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Statistics for Health Data Science: An Organic Approach 2020 ed. [Kõva köide]

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  • Formaat: Hardback, 222 pages, kõrgus x laius: 235x155 mm, kaal: 535 g, 43 Illustrations, color; 22 Illustrations, black and white; XXII, 222 p. 65 illus., 43 illus. in color., 1 Hardback
  • Sari: Springer Texts in Statistics
  • Ilmumisaeg: 05-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030598888
  • ISBN-13: 9783030598884
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Statistics for Health Data Science: An Organic Approach 2020 ed.Suurem pilt
  • Formaat: Hardback, 222 pages, kõrgus x laius: 235x155 mm, kaal: 535 g, 43 Illustrations, color; 22 Illustrations, black and white; XXII, 222 p. 65 illus., 43 illus. in color., 1 Hardback
  • Sari: Springer Texts in Statistics
  • Ilmumisaeg: 05-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030598888
  • ISBN-13: 9783030598884
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030598884.html
Märksõnad:
Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science.





This textbook is designed to overcome students anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engagingexplanations and examples. In this way, the authors cultivate a deep (organic) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts.  





This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms.





Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/
1 Statistics And Health Data
1(16)
1.1 Introduction
1(1)
1.2 Statistics and Organic Statistics
2(1)
1.3 Statistical Methods and Models
3(3)
1.4 Health Care Data
6(7)
1.4.1 Medical Claims
7(2)
1.4.2 Medical Records
9(1)
1.4.3 Health Surveys
10(2)
1.4.4 Disease Registries
12(1)
1.5 Outline of the Text
13(1)
1.6 Software and Data
14(1)
References
14(3)
2 Key Statistical Concepts
17(20)
2.1 Samples and Populations
17(1)
2.2 Statistics Basics
18(7)
2.2.1 Random Variables
18(1)
2.2.2 Dependent and Independent Variables
19(1)
2.2.3 Statistical Distributions and Their Summaries
19(2)
2.2.4 Parameters and Models
21(1)
2.2.5 Estimation and Inference
21(1)
2.2.6 Variation and Standard Error
22(1)
2.2.7 Conditional and Marginal Means
23(1)
2.2.8 Joint and Mixture Distributions
24(1)
2.2.9 Variable Transformations
24(1)
2.3 Common Statistical Distributions and Concepts
25(8)
2.3.1 The Bernoulli and Binomial Distributions for Binary Outcomes
25(2)
2.3.2 The Multinomial Distribution for Categorical Outcomes
27(1)
2.3.3 The Poisson and Negative Binomial Distributions for Counts
27(3)
2.3.4 The Normal Distribution for Continuous Outcomes
30(1)
2.3.5 The Gamma and Lognormal Distributions for Right-Skewed Outcomes
31(2)
2.4 Hypothesis Testing and Statistical Inference
33(3)
2.5 Software and Data
36(1)
References
36(1)
3 Regression Analysis
37(28)
3.1 Introduction
37(1)
3.2 Trends in Body Mass Index in the United States
38(1)
3.3 Regression Overview
39(3)
3.3.1 Regression to Quantify Association
40(1)
3.3.2 Regression to Explain Variability
40(1)
3.3.3 Regression to Estimate the Effect of an Intervention
41(1)
3.3.4 Regression to Predict Outcomes
41(1)
3.4 An Organic View of Regression
42(3)
3.5 The Linear Regression Equation and Its Assumptions
45(1)
3.6 Linear Regression Estimation and Interpretation
46(7)
3.6.1 Estimation of the Regression Coefficients
46(2)
3.6.2 Interpretation of the Regression Coefficients
48(2)
3.6.3 Confounding
50(1)
3.6.4 Moderation or Interaction
51(2)
3.7 Model Selection and Hypothesis Testing
53(3)
3.8 Checking Assumptions About the Random Part
56(1)
3.9 Do I Have a Good Model? Goodness of Fit and Model Adequacy
57(2)
3.10 Quantile Regression
59(1)
3.11 Non-parametric Regression
60(3)
3.12 Software and Data
63(1)
References
63(2)
4 Binary And Categorical Outcomes
65(28)
4.1 Introduction
65(1)
4.2 Binary Outcomes
66(3)
4.2.1 Two-Way Tables
67(2)
4.3 Linear Regression with a Binary Outcome
69(1)
4.4 Logistic Regression
70(2)
4.5 Interpretation of a Logistic Regression
72(4)
4.5.1 A Single Binary Covariate
72(1)
4.5.2 The General Case
73(3)
4.6 Interpretation on the Probability Scale
76(4)
4.6.1 Estimating Probabilities
76(2)
4.6.2 Marginal Effects
78(2)
4.7 Model Building and Assessment
80(6)
4.7.1 Model Comparison: AIC and BIC
80(1)
4.7.2 Model Calibration: Hosmer-Lemeshow Test
81(2)
4.7.3 Model Prediction: ROC and AUC
83(3)
4.8 Multinomial Regression
86(6)
4.8.1 An Extension of Logistic Regression
86(4)
4.8.2 Marginal Effects
90(1)
4.8.3 Ordered Multinomial Regression
91(1)
4.9 Software and Data
92(1)
References
92(1)
5 Count Outcomes
93(20)
5.1 Count Outcomes
93(1)
5.2 The Poisson Distribution
94(1)
5.3 Two Count Data Regression Models
95(2)
5.3.1 Modeling Health Care Utilization
96(1)
5.3.2 Modeling Mortality in a Cancer Registry
96(1)
5.4 Poisson Regression for Individual-Level Counts
97(4)
5.4.1 A Note on Multiplicative Versus Additive Effects
99(1)
5.4.2 Accounting for Exposure
100(1)
5.5 Poisson Regression for Population Counts
101(3)
5.6 Overdispersion, Negative Binomial, and Zero-Inflated Models
104(6)
5.6.1 Negative Binomial Regression
105(3)
5.6.2 Zero-Inflated Count Data Regression
108(2)
5.7 Generalized Linear Models
110(1)
5.8 Software and Data
111(1)
References
112(1)
6 Health Care Costs
113(20)
6.1 Defining and Measuring Health Care Costs
113(1)
6.2 MEPS Data on Health Care Utilization and Costs
114(2)
6.3 Log Cost Models and the Lognormal Distribution
116(5)
6.4 Gamma Models for Right-Skewed Cost Outcomes
121(3)
6.5 Including the Zeros: The Two-Part Model
124(4)
6.6 Beyond Mean Costs
128(2)
6.7 Software and Data
130(1)
References
130(3)
7 Bootstrap Methods
133(16)
7.1 Uncertainty and Inference in Statistical Models
133(2)
7.2 The Bootstrap for Variance Estimation
135(6)
7.3 Bootstrap Confidence Intervals
141(2)
7.4 Hypothesis Testing
143(3)
7.5 Summary
146(1)
7.6 Software and Data
147(1)
References
147(2)
8 Causal Inference
149(24)
8.1 Introduction
149(2)
8.2 Simpson's Paradox
151(1)
8.3 Causal Graphs
152(4)
8.3.1 Confounders
153(1)
8.3.2 Mediators
154(1)
8.3.3 Colliders
155(1)
8.4 Building a Causal Graph
156(1)
8.5 Estimating the Causal Effect
157(8)
8.5.1 Stratifying
159(3)
8.5.2 Matching
162(1)
8.5.3 Weighting
163(2)
8.6 Propensity Scores
165(2)
8.7 Mediation Analysis
167(3)
8.8 Potential Outcomes
170(1)
8.9 Software and Data
171(1)
References
171(2)
9 Survey Data Analysis
173(18)
9.1 Introduction
173(1)
9.2 Introduction to Health Surveys
174(1)
9.3 National Health Surveys
175(1)
9.4 Basic Elements of Survey Design
176(2)
9.5 Stratified Sampling
178(3)
9.5.1 Stratified Designs and Variance
178(2)
9.5.2 Stratification and Weighting
180(1)
9.6 Clustered Sampling
181(2)
9.7 Variance Estimation and Weighting in Complex Surveys
183(2)
9.8 Analyzing Survey Data: The Cost of Diabetes in the United States
185(3)
9.9 Software and Data
188(1)
References
188(3)
10 Prediction
191(28)
10.1 Explaining Versus Predicting
191(3)
10.2 Overfitting and the Bias-Variance Tradeoff
194(2)
10.3 Evaluating Predictive Performance
196(2)
10.4 Cross-Validation
198(2)
10.5 Regularized Regression
200(5)
10.5.1 The Age-BMI Example
200(2)
10.5.2 Regularized Regression with Many Predictors: Hospitalization in MEPS
202(3)
10.6 Tree-Based Methods
205(7)
10.6.1 The Age-BMI Example
206(2)
10.6.2 A Regression Tree with Many Predictors
208(1)
10.6.3 Classification Trees
209(3)
10.7 Ensemble Methods: Random Forests
212(3)
10.8 Summary
215(1)
10.9 Software and Data
216(1)
References
216(3)
Index 219
Ruth Etzioni, PhD has been on the faculty at the Fred Hutchinson Cancer Research Center since 1991 and is an affiliate professor of biostatistics and health services at the University of Washington. She develops statistical models and methods for health policy and is a member of national cancer policy panels including the American Cancer Society and the National Comprehensive Cancer Network.  She has developed and taught a new curriculum in statistical methods for graduate students in the School of Public Health at the University of Washington; the course focuses on health care analytics using contemporary, publicly available data resources. The popularity of this course led her to conceive of and develop the proposed text. Dr. Etzioni received her undergraduate degree in Computer Science and Operations Research from the University of Cape Town and her PhD in Statistics from Carnegie-Mellon University.





