Welcome to my page!
I’m Andreas, a passionate data scientist using R.
I’m also a bonsai enthusiast 🌱 and latin dancer 💃
Andreas Kitsche
Data Scientist, Bonsai Enthusiast & Latin Dancer
About me
Hey folks, this web site provides some information about my activities in data science using R.
I have a Masters degree in Plant Biotechnology with focus on Biostatistics. I earned a PhD at the Institute of Biostatistics at the Leibniz University of Hannover. During my studies I started learning R and wrote some small packages stored on my GitHub page. This blog will showcase some of the analyses I have been doing with different data sets.
In addition I propagate and cultivate Bonsai trees for several years. Within this blog I want to document the developement of some of my favourite trees.
Interests
- Data Visualization
- Data Science
- Machine Learning
- Biostatistics
Education
PhD in Biostatistics, 2014
Leibniz Unversität Hannover
MSc in Plant Biotechnology, 2010
Leibniz Unversität Hannover
BSc in Plant Biotechnology, 2008
Leibniz Unversität Hannover
Skills
R Programming
Base R + tidyverse
Data Visualization
ggplot + plotly
Dashboarding
shiny + PowerBI
Biostatistics
Experimental Design + Multiple Comparison Procedures
Python
learning
Machine Learning
tidymodels
Experience
Data Scientist
Continental AG
Responsibilities include:
- Cross-divisional consulting for digitalization use cases within finance and controlling, law, and tax departments
- Programming data analysis pipelines, e.g. time-series forecasts, OCR (object character recognition) modules, and text mining applications
- Implementing interactive reporting dashboards using R shiny and PowerBI
- Creating and leading trainings in Introduction to R and RStudio
Data analyst and tool developer
KWS Saat SE
Responsibilities include:
- Programmed data analysis applications for the evaluation of high-dimensional biological data
- Developed graphical user interfaces with R shiny for daily business applications
- Statistical consultation for a variety of data analysis projects, e.g.:
- Sample size and power calculations for the design of experiments
- Genome wide association studies
- Visualization and analysis of weather data
- Trained scientists in data visualization and analysis using R
Research associate
Biostatistics Department
Responsibilities include:
- Co-worker at the DFG project ‘Simultaneous confidence intervals for non-parametric effect sizes in factorial designs’
- Methodological research in multiple comparison procedures
- Taught intermediate and advanced-level students in statistical analysis using R
- Provided statistical guidance and help to students and researchers at the Faculty of Natural Sciences
Posts
Tidy Tuesday 52 2020
Hey folks,
this is the first of a series of blog post where I want to participate on the tidytuesday weekly social data project in R.
The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions.
Bonsai Impression Spring 2018
During the last weeks I did some work including root cutting, potting and styling on a couple of my pre-bonsai trees. My collection consists of a stock of approximately 100 trees ranging from European oak (Quercus robur), small-leaved lime (Tilia cordata), pin oak (Quercus palustris), horse-chestnut (Aesculus hippocastanum), European beech (Fagus sylvatica), Ginkgo biloba, Common hazel (Corylus avellana), Creeping pine (Pinus mugo), Scots pine (Pinus sylvestris), English Yew (Taxus Baccata), … .
Projects
Power and sample size computations for the simultaneous assessment of the consistency of treatment effects
In this study, we address the problem of calculating the power and sample size in the simultaneous assessment of the consistency of treatment effects. The method is based on a general formulation of inconsistency as a treatment-by-subset interaction, while the interaction term is defined as the ratio of the treatment effects. This approach allows the interpretation of inconsistency as a relative change in the treatment effects. In addition, conclusions are given based on an appropriately defined consistency margin. The methodology is applicable to trials with continuous and binary endpoints. Two power definitions arising in multiple testing, namely the all-pair (complete) power and any-pair (minimal) power, are considered. The focus of this study is the assessment of consistency in multi-regional clinical trials, but the proposed methodology is in general applicable to the assessment of any treatment-by-subset interactions, including the detection of qualitative interactions. Several examples from clinical trials are provided to illustrate the application of the proposed procedure. An R add-on package poco was developed for the analysis, which provides the functionality presented in this study.
Alternative effect size measurements corresponding to the two sample t-test
If interest is in comparing the means of two (normally distributed) samples it is common practise to perform a two-sample t-test and report the corresponding p-value. Nevertheless, it has been widely criticized that the p-value does not provide a measure for the magnitude of the mean effect (e.g., Browne (2010)). This report provides an overview of existing alternatives recently published in the scientific literature that provide a more meaningful measurement of the effect size. Browne (2010) introduced closed form equations to translate a significant t-test p-value and sample size into the probability of one treatment being more successful than another on a per individual basis. This term was afterwards denoted as win probability by Hayter (2013) and he demonstrated the interpretation as ‘what would happen if a single future observation were to be taken from either of the two treatments, with attention being directed towards which treatment would win by providing the better value.’ In addition Hayter (2013) introduced the corresponding confidence interval as well as the odds of X being greater than Y. He further introduced the transformation into Cohens effect size and the corresponding confidence intervals.