Risk Stratification: A Practical Guide for Clinicians

Front Cover
Cambridge University Press, Feb 22, 2001 - Medical - 174 pages
Risk stratification is a statistical process by which quality of care can be assessed independently of patient case mix. The evaluation of risk-adjusted patient outcome has become an important part of managed care contracting in some markets, and risk-adjusted outcome rates for hospitals are being reported more frequently in the popular press and on the Internet. This book, written by a statistician and two surgeons for a clinical audience, is a practical guide to the process of risk stratification and does not require or assume an extensive mathematical background. It describes the rationale and assumptions for risk stratification, and provides information on evaluating the quality of various published risk-stratification studies. Numerous practical examples using real clinical data help to illustrate risk stratification in health care. The volume also serves as a step-by-step guide to the production and dissemination of risk-adjusted outcome results for local programs.
 

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Contents

Risk
7
Probability and risk
9
Risk and odds
11
Risk and a single risk factor
15
Risk and multiple risk factors
17
Selection of variables into logistic models
25
Model error and classification accuracy
26
Generalizing the results
28
Multivariable risk stratification
74
Calculation of expected risk for a study sample
78
The observedexpected ratio
82
Interpretation issues
85
Conclusion
87
Interpreting risk models
88
Bias
89
Missing data
95

Conclusion
29
Collecting data
30
Identifying a question
33
Identification of variables
35
Case definition
36
Case ascertainment
40
Planning data collection
41
Selecting data collection software
43
Data entry
44
Pilot testing
45
Quality control
46
Source documentation
48
Conclusion
49
Risk and published studies
50
Evaluating the quality of a study
51
Classical clinical research
54
Risk studies
61
Determining the appropriateness of a reference population
64
Studies that can be used for risk measurements
67
Conclusion
68
References
69
Applying published risk estimates to local data
70
Presentation of results
97
Administrative versus clinical data
103
Some guidelines for application and interpretation of the methods
105
Conclusion
108
References
109
Advanced issues
110
Design issues
112
Collecting data
118
Univariate analysis
120
Multivariate analysis
137
Statistical significance
140
Evaluating model prediction
141
Variable coding
144
Fit testing
149
Plotting
152
Interpretation
155
Conclusion
161
References
162
Appendices
164
Appendix 2
166
Appendix 3
167
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Page 3 - As in any other application the models are only as good as the data on which they are based.

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