Modeling in Medical Decision Making: A Bayesian Approach

ISBN-13: 9780471986089 / Angielski / Twarda / 2002 / 280 str.

Giovanni Parmigiani; G. Parmigiani

Opis

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to interpret, and can help to address the most pressing practical and ethical concerns arising in medical decision making.
* Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory.
* Driven by three real applications, presented as extensively detailed case studies.
* Case studies include simplified versions of the analysis, to approach complex modelling in stages.
* Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling.
* Accessible to readers with only a basic statistical knowledge.
Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health services research, and health policy.

Recenzje

"…good to use as one component in a graduate course…for established statisticians and biostatisticians, the book is a good way to get up to speed…" ( Journal of the American Statistical Association, March 2007)

"…strongly recommend…[it] to clinical researchers and statisticians." (Journal of Statistical Computation & Simulation, May 2004)

"...I recommend his book." (Statistics in Medicine, 28 February 2003)

"...a comprehensive presentation of topics..." (Clinical Chemistry, Vol. 49, No. 4)

"…an indispensable volume owing to the clarity of its discussion…" (Journal of Drug Assessment, Vol.6, No.4, 2003)

"...another fine practical applications book..." (Technometrics, Vol. 44, No. 4, November 2002)

"…skillfully brings together sophisticated statistical models and detailed medical applications…" (Applied Clinical Trials, June 2002)

"...surveys inferential methods…features case studies..." (SciTech Book News, Vol. 26, No. 2, June 2002)

"...useful to research students in biostatistics...a welcome addition to any undergraduate library in statistics..." (The Statistician)

Spis treści

Preface.

PART I: METHODS.

1. Inference.

Summary.

Medical Diagnosis.

Genetic Counseling.

Estimating sensitivity and specificity.

Chronic disease modeling.

2. Decision making.

Summary.

Foundations of expected utility theory.

Measuring the value of avoiding a major stroke.

Decision making in health care.

Cost–effectiveness analyses in the μ SPPM.

Statistical decision problems.

3. Simulation.

Summary.

Inference via simulation.

Prediction and expected utility via simulation.

Sensitivity analysis via simulation.

Searching for strategies via simulation.

Part II: CASE STUDIES.

4. Meta–analysis.

Summary.

Meta–analysis.

Bayesian meta–analysis.

Tamoxifen in early breast cancer.

Combined studies with continuous and dichotomous responses.

Migraine headache.

5. Decision trees.

Summary.

Axillary lymph node dissection in early breast cancer.

A simple decision tree

A more complete decision tree for ALND

6. Chronic disease modeling.

Summary.

Model overview.

Natural history model.

Modeling the effects of screening.

Comparing screening schedules.

Model critique.

Optimizing screening schedule.

References

Index.

Blurb

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation–based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making.

• Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory.
• Driven by three real applications, presented as extensively detailed case studies.
• Case studies include simplified versions of the analysis, to approach complex modelling in stages.
• Features coverage of meta–analysis, decision analysis, and comprehensive decision modeling.
• Accessible to readers with only a basic statistical knowledge.

Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence–based medicine, health economics, health service research and health policy.