Micha Mandel, PhD, is professor of statistics at the Hebrew University of Jerusalem. Micha has vast experience teaching at all levels from undergraduate to PhD students, and has been engaged with a wide range of problems in medicine and health care. His interaction with students and researchers from different fields led him to develop tools to explain sophisticated statistical concepts and methods in ways that are accessible to many audiences. His main areas of research include biased sampling, survival analysis, and forensic statistics, but he continues to expand his reach, most recently to the estimation of COVID-19 natural history. He has published in many high-profile statistical journals including Biometrics, Biometrika, Journal of the American Statistical Association, and Statistics in Medicine. Micha received his PhD in Statistics from the Hebrew University of Jerusalem.





Roman Gulati, MS, has been a senior statistical analyst at the Fred Hutchinson Cancer Research Center since 2005. Mr. Gulati is a designer, developer, and analyst of statistical models to investigate population impacts of national clinical practice patterns and cancer control policies. He has led or contributed to many independent and collaborative modeling studies for the Cancer Intervention and Surveillance Modeling Network of the National Cancer Institute. He is also chief biostatistician for the prostate cancer research program at the Fred Hutch and the University of Washington, supporting many molecular, preclinical, and clinical research studies. Mr. Gulati received graduate training first in mathematics and then in Chinese before earning his MS in Statistics from Oregon State University.