregnancy in Puerto Rico. Free radical biology & medicine, 143, 95-100.

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Adiposity-Metabolite Associations in Prostate Cancer

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We repeated the analyses to identify adiposity-associated metabolites after restricting to men with (1) complete information on all adiposity measures and (2) fasting blood samples. We repeated the analyses for advanced prostate cancer after excluding men diagnosed in the first two years after the blood draw to minimize the chance that latent disease influenced their metabolite or adiposity measures.
The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. The analyses were conducted using R, version 3.6.0, and SAS, version 9.4 (SAS Institute, Inc. Cary, NC, USA).

Dickerman B.A., Ebot E.M., Healy B.C., Wilson K.M., Eliassen A.H., Ascherio A., Pernar C.H., Zeleznik O.A., Vander Heiden M.G., Clish C.B., Giovannucci E, & Mucci L.A. (2020). A Metabolomics Analysis of Adiposity and Advanced Prostate Cancer Risk in the Health Professionals Follow-Up Study. Metabolites, 10(3), 99.

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Metabolome Pathway Analysis in R and SAS

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E (Y_{i j}) = β_{0} + μ_{i} + β_{1} {species}_{i j} + β_{2} P M_{2.5 i j} + β_{3} N O_{2 i j} + β_{4} O_{3 i j} + β_{5} T e m p e r a t u r e_{i j} + β_{6 - n} C o v a r i a t e s_{i j}

where Y_ij is the metabolome level of subject i at visit j, β₀ is the fixed intercept, μ_i, is the random intercept for subject i, constituent _ij is the moving average of one species at a time (for subject i at visit j.
Analyses were conducted using R version 3.6.0 and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
We then further conducted pathway analysis for the significant metabolites (at p-value <0.01) from the multi- species model and the 100 metabolites that had the highest contributing weights to the ICA-factor2. To do that, we used the ‘Pathway Analysis’ functionality in MetaboAnalyst 4.0, that accounts for both over-representation analyses (i.e., number of significant metabolites within a pathway) and pathway topology (i.e., influence of those metabolites to that given pathway)^{38 (link)} and uses Human Metabolome Database (HMDB), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to define the underlying pathways. We considered statistical significance for the pathways at level of p-value ≤ 0.1²¹ and considered additional noteworthy pathways if the impact score was ≥ 0.5 while p-value < 0.3.

Nassan F.L., Wang C., Kelly R.S., Lasky-Su J.A., Vokonas P.S., Koutrakis P, & Schwartz J.D. (2021). Ambient PM2.5 species and ultrafine particle exposure and their differential metabolomic signatures. Environment international, 151, 106447.

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Survival Analysis of Patient Cohorts

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Patient characteristics in each cohort were described using frequencies and percentages for categorical variables or means and standard deviations for continuous variables. To assess the association between predictors and 1-year survival, we used univariate and multivariable Cox proportional hazards models to estimate the hazard ratios (HRs) and corresponding 95% confidence intervals.
All statistical analyses were performed with SAS version 9.4 or R version 3.6.0 (SAS Institute, Cary, NC, USA). A p-value of less than 0.05 was considered statistically significant.

Wang R.R., Gu H.Q., Wei Y.Y., Yang J.X., Hou Y.X., Liu H.M., Yang Z.Y., Wang X.B, & Jiang Y.Y. (2021). Development and Validation of a Prognostic Model for One-year Survival of Cirrhosis Patients with First-ever Spontaneous Bacterial Peritonitis. Journal of Clinical and Translational Hepatology, 9(5), 647-654.

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Statistical Analyses in Biomedical Research

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All statistical analyses were performed in R version 3.6.0 and SAS version 9.4 (SAS Institute Inc., Cary, NC). We used false discovery rate (FDR) for multiple comparisons correction.

Palacios N., Hannoun A., Flahive J., Ward D., Goostrey K., Deb A, & Smith K.M. (2021). Effect of Levodopa Initiation on the Gut Microbiota in Parkinson's Disease. Frontiers in Neurology, 12, 574529.

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Prognostic Value of Circulating Tumor Cells

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Patient and tumor characteristics were summarized according to CTC status and the proportions between groups (CTC-positive versus CTC-negative) were compared using Pearson’s chi-squared test.
Survival curves were estimated by the Kaplan-Meier method and compared using the log-rank test (18 (link)). Multivariable Cox regression models adjusted for known prognostic factors were used to estimate hazard ratios (HR) and 95% confidence intervals (CI). Restricted mean survival times and differences were calculated for different time points (19 (link)). The prognostic effect of changes in CTC status between baseline and other time points was tested using a time-dependent Cox model. Data collection and statistical analyses were conducted by the Alliance Statistics and Data Center. Statistical analysis was performed using R (version 3.6.0) and SAS software (version 9.4).

Magbanua M.J., Savenkov O., Asmus E.J., Ballman K.V., Scott J.H., Park J.W., Dickler M., Partridge A., Carey L., Winer E, & Rugo H.S. (2020). Clinical significance of circulating tumor cells in hormone receptor-positive metastatic breast cancer patients who received letrozole with or without bevacizumab. Clinical cancer research : an official journal of the American Association for Cancer Research, 26(18), 4911-4920.

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Pathologists' Practices and Policies for Second Opinions

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Descriptive statistics were calculated for pathologist and practice characteristics and for responses to questions related to second opinions. For descriptive purposes, second opinion policy requirements involving any of the four initial diagnoses were consolidated into “Any Policy” versus none at all or “No Policy”. Summary statistics are presented as frequencies and percentages. Pearson's Chi-squared test was used to analyze categorical variables. All reported p-values are two-sided and considered significant at α=0.05. Analyses were performed in R version 3.0.1 [16 ] using the HH [17 ] Likert() function for plots and in SAS 9.3 (SAS Institute Inc., Cary, NC, USA) using the FREQ procedure.

Geller B.M., Nelson H.D., Carney P.A., Weaver D.L., Onega T., Allison K.H., Frederick P., Tosteson A.N, & Elmore J.G. (2014). Second opinion in breast pathology: Policy, practice and perception. Journal of clinical pathology, 67(11), 955-960.

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Comparison of Cardiac Workup Outcomes

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After stratification of patients by the level of cardiac workup received, the Fisher’s exact test or analysis of variance, for categorical and continuous variables, respectively, was used for comparison of patient and procedural characteristics and the aforementioned postoperative outcomes. An affirmative decision was made a priori to set the significance level at α = .05 for all analyses. Statistical analysis was done with JMP Pro 10.0.2 (SAS Institute, Cary, NC) and R version 3.0.1 (Vienna, Austria).

Ganapathi A.M., Englum B.R., Schechter M.A., Vavalle J.P., Harrison J.K., McCann R.L, & Hughes C. (2014). Role of cardiac evaluation before thoracic endovascular aortic repair. Journal of vascular surgery, 60(5), 1196-1203.

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