R language and environment for statistical computing

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R is an open-source programming language and software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, and is highly extensible. R is widely used in academia and industry for data analysis, visualization, and statistical modeling.

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Spss version 24, by IBM (1 mentions)

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6 protocols using r language and environment for statistical computing

Evaluation of Healthcare Training Curriculum

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We aligned our evaluations with Kirkpatrick's levels of training evaluation.²⁷ Pre-/posttest questions (Kirkpatrick level 2: learning) were based on learning objectives for each session. We created brief clinical vignettes for the four to eight diagnoses presented in each learning session, with corresponding multiple-choice questions related to the diagnosis, workup, or management of each diagnosis. Questions were not based on previously published material but created based on best practices for writing vignette-based multiple-choice questions.²⁸ Learner and instructor surveys (Kirkpatrick level 1: reaction) asked respondents to indicate their level of agreement with statements on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
We obtained institutional review board approval (CA, OR) or exemption (WA, OH) prior to the project and obtained consent for pre-/posttests and learner/instructor surveys. Learners received the curriculum regardless of whether they had consented to complete the surveys and participate in the project. We analyzed the paired pre-/posttests using the nonparametric Wilcoxon test and the learner/instructor evaluations using descriptive statistics. We performed statistical analyses using the R language and environment for statistical computing (R Foundation).

Madduri G.B., Torwekar E.L., Demirel S., Durham M., Hauff K.I., Kaul R., Nichols T., Ravid N.L., Shaner M.A, & Rassbach C.E. (2024). CRISP: An Inpatient Pediatric Curriculum for Family Medicine Residents Using Clinical Reasoning and Illness Scripts. MedEdPORTAL : the Journal of Teaching and Learning Resources, 20, 11393.

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Comprehensive Statistical Analysis Protocol

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Statistical analyses were performed on the entire sample population, if not differently specified. Data collected have been summarized by descriptive statistics such as mean and standard deviation or median with its interquartile range (IQR) for continuously distributed variables, otherwise, rates and relative frequencies have been reported for categorical distributions. Fisher exact test or Chi‐squared test have been adopted to evaluate associations between groups and nominal variables were appropriate. The Mann–Whitney test has been applied, alternatively, to assess differences between medians in non‐normal distributions. Survival analysis has been performed by means of the Kaplan–Meier estimator and its algorithm for survival curve generation. Subgroup analyses were conducted whenever appropriate. Log‐rank test was used to compare survivals curves probabilities, while semiparametric Cox regression analysis has been adopted to estimate Hazard Ratios (HR). All the analyses were conducted using R language and environment for statistical computing (R Foundation for Statistical Computing). 0.05 was taken as the cut‐off for two‐sided p‐values statistical significance. All confidence intervals (CI) were reported as 95% CI.

Marconi G., Candoni A., Di Nicola R., Sartor C., Parisi S., Abbenante M., Nanni J., Cristiano G., Zannoni L., Lazzarotto D., Giannini B., Baldazzi C., Bandini L., Ottaviani E., Testoni N., Bezzi C.D., Abd‐alatif R., Ciotti G., Fanin R., Martinelli G., Paolini S., Ricci P., Cavo M., Papayannidis C, & Curti A. (2023). The baseline comorbidity burden affects survival in elderly patients with acute myeloid leukemia receiving hypomethylating agents: Results from a multicentric clinical study. Cancer Medicine, 12(10), 11838-11848.

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Genomic Complexity in 1q&13 Cancers

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All the analyses were conducted using R language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria). The analysis was performed with a significance level of at least 0.05 and all variables objected of inference were reported together with their 95% confidence intervals (CI). The genomic complexity of 1q&13 classification was explored by comparing characteristics between groups with non-parametric methods such as Kruskal-Wallis’s test on the medians (or the parametric t-test on means). For the parametric comparisons, the Pearson’s test was employed. PFS was measured in months, from the start of therapy to the event of first progression of the disease or death. OS considered death as outcome/event and was measured from the same landmark. Univariate survival analysis on both PFS and OS were performed by the Kaplan-Meier method, as for drawing the survival curves. Semi-parametric Cox regression analysis was adopted to estimate hazard ratios (HR) with an 95% CI between predefined possible prognostic groups. Multivariate analysis was performed again by Cox regression analysis to identify the abnormalities independently affecting the prognosis with their HR and 95% CI, stratifying for sub-group variable in BO dataset, in order to adjust the HR.

