Advanced Statistics for Health Research

健康研究*统计

医学生物化学

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出  版 社
出版时间
2023年04月27日
装      帧
精装
ISBN
9789811262302
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页      码
300 pp
语      种
英文
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图书简介
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.Key Features·Simple geometric figures make causal inference techniques more intuitive·Easy to understand empirical examples are drawn from a broad collection of the health research literature·SAS, Stata, and R codes make implementing regression models straightforward
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