Decision making in health care involves consideration of a complex set of diagnostic, therapeutic and prognostic uncertainties. Medical therapies have side effects, surgical interventions may lead to complications, and diagnostic tests can produce misleading results. Furthermore, patient values and service costs must be considered. Decisions in clinical and health policy require careful weighing of risks and benefits and are commonly a trade-off of competing objectives: maximizing quality of life vs maximizing life expectancy vs minimizing the resources required. This text takes a proactive, systematic and rational approach to medical decision making. It covers decision trees, Bayesian revision, receiver operating characteristic curves, and cost-effectiveness analysis, as well as advanced topics such as Markov models, microsimulation, probabilistic sensitivity analysis and value of information analysis. It provides an essential resource for trainees and researchers involved in medical decision modelling, evidence-based medicine, clinical epidemiology, comparative effectiveness, public health, health economics, and health technology assessment.
Review of previous edition:‘This is an impressively comprehensive approach to clinical decision-making with every angle considered, advantages and disadvantages tabulated … there is a great deal of knowledge and information in these chapters, presented with masses of detailed mathematical and statistical analysis.'
Source: Journal of the Royal Society of Medicine
Review of previous edition:‘… once I started to read the book, I was gripped. It is very readable, making a complex subject appear crystal clear and easy to understand … this book is a very useful addition to the literature on evidence-based health care, providing clear strategies for incorporating decision analysis into our daily decision making.'
Source: Family Practice
Review of previous edition:‘This book would be a useful acquisition for a statistician who needs the basics of decision-making, and it would certainly be a good book for a non-statistician.'
Source: Pharmaceutical Statistics
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