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Advanced Statistics for Health Research provides a rigorous
geometric understanding of models used in the analysis of health
data, including linear and non-linear regression models, and
supervised machine learning models. Models drawn from the health
literature include: ordinary least squares, two-stage least
squares, probits, logits, Cox regressions, duration modeling,
quantile regression and random forest regression. Causal inference
techniques from the health literature are presented including
randomization, matching and propensity score matching,
differences-in-differences, instrumental variables, regression
discontinuity, and fixed effects analysis. Codes for the respective
statistical techniques presented are given for STATA, SAS and R.
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