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Lab products found in correlation

Spss ver 23, by IBM (1 mentions) Rstudio, by R Project for Statistical Computing (1 mentions) Prism version 9, by GraphPad (1 mentions) Stata statistical software version 15, by StataCorp (1 mentions) Rstudio statistical software, by Posit (1 mentions) Prism 6, by GraphPad (1 mentions)

15 protocols using r statistical software version

Psychiatric Profiles in Salla Disease

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The study was conducted as a retrospective register-based study. We identified 24 people diagnosed with Salla disease (SD) from the registry of Oulu University Hospital Department of Clinical Genetics (from 1982 to 2015). The patient charts of all identified individuals were systematically reviewed for psychiatric symptoms and medications. Patient identification was based on diagnostic codes according to the International Classification of Diseases (ICD-9 and ICD-10). Psychiatric symptoms were first identified by identifying patients with patient records from a psychiatric clinic or described psychiatric medication. The patient charts of these individuals were examined more thoroughly by two independent authors to identify symptoms and clinical phenotypes. Statistical analysis was conducted using R Statistical Software (version 1.3–0; R Foundation for Statistical Computing, Vienna, Austria).
The median age of the patients was 21 years, ranging from 6 to 63. Children or young adults under 20 years old represented 41.7% (10/24) of the cohort. Those aged between 20 and 40 comprised 37.5% (9/24). Only five individuals (5/24, 20.8%) were aged 40 and older. A majority of the patients, 75% (18/24), were male, and 25% (6/24) were female.

Aulanko I., Rahikkala E, & Moilanen J. (2022). Psychiatric symptoms in Salla disease. European Child & Adolescent Psychiatry, 32(10), 2043-2047.

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Longitudinal Telomere Length Changes

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Data were analyzed using statistical software (SPSS ver 23.0; IBM Corp., Armonk, NY, USA) and R statistical software (version 3.3.2; R Foundation, Vienna, Austria). Changes in LTL during the first year of life were assessed by the paired t tests, and each group's yearly change was assessed using the Wilcoxon signed rank test. Differences in cord blood and 1-year-old peripheral blood LTLs were assessed using the Mann-Whitney U test, and the absolute amount of attrition in LTLs across all 4 groups was evaluated using the Kruskal-Wallis test followed by post hoc Dunn's test with further P value adjustment by the Benjamini-Hochberg false discovery rate method.17 The proportion of later AD development according to the stress exposure and telomere length was assessed by the Fischer's exact test. The cutoff for shorter LTL was defined by the median of total subjects enrolled in this study. Differences were considered statistically significant when the P value was less than 0.05.

Suh D.I., Kang M.J., Park Y.M., Lee J.K., Lee S.Y., Sheen Y.H., Kim K.W., Ahn K., Won H.S., Lee M.Y., Choi S.J., Kwon J.Y., Park H.J., Jun J.K., Hong S.J, & Koh Y.Y. (2019). Leukocyte Telomere Length Reflects Prenatal Stress Exposure, But Does Not Predict Atopic Dermatitis Development at 1 Year. Allergy, Asthma & Immunology Research, 11(3), 357-366.

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Differentiating Alzheimer's and Lewy Body Dementia

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Characteristics of the subjects were summarized using means and standard deviations, or counts and percentages. ADem and DLB groups were compared using Student t-tests for continuous variables and chi-squared tests for categorical variables. Due to skewness, the PiB-PET was analyzed with a log transformation. Logistic regressions with one, two, or three modalities as predictors of ADem vs. DLB were run and summarized using odds ratios (ORs) and associated standard errors, p-values, and C-statistics. Scatter plots and Pearson correlations were used for describing associations between continuous imaging biomarkers. P-values of <0.05 were considered as statistically significant for the analyses. Statistical analyses were performed in SAS (version 9.4; SAS Institute, Inc.) and R statistical software, version 3.4.2 (RFoundation).

