Titlebook: Statistics for Health Data Science; An Organic Approach Ruth Etzioni,Micha Mandel,Roman Gulati Textbook 2020 The Author(s), under exclusive

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Regression Analysis,itative framework that is most commonly used to establish whether outcomes are associated with individual, community, or environmental characteristics. It quantifies the strength of relationships in conceptual models of health care utilization and costs. It provides a framework for explaining why so

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Binary and Categorical Outcomes,ends on covariates. The most commonly used model for binary outcomes is logistic regression. We show that logistic regression coefficients are directly interpretable as relative odds of a positive outcome corresponding to changes in covariate values and discuss common misinterpretations of these mod

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Health Care Costs,uce several regression models for right-skewed expenditure outcomes. Standard linear regression modeling of the logarithm of the outcome is a classical solution to the problem of right-skewed outcome data. We introduce the lognormal distribution, which this approach implicitly assumes, and discuss t

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Causal Inference,outcome. Causal effects are ideally investigated via randomized controlled trials, but such studies cannot address all health care questions of interest. Consequently, observational data sources are increasingly being used to answer causal questions. This chapter reviews the challenges of using obse

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Statistics and Health Data,t. This chapter thus creates a roadmap for forthcoming chapters while standing alone as an introduction to the key themes of this text: health data resources and their features, the research question and its role in analysis, and the mindset of organic statistics.