RECAP workshop: Statistical Methods for combined data sets

Overview

This site contains materials for the RECAP workshop Statistical Methods for combined data sets: Theory, techniques and tools on September 4-5, 2017 in Leiden.

Motivation

Combining data sets generates blocks of missing data. However, most data analysis procedures are designed for complete data, and many will fail if the data contain missing values. Most procedures will therefore simply ignore any incomplete rows in the data, or revert to ad-hoc procedures like replacing missing values with some sort of "best value". However, such fixes are based on assumptions, and may introduce serious biases when these assumptions are not met.

This workshop revises practical issues with combining data, and explores the use of multiple imputation as a principled solution.

Contents

The workshop consist of 6 sessions, each of which comprises a lecture followed by a computer practical using R:

1. Session I: Combining Datasets & Missing Data
2. Session II: Multiple imputation using mice
3. Session III: Creating Comparable Variables
4. Session IV: Developmental milestones
5. Session V: Loss-to-Follow-Up
6. Session VI: Multilevel Analysis

How to prepare

Please remember to bring your own laptop computer and make sure that you have write-access to that machine (some corporate computers do not allow write access) or that you have the following software and packages pre-installed.

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1. Download and install the latest version of R from the R-Project website
2. Download and install the most recent version of RStudio Desktop (Free License) from RStudio's website. This is not necessary, per se, but it is highly recommended as RStudio delivers a tremendous improvement to the user experience of base R.
3. Install the packages markdown, mice, lme4, dplyr, plyr and mlmRev.

• You can install packages from within RStudio by navigating to Tools > Install Packages in the upper menu and entering the names of the package into the Packages field. Make sure that the button Install dependencies is selected. Once done, click Install and you're all set.
• Or, from within R or RStudio, copy, paste and enter the following code in the console window (by default the top-right window in RStudio / the only window in R):

install.packages(c("markdown", "mice", "lme4", "dplyr", "plyr", "mlmRev"))

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Workshop materials

1. Lectures
2. Practical I
3. Practical II
4. Practical III
5. Practical IV
6. Practical V
7. Practical VI
8. Practical I .Rmd
9. Practical II .Rmd
10. Practical III .Rmd
11. Practical IV .Rmd
12. Practical V .Rmd
13. Practical V data_July2017.txt
14. Practical VI .Rmd
15. Unifying perspective