Miyagawa T., Przybelski S.A., Maltais D., Min H.K., Jordan L., Lesnick T.G., Chen Q., Graff-Radford J., Jones D., Savica R., Knopman D., Petersen R., Kremers W.K., Forsberg L.K., Fields J.A., Ferman T.J., Allen L., Parisi J., Reichard R.R., Murray M., Dickson D., Boeve B.F., Kantarci K, & Lowe V.J. (2020). The Value of Multimodal Imaging with 123I-FP-CIT SPECT in Differential Diagnosis of Dementia with Lewy Bodies and Alzheimer’s Disease Dementia. Neurobiology of aging, 99, 11-18.

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Statistical Analysis of Research Data

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Statistical analysis was performed using R Statistical Software, version 4.1.2 (The R Foundation, Austria) and the Statistical Product and Service Solutions software, version 20.0 (SPSS, USA). For categorical variables, descriptive analysis was performed using the frequency and percentage. Numerical variables were presented as the number of cases, mean ± standard deviation and 95% confidence interval (CI). The Shapiro–Wilk test was performed to evaluate normal distributions. Depending on whether the variable had a normal distribution, the comparison between means was carried out using either a paired Student's t-test or Wilcoxon signed-rank test. McNemar's test was used for the statistical analysis of qualitative variables. Values of P < 0.05 were considered statistically significant.

Bosch A., Albero S., Castillo J.C., Ten J., Guerrero J., Ortiz J.A., Bernabeu A, & Bernabeu R. (2023). Does mRNA COVID-19 vaccination in oocyte donors impact ovarian stimulation parameters or IVF outcomes for recipients?. Reproductive Biomedicine Online, 46(4), 697-704.

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Relative Age Effect in Soccer and Futsal

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The observed birthdate distributions of all players were calculated for each quarter and semester of the year and presented as absolute and relative frequencies, for each age group, gender, and certification level in both soccer and futsal. Chi-square goodness-of-fit tests were used to compare the observed and expected birthdate distributions across quartiles. As Chi-squared statistics cannot reveal the magnitude and direction of an existing relationship, we additionally calculated the odds ratios (OR) and 95% CI for the quartiles (Q1, Q2, and Q3) and semester (S1), with the youngest group as reference (i.e., Q4 and S2). We also applied the Benjamini and Hochberg (1995) procedure for multiple testing correction, and reported the false discovery rate (FDR) adjusted p-values. FDR-adjusted p-values lower than 0.05 were assumed to be statistically significant. We assumed the existence of a RAE if the 95% CI range did not include a value ≤1, and interpreted an OR 1.22 ≤ OR < 1.86 as small, 1.86 ≤ OR <3.00 as medium, and OR ≥ 3.00 as large (Olivier and Bell, 2013 (link)). All statistical analyses were conducted using R statistical software (version 4.0.2, R Foundation for Statistical Computing, Vienna, Austria).

Figueiredo P., Seabra A., Brito M., Galvão M, & Brito J. (2021). Are Soccer and Futsal Affected by the Relative Age Effect? The Portuguese Football Association Case. Frontiers in Psychology, 12, 679476.

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Statistical Analysis of Experimental Data

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All statistical analyses were conducted with R statistical software, version 3.6.2 (2019-12-12) (The R Foundation for Statistical Computing) and R studio Version 1.2.5033. P-values were considered significant when meeting a two-tailed α threshold of 0.05. Correction for multiple comparisons was performed using Bonferroni, i.e., multiplying the p-value by the number of tests.

Delmotte K., Schaeverbeke J., Poesen K, & Vandenberghe R. (2021). Prognostic value of amyloid/tau/neurodegeneration (ATN) classification based on diagnostic cerebrospinal fluid samples for Alzheimer’s disease. Alzheimer's Research & Therapy, 13, 84.

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Taiwanese Alzheimer's Disease Registry

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All data collected in the present study were compared to the first AD registration in Taiwan.^{6 (link)} All statistical analyses were performed using R Statistical Software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria).¹⁴ All statistical tests were two-tailed and an alpha value of .05 was taken to indicate significance. Descriptive statistical analyses were conducted for all continuous variables with mean ± standard deviation. Differences between categorical data were calculated using Pearson’s chi-square test or Fisher’s exact test. Numerical data were analyzed using Student’s t-test or the Kruskal–Wallis rank-sum test.