Terragna C., Poletti A., Solli V., Martello M., Zamagni E., Pantani L., Borsi E., Vigliotta I., Mazzocchetti G., Armuzzi S., Taurisano B., Testoni N., Marzocchi G., Kanapari A., Pistis I., Tacchetti P., Mancuso K., Rocchi S., Rizzello I, & Cavo M. (2024). Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features. Nature Communications, 15, 1551.

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Diagnostic Accuracy of Expert Reviewers

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Data analysis was undertaken using SPSS version 24 (IBM Corporation, Texas, USA) and the open-source R language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2018). The study design did not permit the ARPs to attain superior diagnostic accuracy to the reviewers so a one-tailed hypothesis test was used. A p value of < 0.05 is considered statistically significant. The degree of agreement between the two expert reviewers interpretation of the images is analysed using the Cohen Kappa statistic.

Ronaldson J., Moultrie C.E., Corfield A.R, & McElhinney E. (2020). Can non-physician advanced retrieval practitioners (ARP) acquire and interpret diagnostic views of the lungs with sufficient quality to aid in the diagnosis of pneumothorax in the pre-hospital and retrieval environment?. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28, 102.

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Robust Biomarker Discovery for Drug Resistance

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The sample size was chosen according to the availability of heavily treated OC patients who agreed to participate in the study. Despite the low numerosity of the sample to statistically validate a single biomarker (i.e., this study is intended to provide only a first step into drug resistance circulating markers); the statistical approach adopted during MS analysis and metadata integration have been shown to provide robust outcomes [21 (link)]. All the serum samples were treated in duplicate and analyzed in triplicate in LC-MS to eliminate both biochemical and instrumental biases. Statistical analyses were conducted using R language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria). A type I error of 5% was taken as the limit for two-sided p-value statistical significance and all confidence intervals (CI) were reported as 95% CI. Differential protein abundance intended as fold change (FC) over time between patient response stratification was analyzed by mean of a paired t-test. A volcano plot was adopted to show the results of the FC analysis and to highlight which proteins had a statistically significant behavior change. Response to treatment contribution over time and its interaction with the different timepoints was investigated by mean of mixed-effect analysis of variance (ANOVA).

D’Arca D., Severi L., Ferrari S., Dozza L., Marverti G., Magni F., Chinello C., Pagani L., Tagliazucchi L., Villani M., d’Addese G., Piga I., Conteduca V., Rossi L., Gurioli G., De Giorgi U., Losi L, & Costi M.P. (2023). Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma. Cancers, 15(2), 412.

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Genome-Wide Association Analysis of Traits

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Association testing was performed using PLINK v1.9 (http://pngu.mgh.harvard.edu/purcell/plink/) [24 (link)]. Linear regression assuming an additive genotypic model was used for TBS. Age, sex, and body mass index (BMI) were used as covariates. The results of the discovery cohort can be downloaded from the following webpage: http://bri.snuh.org/bench/_/notice/5250/view.do. In the validation cohort, only variants that passed the significance threshold (P<5.0×10⁻⁸) or false discovery rate (FDR) (P<0.05) in the discovery cohort were analyzed [25 (link),26 (link)]. The significance threshold for the validation cohort follow-up was P<0.05. Genome-wide significance was considered as P<5.0×10⁻⁸. Analyses were performed separately for each sex to identify sex-specific effects.
All analyses were performed with the R language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria). We used Plink v1.9 [24 (link)] and data from the 1000 Genome Project Phase 3 [27 (link)] to identify proxy SNPs (r²<0.7). Each GWAS locus was defined based on the positions of the left and rightmost proxy SNPs. All SNP and gene locations were relative to the hg38 genome assembly. We also included the closest gene up- and down-stream.

Kong S.H., Yoon J.W., Kim J.H., Park J., Choi J., Lee J.H., Hong A.R., Cho N.H, & Shin C.S. (2020). Identification of Novel Genetic Variants Related to Trabecular Bone Score in Community-Dwelling Older Adults. Endocrinology and Metabolism, 35(4), 801-810.

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