Isbn: 9781009648189 - healthcare quality measurement: volume 2, statistical methodology (practical guides to biostatistics and epidemiology) (11 resultados)

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Condición: New. 2026. paperback. . . . . .

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented.…

Condición: NEW.

Paperback. Condición: new. Paperback. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models, IRT, and computational methods. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models, IRT, and computational methods. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented. This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models, IRT, and computational methods. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…