LEADER 04289nam 22006855 450 001 9910483655003321 005 20250402120742.0 010 $a3-030-59889-6 024 7 $a10.1007/978-3-030-59889-1 035 $a(CKB)5460000000008725 035 $a(DE-He213)978-3-030-59889-1 035 $a(MiAaPQ)EBC6455873 035 $a(PPN)253253276 035 $a(EXLCZ)995460000000008725 100 $a20210104d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistics for Health Data Science $eAn Organic Approach /$fby Ruth Etzioni, Micha Mandel, Roman Gulati 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource $cillustrations (chiefly color) 225 1 $aSpringer Texts in Statistics,$x2197-4136 311 08$a3-030-59888-8 320 $aIncludes bibliographical references and index. 330 $aStudents 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 tackleanalysis 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/. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aBiometry 606 $aQuantitative research 606 $aStatistics 606 $aPublic health 606 $aEpidemiology 606 $aBiostatistics 606 $aData Analysis and Big Data 606 $aStatistical Theory and Methods 606 $aPublic Health 606 $aEpidemiology 615 0$aBiometry. 615 0$aQuantitative research. 615 0$aStatistics. 615 0$aPublic health. 615 0$aEpidemiology. 615 14$aBiostatistics. 615 24$aData Analysis and Big Data. 615 24$aStatistical Theory and Methods. 615 24$aPublic Health. 615 24$aEpidemiology. 676 $a519.5 700 $aEtzioni$b Ruth$01065697 702 $aMandel$b Micha 702 $aGulati$b Roman 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483655003321 996 $aStatistics for health data science$92547558 997 $aUNINA