Jhang K.M., Wang W.F., Hsu K.N., Huang S.C., Yang S.H., Huang L.C, & Yang Y.H. (2023). A 12-Year Comparison of Alzheimer’s Dementia Patients With Their Informants in Taiwan. American Journal of Alzheimer's Disease and Other Dementias, 38, 15333175231218089.

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Statistical Analysis of Osteoarthritis Markers

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All statistical analyses were performed using GraphPad PRISM version 9 (GraphPad, San Diego, CA, USA) and R Statistical Software (version 4.0.5; R Foundation for Statistical Computing, Vienna, Austria). Comparisons of OA grades, Ki67 positive nuclei count averages, and Luminex biomarker assay results were performed using the Wilcoxon rank sum test. Data are considered significant at a p-value of ≤0.05, calculated using the R and GraphPad PRISM version 9 software.

Aleksiuk V., Baleisis J., Kirdaite G., Uzieliene I., Denkovskij J., Bernotas P., Ivaskiene T., Mobasheri A, & Bernotiene E. (2023). Evaluation of Cartilage Integrity Following Administration of Oral and Intraarticular Nifedipine in a Murine Model of Osteoarthritis. Biomedicines, 11(9), 2443.

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Mental Health Status and Associated Factors

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Categorical variables were presented as numbers and percentages and compared using a Pearson χ² test or Fisher exact test between poor mental health (GHQ score ≥3) and good mental health (GHQ score <3) groups. P values for trends were calculated using the mental health variable as a binary categorical variable (1 = GHQ score ≥3, 0 = GHQ score <3) in the Pearson χ² trend test and regression model trend test. P values were 2-sided, and statistical significance was set at P < .05.
To further examine potential factors associated with risk or protection for mental health status, a multivariate logistic regression analysis was used, and odds ratios (ORs) and 95% CIs were calculated. In this model, we included potential variables and set the significance level at P < .05. All analyses were performed with Stata statistical software version 15.0 (StataCorp) and R statistical software version 4.0.3 with forestplot package (R Project for Statistical Computing). To account for the clustering of participants, we weighted by geographic area of residence and stratified by sex. Data were analyzed from April 5 to July 20, 2020.

Qin Z., Shi L., Xue Y., Lin H., Zhang J., Liang P., Lu Z., Wu M., Chen Y., Zheng X., Qian Y., Ouyang P., Zhang R., Yi X, & Zhang C. (2021). Prevalence and Risk Factors Associated With Self-reported Psychological Distress Among Children and Adolescents During the COVID-19 Pandemic in China. JAMA Network Open, 4(1), e2035487.

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Molecular Markers Impact on Glioma Survival

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All statistical analyses were performed using R Statistical Software, version 4.0.3 (www.r-project.org), accessed date 9 November 2020. Pearson’s chi-square and Fisher’s exact tests were used for testing associations between a type of therapy and molecular markers value levels. Overall survival (OS) and progress-free survival were estimated using the Kaplan-Meier method (presented as medians in tables of results). The OS range extended from the day of the first surgery until death or last follow-up. The PFS range extended from the day of the first surgery until MRI tumor progression. The influence of each molecular marker on OS, resp. PFS was investigated using Cox proportional hazard models, both with one factor (a univariate model—Table S1, Figure 2 and Figure 3) as well as adjusted for the major clinical prognostic factors (a multivariate model—Table S2), i.e., a categorized age at diagnosis (≤55 vs. >55 years) and a categorized Karnofsky score (KS; <80 vs. ≥80), in therapy-subgroups of patients. The influence of each factor on OS, resp. PFS across groups of all patients was investigated by the Cox proportional hazard model (one for each marker) stratified by therapy and adjusted for categorized age and categorized Karnofsky score (Table S3).

Kalita O., Sporikova Z., Hajduch M., Megova Houdova M., Slavkovsky R., Hrabalek L., Halaj M., Klementova Y., Dolezel M., Drabek J., Tuckova L., Ehrmann J J.r., Vrbkova J., Trojanec R, & Vaverka M. (2021). The Influence of Gene Aberrations on Survival in Resected IDH Wildtype Glioblastoma Patients: A Single-Institution Study. Current Oncology, 28(2), 1280-1293.

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