COMH7266

Longitudinal Analysis and Causal Inference

17-28 July 2017

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Instructors
Michael G Hudgens PhD	Tobias Chirwa, PhD
Professor of Biostatistics	Associate Professor of Bios and Epid
University of North Carolina	Head, Witwatersrand SPH
mhudgens@bios.unc.edu	Tobias.Chirwa@wits.ac.za

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HW assignment: Due 24 July

Lab assignments for 27-28 July

Final assignment: Due 11 Aug

Causal Inference by Hernan and Robins

Lecture Notes Introduction 1 A definition of causal effect 2 Randomized experiments 3 Observational studies 4 Effect modification 5 Interaction 6 DAGs 7 Confounding 8 Selection bias 9 Measurement bias 10 Random variability 11 Why model? 12 Marginal structural models 13 Parametric g-formula 14 Structural nested models 15 Outcome reg. & p-scores Time varying exposures

Data and Code NHEFS data codebook sas7bdat xls csv 11 Introduction R SAS Stata 12 Marg. structural models R SAS Stata 13 Parametric g-formula R SAS Stata 14 Structural nested models R SAS Stata 15 Outcome reg. & p-scores R SAS Stata

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