## Recently Published

##### Get started with R (Uppsala), part 2

Slides for the workshop Get started with R, held at Uppsala University Hospital

##### Lab: the Normal distribution

Exercises and solutions for the lab on the normal distribution in the course Basics Statistics (Karolinska Institutet)

##### Lab: discrete random variables

Exercises and solutions for the lab on discrete random variables in the course Basics Statistics (Karolinska Institutet)

##### Ted talks analyses

Rmarkdown to reproduce the notebook used during R Hackathon 2017 (Stockholm) organized by the SRUG meetup.

##### Get started with R (Uppsala)

Slides for the workshop Get started with R, held at Uppsala University Hospital

##### Meta-analysis of epidemiologic dose-response data

R code to reproduce the results of Berlin JA, Longnecker MP, Greenland S. Meta-analysis of epidemiologic dose-response data. Epidemiology. 1993 May 1:218-28.

##### Visualizing multivariate logistic regression

An rmarkdown to graphically present results from multivariate logistic regression, with examples on modelling interaction and non-linear associations.

##### fluoride

fluoride presentation

##### Get started with R

Slides for the workshop Get started with R, held at the deparment of Public Health Sciences - Karolinska Institutet.

##### Shiny: interactive web applications with R

A very short introduction to shiny.

##### Meta-analysis of epidemiological dose-response data

I will give a general overview of the methodology for meta-analysis of epidemiological dose-response data. After presenting the relevance and background, I will present the main aspect of the methodology. I will also show how to perform similar analyses using an interactive web application.

##### Meta-Analysis of Epidemiological Dose-Response Studies with the dosresmeta R package

Slides for the presentation at the useR! 2016 conference. The aim was to give a short introduction to the methodological framework of dose-response meta-analysis (structure of aggregated data, covariance of correlated outcomes, estimation and pooling of individual curves). I described how to test hypothesis and how to quantify statistical heterogeneity. In addition, tools to flexibly model the quantitative exposure were presented. I illustrated modelling techniques and presentation of (graphical and tabular) results using the dosresmeta R package.

##### An Introduction to Reproducible Research in R

Reproducible research is a fundamental aspect of research. This presentation is a general introduction on how to conduct reproducible research in R and Rstudio, with a specific focus on literate programming. Practical example analyses were interactively proposed after that.

##### QinLiu

R code to reproduce the results of Qin Liu et a, (2009), A Two-Stage Hierarchical Regression Model for Meta-Analysis of Epidemiologic Nonlinear Dose–Response Data Dose–Response Data.

##### glst

R code to reproduce the results of Orsini et a, (2006), Generalized Least Squares for Trend Estimation of Summarized Dose–Response Data.

##### Estimating and pooling dose-response curves from published data

It describes the general framework in estimating a pooled dose-response curve from published available data. I will present the results obtained using the “dosresmeta” package of two practical examples: the first one consider a binary outcome, while the latter investigates a continuous one.

##### Dose-Response Meta-Analysis: an Overview

This is a general overview of the dose-response meta-analysis methodology. After presenting the background, the advantages and the possible limitations of the technique, I will show how to conduct a dose-response meta-analysis based on a real data using the “dosresmeta” R package.