U.S. Healthcare Data All-in-One

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Bol U.S. Healthcare Data All-in-One is a hands-on handbook for anyone working with healthcare data, bridging its two worlds: the strict order of clinical-trial data (CDISC's SDTM and ADaM, define.xml, and submission packages) and the sprawl of real-world data (insurance claims and electronic health records, medical coding systems, and the OMOP common data model). Across 42 chapters and five appendices, every chapter is built around a runnable deliverable, and all code-written exclusively in SAS and Oracle SQL-has been actually tested. Rather than piling up algorithms, the book keeps answering the question that really matters: why does this data look the way it does, and where will it trip you up? It moves from data profiling, specification, and metric calculation, through building SDTM/ADaM domains, producing TFLs, and assembling a submission package, to real-world study design, avoiding bias, statistical fundamentals, and explaining results to non-technical audiences-closing with a career map and a new-hire survival guide. Written for analysts entering the field who want to treat the person behind every record as a person.

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Beschrijving (1)

U.S. Healthcare Data All-in-One is a hands-on handbook for anyone working with healthcare data, bridging its two worlds: the strict order of clinical-trial data (CDISC's SDTM and ADaM, define.xml, and submission packages) and the sprawl of real-world data (insurance claims and electronic health records, medical coding systems, and the OMOP common data model). Across 42 chapters and five appendices, every chapter is built around a runnable deliverable, and all code-written exclusively in SAS and Oracle SQL-has been actually tested. Rather than piling up algorithms, the book keeps answering the question that really matters: why does this data look the way it does, and where will it trip you up? It moves from data profiling, specification, and metric calculation, through building SDTM/ADaM domains, producing TFLs, and assembling a submission package, to real-world study design, avoiding bias, statistical fundamentals, and explaining results to non-technical audiences-closing with a career map and a new-hire survival guide. Written for analysts entering the field who want to treat the person behind every record as a person.


Productspecificaties

Merk Jiazheng Chen
EAN
  • 9781965630747
Maat


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