Set of slides presented at the ProBio International Investigator’s Meeting in 2022 and at a meb biostat seminar

R code to reproduce the results of Orsini et a, (2021), Weighted mixed-effects dose-response models for tables of correlated contrasts. Stata Journal.

Updated version of https://rpubs.com/alecri/survR

Based on "Regression models for dependent data" by Nicola Orsini

R Workshop for students in the DIS - Study abroad in Scandinavia program

Based on Nicola's material.

This page is a supplementary material for the lecture Statistical Methods for Meta-Analysis, in the course Systematic Review and Meta-Analysis.

Supplementary R code for Categorical Data Analysis - Part I in the course Biostatistics II in the master Metodologia Epidemilogica e Bostatistica per la Ricerca Clinica

Slides for useR! 2019, Toulouse - France: Implementation and analysis design of an adaptive-outcome trial in R

Slides for the biostat meeting, 190516

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

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

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

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

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

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.

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

fluoride presentation

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

A very short introduction to shiny.

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.

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.

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.

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.

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

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.