Statistics for health data science : an organic approach
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Bibliographic Information
Statistics for health data science : an organic approach
（Springer texts in statistics）
Springer, c2020
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Includes bibliographical references and indexes
Description and Table of Contents
Description
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 engaging explanations 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/
Table of Contents
Chapter 1: Introduction: Data science, statistics, and big data in healthExamples of the "new" health services, delivery and outcomes data including surveys, claims and EMR's. Examples of the big questions that can be addressed. Data Science versus statistics, big databases versus big data, prediction versus inference. Characteristics of health care utilization data. What does health care cost? Different ways of quantifying health care costs. Characteristics of health cost data.
Chapter 2: The new health care data: surveys, medical claims and EMR'sSurveys, Medical Claims, EMR's: characteristics and challenges. Examples of studies based on the different types of data resources. Strengths and weaknesses of each. Tips for quality control. Possibly: An overview of issues in processing unstructured data and linking databases
Chapter 3: Basic statistical background useful for analysis of health care costs and utilizationThe generic inference problem. Some useful statistical distributions. Conditional and marginal probability. Least squares and maximum likelihood. Hypothesis testing and discussion about p-values. Statistical power.
Chapter 4: Conceptual models for health care utilization and costs Anderson-Newman model, variants and extensions.
Chapter 5: Linear regression for observational studiesConfounding, Mediation and Moderation. Difference in difference models. Impact of violating OLS assumptions
Chapter 6: Nonlinear models 1: Binary outcomes and choice models Probit models. Logistic models and conditional logistic models. Multinomial logit regression models and ordered logit models. The method of recycled predictions.
Chapter 7: Nonlinear models 2: Models for count outcomes Log-linear models for count outcomes. Poisson and negative binomial regression. Models for individual and population counts. Zero-inflated and zero-truncated models. Generalized Linear Models.
Chapter 8: Risk adjustmentConstructing comorbidity and risk adjustment variables using claims data. Computing Q/E ratios. Using O/E ratios for profiling facilities.
Chapter 9: Models for skewed health costsLog-normal models for skewed costs. Duan's method of smearing for lognormal data. The difference between modeling the log of Y (lognormal models for costs) and log(E(Y)) log-linear models for count outcomes. Gamma models as an alternative to lognormal models for cost data. Cross-validation for model selection.
Chapter 10: Two-part models for costs and countsZero-inflated Poisson and negative binomial models. Two part models (logit-normal or logit-gamma) for cost outcomes. Cross-validation for model selection.
Chapter 11: The bootstrap: General principles and use in variance estimation for two-part modelsDoes the normality assumption matter? Using the bootstrap to examine the properties of regression coefficient estimates in large sample. Different types of bootstrap confidence intervals. Extending the bootstrap to compute the variance of the marginal effects in the two-part model.
Chapter 12: Survey data analysisExamples of Health Surveys. Complexity of Health Surveys. Simple Random Sampling. Stratified Sampling. Post-Stratification. Other methods for dealing with missing data. Cluster Sampling. Sample Weights: when to use or not to use? Ratio estimation, linearization and variance estimation
Chapter 13: Machine learning methods for predictionPredictive analytics versus statistical inference. Simple classification and discrimination algorithms. Trees, bagged models, random forests and boosting. Adjustments for rare outcomes. Regularization. Penalized regression and the LASSO. Prediction versus estimation versus inference.
Chapter 14: Comparative Effectiveness and causal inference. Defining comparative effectiveness. The problem of selection bias or confounding by indication. General framework for causal inference. Inverse probability weighting and its applications. Instrumental variables, their potential and their limitations
by "Nielsen BookData"