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INTERFACULTY CENTER DATA PROCESSING AND STATISTICS

HUMANITIES - LIFE SCIENCES & MEDICINE
icdsminsmiple

resources

 

workshops, presentations, technical notes, shiny apps, ... all made available to support our research community

Resources

Workshops

= Sample Size Calculation with GPower =
Introduction of the necessary concepts (power, effect size and type I/II errors), with small exercises to promote statistical reasoning. After a focus on the comparison of 2 independent groups, several extensions are highlighted. [slides] [pdf]

= Data Manipulation in R, tidyverse =
Introduction of the tidyverse packages dplyr and friends for intuitive and flexible data management in R. [webpage] [pdf]

= Visualization in R, tidyverse =
Introduction of the tidyverse package ggplot2 for intuitive and flexible visualization in R.  [webpage] [pdf]

 

Presentations

= R Primer =
Short introduction on the essentials of the R statistical programming tool. [webpage] [pdf]

= Data Representation =
Brief introduction to help structure data in preparation of statistical analyses; highlighting errors, inconveniences, common problems and solutions. [webpage] [pdf]

= Methodological & Statistical issues in research proposals =
Info session for experienced researchers to help highlight relevant aspects of the research aim and design that are part of a research proposal. [webpage] [pdf]

 

Technical Notes

= Corona hospitalisation SIR model =
Technical note on the analytical approximation of the popular SIR-equations which after reparametrization allow for straightforward parameter estimation and prediction of hospitalisation sustainability based on limited time series. Extensions that add flexibility are highlighted. [pdf
Please reference: Barbé, K.,  Blotwijk, S., & Cools, W. (2020) Data-driven epidemiological model to monitor the sustainability of hospital care, VUB Covid19 Technical Note No. ICDS043020.

Tools

Shiny Apps

all Shiny Apps are deployed on our own shiny server and are available online.
alternatively, the Apps can be run locally using the R package `shiny` and the code on GitHub

sample size simulation

interim analysis based on alpha spending for t-test and one-way ANOVA 
run locally with runGitHub('simAlphaSpending','ICDS-VubUZ')

effect size calculation

eta squared effect size specification for two-way ANOVA and repeated measures ANOVA 
run locally with runGitHub('effectSizes','ICDS-VubUZ')

didactical tool

sample sizes for one-sided t-test
run locally with runGitHub('shinyT','ICDS-VubUZ')

Thank you for your information, about the good, the bad and the ugly.