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Making Sense of Data: A Self-Instruction Manual on the Interpretation of Epidemiological Data, Third Edition

Authors:
J. H. Abramson and Z. H. Abramson
Publisher: Oxford University Press
Copyright: 2001
ISBN-13: 978-0-19-514525-0
Pages: 367; paperback
Price: $29.75

Comment from the Stata technical group

Making Sense of Data: A Self-Instruction Manual on the Interpretation of Epidemiological Data, Third Edition, by J. H. Abramson and Z. H. Abramson, is a nonmathematical workbook that introduces statistical methods for epidemiological data. Because computer software is readily available to perform the necessary calculations, this text focuses on how to choose the appropriate analysis and interpret the results, rather than detailing the theory and formulas behind such methods. The authors’ nonmathematical treament of data makes this text ideal for self-instruction for those in scientific fields other than statistics.

Abramson and Abramson cover the usual topics for epidemiological data—such as rates, odds, standardization, and two-dimensional tables—but the topics are organized to allow for a more qualitative approach to data analysis. The text also covers advanced topics, including ROC curves, Cox proportional-hazards regression, multivariate analysis, and meta-analysis.


Table of contents

Introduction
The aim of this book
How to use this book
A. Basic Concepts and Procedures
A1. Introduction
A2. Determining what the facts are
Summarizing the facts
A3. Absolute and relative differences
A4. Diagrams
A5. Seeking explanations for the facts
Testing explanations
A6. The basic scientific process
Rates
A7. Rates (continued)
Inspecting a two-dimensional table
A8. Inspecting a two-dimensional table (continued)
A9. Inspecting a two-dimensional table (continued)
Associations
A10. Associations (continued)
Confounding
A11. Confounding (continued)
Effect modification
A12. Refinement
Skeleton tables
Elaborating as association
A13. Modifying and confounding effects
A14. Elaborating an association (continued)
A15. The use of rates
Causal explanations
Testing causal explanations
A16. Testing causal explanations (continued)
Basic procedure for appraisal of data
What are the facts?
What are the possible explanations?
What additional information is required?
A17. Uses of epidemiological data
A18. Test yourself
B. Rates and Other Measures
B1. Introduction
What is a rate?
Prevalence rates
B2. Prevalence rates (continued)
B3. Questions about a rate
What kind of rate is it?
Of what is it a rate?
To what population or group does the rate refer?
How was the information obtained?
B4. Sources of bias
Confidence interval
Validity
Qualitative studies
B5. Use of prevalence data
Incidence rates
B6. Incidence rates (continued)
B7. Bias in incidence studies
B8. Uses of incidence rates
B9. Estimating the individual's chances
Time to event (survival time)
B10. Estimating the individual's chances (continued)
Other rates
What are the odds?
B11. Other rates (continued)
Odds ratios
B12. Other measures
B13. Indirect standardization
B14. Indirect standardization (continued)
Direct standardization
B15. The use of standardized rates
B16. Test yourself (B)
C. How Good Are the Measures?
C1. Introduction
C2. Validity of a measure
Sensitivity and specificity
C3. Misclassification
C4. Differential misclassification
C5. Effects of misclassification
C6. Effects of misclassification (continued)
C7. Other ways of appraising validity
Reliability
C8. Appraisal of reliability
C9. Appraisal of reliability (continued)
Regression toward the mean
Taking account of validity and reliability
Screening tests
C10. Appraisal of a screening test
C11. Appraisal of a screening test (continued)
C12. Appraisal of diagnostic tests
ROC curves
The meaning of "normal"
C13. Test yourself (C)
D. Making Sense of Associations
D1. Introduction
D2. Explanations for an association
D3. Effects of misclassification
Statistical significance
D4. Statistical significance (continued)
D5. Confounding effects
D6. Confounding effects (continued)
D7. Multivariate analysis
D8. Explanations for the findings
Risk factors and risk markers
Appraising a risk marker
Uses of the findings
D9. Risk factors and risk markers (continued)
Measures of the strength of an association
D10. Measures of strength
D11. Measures of strength (continued)
Matched samples
D12. Synergism
D13. Appraising stratified data
Making sense of a multivariate analysis
D14. Multiple logistic regression
D15. Multiple logistic regression (continued)
D16. Proportional hazards regression
D17. Multiple linear regression
D18. Test yourself (D)
E. Causes and Effects
E1. Introduction
Kinds of study
E2. Appraising the results of a cross-sectional study
E3. Appraising the results of a case-control study
E4. Appraising the results of a cohort study
E5. Appraising the results of a group-based study
E6. Appraising the results of an experiment
E7. Appraising the results of a quasi-experiment
E8. Artifact, confounding or cause?
E9. Coping with confounding
Delving into causes
E10. Evidence for a causal relationship
E11. Evidence for a causal relationship (continued)
The impact of a causal factor
E12. The attributable fraction
E13. Prevented and preventable fractions
E14. Test yourself (E)
F. Meta-Analysis: Putting It All Together
F1. Introduction
F2. The scope of meta-analysis
F3. Measures used in meta-analysis
F4. Measures used in meta-analysis (continued)
Basic information
F5. Finding the studies
F6. Selecting studies
F7. The quality of the studies
Extracting the findings
Apples and oranges
F8. Appraising combinability
Explaining heterogeneity
F9. Explaining heterogeneity (continued)
F10. Effect modification
F11. Using the results
Evaluating a meta-analysis
F12. Test yourself (F)
G. Putting Study Finds to Use
G1. Introduction
G2. Are the results accurately known?
G3. Validity of the findings
G4. Relevance of the findings
G5. Expected effects
G6. Feasibility and cost
G7. Test yourself (G)
References
Index
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