دانلود کتاب Statistics for Health Data Science: An Organic Approach

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Preface Contents List of Figures List of Tables 1 Statistics and Health Data 1.1 Introduction 1.2 Statistics and Organic Statistics 1.3 Statistical Methods and Models 1.4 Health Care Data 1.4.1 Medical Claims 1.4.2 Medical Records 1.4.3 Health Surveys 1.4.4 Disease Registries 1.5 Outline of the Text 1.6 Software and Data References 2 Key Statistical Concepts 2.1 Samples and Populations 2.2 Statistics Basics 2.2.1 Random Variables 2.2.2 Dependent and Independent Variables 2.2.3 Statistical Distributions and Their Summaries 2.2.4 Parameters and Models 2.2.5 Estimation and Inference 2.2.6 Variation and Standard Error 2.2.7 Conditional and Marginal Means 2.2.8 Joint and Mixture Distributions 2.2.9 Variable Transformations 2.3 Common Statistical Distributions and Concepts 2.3.1 The Bernoulli and Binomial Distributions for Binary Outcomes 2.3.2 The Multinomial Distribution for Categorical Outcomes 2.3.3 The Poisson and Negative Binomial Distributions for Counts 2.3.4 The Normal Distribution for Continuous Outcomes 2.3.5 The Gamma and Lognormal Distributions for Right-Skewed Outcomes 2.4 Hypothesis Testing and Statistical Inference 2.5 Software and Data References 3 Regression Analysis 3.1 Introduction 3.2 Trends in Body Mass Index in the United States 3.3 Regression Overview 3.3.1 Regression to Quantify Association 3.3.2 Regression to Explain Variability 3.3.3 Regression to Estimate the Effect of an Intervention 3.3.4 Regression to Predict Outcomes 3.4 An Organic View of Regression 3.5 The Linear Regression Equation and Its Assumptions 3.6 Linear Regression Estimation and Interpretation 3.6.1 Estimation of the Regression Coefficients 3.6.2 Interpretation of the Regression Coefficients 3.6.3 Confounding 3.6.4 Moderation or Interaction 3.7 Model Selection and Hypothesis Testing 3.8 Checking Assumptions About the Random Part 3.9 Do I Have a Good Model? Goodness of Fit and Model Adequacy 3.10 Quantile Regression 3.11 Non-parametric Regression 3.12 Software and Data References 4 Binary and Categorical Outcomes 4.1 Introduction 4.2 Binary Outcomes 4.2.1 Two-Way Tables 4.3 Linear Regression with a Binary Outcome 4.4 Logistic Regression 4.5 Interpretation of a Logistic Regression 4.5.1 A Single Binary Covariate 4.5.2 The General Case 4.6 Interpretation on the Probability Scale 4.6.1 Estimating Probabilities 4.6.2 Marginal Effects 4.7 Model Building and Assessment 4.7.1 Model Comparison: AIC and BIC 4.7.2 Model Calibration: Hosmer–Lemeshow Test 4.7.3 Model Prediction: ROC and AUC 4.8 Multinomial Regression 4.8.1 An Extension of Logistic Regression 4.8.2 Marginal Effects 4.8.3 Ordered Multinomial Regression 4.9 Software and Data References 5 Count Outcomes 5.1 Count Outcomes 5.2 The Poisson Distribution 5.3 Two Count Data Regression Models 5.3.1 Modeling Health Care Utilization 5.3.2 Modeling Mortality in a Cancer Registry 5.4 Poisson Regression for Individual-Level Counts 5.4.1 A Note on Multiplicative Versus Additive Effects 5.4.2 Accounting for Exposure 5.5 Poisson Regression for Population Counts 5.6 Overdispersion, Negative Binomial, and Zero-Inflated Models 5.6.1 Negative Binomial Regression 5.6.2 Zero-Inflated Count Data Regression 5.7 Generalized Linear Models 5.8 Software and Data References 6 Health Care Costs 6.1 Defining and Measuring Health Care Costs 6.2 MEPS Data on Health Care Utilization and Costs 6.3 Log Cost Models and the Lognormal Distribution 6.4 Gamma Models for Right-Skewed Cost Outcomes 6.5 Including the Zeros: The Two-Part Model 6.6 Beyond Mean Costs 6.7 Software and Data References 7 Bootstrap Methods 7.1 Uncertainty and Inference in Statistical Models 7.2 The Bootstrap for Variance Estimation 7.3 Bootstrap Confidence Intervals 7.4 Hypothesis Testing 7.5 Summary 7.6 Software and Data References 8 Causal Inference 8.1 Introduction 8.2 Simpson's Paradox 8.3 Causal Graphs 8.3.1 Confounders 8.3.2 Mediators 8.3.3 Colliders 8.4 Building a Causal Graph 8.5 Estimating the Causal Effect 8.5.1 Stratifying 8.5.2 Matching 8.5.3 Weighting 8.6 Propensity Scores 8.7 Mediation Analysis 8.8 Potential Outcomes 8.9 Software and Data References 9 Survey Data Analysis 9.1 Introduction 9.2 Introduction to Health Surveys 9.3 National Health Surveys 9.4 Basic Elements of Survey Design 9.5 Stratified Sampling 9.5.1 Stratified Designs and Variance 9.5.2 Stratification and Weighting 9.6 Clustered Sampling 9.7 Variance Estimation and Weighting in Complex Surveys 9.8 Analyzing Survey Data: The Cost of Diabetesin the United States 9.9 Software and Data References 10 Prediction 10.1 Explaining Versus Predicting 10.2 Overfitting and the Bias-Variance Tradeoff 10.3 Evaluating Predictive Performance 10.4 Cross-Validation 10.5 Regularized Regression 10.5.1 The Age–BMI Example 10.5.2 Regularized Regression with Many Predictors: Hospitalization in MEPS 10.6 Tree-Based Methods 10.6.1 The Age–BMI Example 10.6.2 A Regression Tree with Many Predictors 10.6.3 Classification Trees 10.7 Ensemble Methods: Random Forests 10.8 Summary 10.9 Software and Data References Index