Statistical Analysis with Missing Data using R

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Jonathan Bartlett (The Stats Geek)
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USD 99
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Jonathan Bartlett
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This online course covers the key concepts and methods for handling missing data in statistical analyses using R, including:

- Rubin's missing data mechanism assumptions, how to investigate them using the observed data, using directed acyclic graphs (DAGs) to think about them

- problems with ad-hoc methods like adding a missing category or last observation carried forward - when is complete case analysis unbiased (more often than many think!)

- multiple imputation, via the chained equations method in the MICE package, and practicalities such as how many imputations to use and how to choose which variables to include

- advanced imputation topics, including imputation of covariates with survival and competing risks outcomes, non-linear effects and interactions, and incorporating survey design

Concepts and methods are introduced through mini-lecture videos. How to apply the methods in R is demonstrated through videos, with datasets and R scripts downloadable. Multiple choice quizzes are used throughout to test and develop understanding. Click the Free Preview button at https://thestatsgeek.thinkific... to sign up and view some of the lessons for free.

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