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Book Synopsis
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.

Advanced Statistics For Health Research

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    RRP £135.00 – you save £13.50 (10%)

    Order before 4pm today for delivery by Thu 11 Jun 2026.

    A Hardback by Richard J Butler, Matthew J Butler, Barbara L Wilson

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      View other formats and editions of Advanced Statistics For Health Research by Richard J Butler

      Publisher: World Scientific Publishing Co Pte Ltd
      Publication Date: 12/05/2023
      ISBN13: 9789811262302, 978-9811262302
      ISBN10: 9811262306
      Also in:
      Business, Finance & Law Health economics

      Description

      Book Synopsis
      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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