Statistics for health data science : an organic approach
著者
書誌事項
Statistics for health data science : an organic approach
(Springer texts in statistics)
Springer, c2020
大学図書館所蔵 全5件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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/
目次
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
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