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Advanced Statistics for Health Research

About Advanced Statistics for Health Research

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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  • Language:
  • English
  • ISBN:
  • 9789811262302
  • Binding:
  • Hardback
  • Pages:
  • 396
  • Published:
  • April 10, 2023
  • Dimensions:
  • 157x26x235 mm.
  • Weight:
  • 724 g.
Delivery: 2-3 weeks
Expected delivery: December 12, 2024

Description of Advanced Statistics for Health Research

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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