Wassertheil-Smoller / Einstein Biostatistics and Epidemiology

A Primer for Health and Biomedical Professionals
3rd Auflage 2004
ISBN: 978-0-387-21829-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 275 Seiten

For the new edition of Biostatistics and Epidemiology, Dr. Wassertheil-Smoller has included several new chapters (genetic statistics, molecular epidemiology, scientific integrity and research ethics) and a new appendix on the basic concepts of genetics and a glossary of genetic terminology. She has also expanded the coverage of multi-center trials (an important aspect of implementation of the standards of evidence-based medicine), controversies in screening for prostate, colon, breast, and other cancers.

Written for:
Graduate and undergraduate students in Medicine, fellows, postdoc researchers, clinical researchers, MSW, MPH candidates

Autoren/Hrsg.

Wassertheil-Smoller, Sylvia

Einstein, Albert

Inhaltsverzeichnis

1;PREFACE TO THE THIRD EDITION;7
2;ACKNOWLEDGMENTS;10
3;CONTENTS;11
4;THE SCIENTIFIC METHOD;17
4.1;1.1 The Logic of Scientific Reasoning;17
4.2;1.2 Variability of Phenomena Requires;22
4.3;1.3 Inductive Inference: Statistics as the Technology of the Scientific Method;23
4.4;1.4 Design of Studies;24
4.5;1.5 How to Quantify Variables;26
4.6;1.6 The Null Hypothesis;27
4.7;1.7 Why Do We Test the Null Hypothesis?;28
4.8;1.8 Types of Errors;30
4.9;1.9 Significance Level and Types of Error;31
4.10;1.10 Consequences of Type I and Type II Errors;32
5;A LITTLE BIT OF PROBABILITY;34
5.1;2.1 What Is Probability?;34
5.2;2.2 Combining Probabilities;35
5.3;2.3 Conditional Probability;38
5.4;2.4 Bayesian Probability;39
5.5;2.5 Odds and Probability;40
5.6;2.6 Likelihood Ratio;41
5.7;2.7 Summary of Probability;42
6;MOSTLY ABOUT STATISTICS;44
6.1;3.1 Chi-Square for 2x2 Tables;44
6.2;3.2 McNemar Test;49
6.3;3.3 Kappa;50
6.4;3.4 Description of a Population: Use of the Standard Deviation;51
6.5;3.5 Meaning of the Standard Deviation: The Normal Distribution;55
6.6;3.6 The Difference Between Standard Deviation and Standard Error;57
6.7;3.7 Standard Error of the Difference Between Two Means;61
6.8;3.8 Z Scores and the Standardized Normal Distribution;62
6.9;3.9 The t Statistic;66
6.10;3.10 Sample Values and Population Values Revisited;67
6.11;3.11 A Question of Confidence;68
6.12;3.12 Confidence Limits and Confidence Intervals;70
6.13;3.13 Degrees of Freedom;71
6.14;3.14 Confidence Intervals for Proportions;72
6.15;3.15 Confidence Intervals Around the Difference Between Two Means;73
6.16;3.16 Comparisons Between Two Groups;75
6.17;3.17 Z-Test for Comparing Two Proportions;75
6.18;3.18 t-Test for the Difference Between Means of Two Independent Groups: Principles;77
6.19;3.19 How to Do a t-Test: An Example;79
6.20;3.20 Matched Pair t-Test;81
6.21;3.21 When Not to Do a Lot of t-Tests: The Problem of Multiple Tests of Significance;82
6.22;3.22 Analysis of Variance: Comparison Among Several Groups;84
6.23;3.23 Principles;84
6.24;3.24 Bonferroni Procedure: An Approach to Making Multiple Comparisons;87
6.25;3.25 Analysis of Variance When There Are Two Independent Variables: The Two- Factor ANOVA;89
6.26;3.26 Interaction Between Two Independent Variables;90
6.27;3.27 Example of a Two-Way ANOVA;91
6.28;3.28 Kruskal–Wallis Test to Compare Several Groups;92
6.29;3.29 Association and Causation: The Correlation Coefficient;93
6.30;3.30 How High Is High?;94
6.31;3.31 Causal Pathways;94
6.32;3.32 Regression;97
6.33;3.33 The Connection Between Linear Regression and the Correlation Coefficient;99
6.34;3.34 Multiple Linear Regression;99
6.35;3.35 Summary So Far;101
7;MOSTLY ABOUT EPIDEMIOLOGY;102
7.1;4.1 The Uses of Epidemiology;102
7.2;4.2 Some Epidemiologic Concepts: Mortality Rates;103
7.3;4.3 Age- Adjusted Rates;105
7.4;4.4 Incidence and Prevalence Rates;107
7.5;4.5 Standardized Mortality Ratio;109
7.6;4.6 Person- Years of Observation;109
7.7;4.7 Dependent and Independent Variables;111
7.8;4.8 Types of Studies;111
7.9;4.9 Cross-Sectional Versus Longitudinal Looks at Data;112
7.10;4.10 Measures of Relative Risk: Inferences From Prospective Studies: the Framingham Study;116
7.11;4.11 Calculation of Relative Risk from Prospective Studies;118
7.12;4.12 Odds Ratio: Estimate of Relative Risk from Case- Control Studies;119
7.13;4.13 Attributable Risk;122
7.14;4.14 Response Bias;124
7.15;4.15 Confounding Variables;126
7.16;4.16 Matching;127
7.17;4.17 Multiple Logistic Regression;128
7.18;4.18 Confounding By Indication;131
7.19;4.19 Survival Analysis: Life Table Methods;132
7.20;4.20 Cox Proportional Hazards Model;135
7.21;4.21 Selecting Variables For Multivariate Models;137
7.22;4.22 Interactions: Additive and Multiplicative Models;139
8;MOSTLY ABOUT SCREENING;144
8.1;5.1 Sensitivity, Specificity, and Related Concepts;144
8.2;5.2 Cutoff Point and Its Effects on Sensitivity and Specificity;151
9;MOSTLY ABOUT CLINICAL TRIALS;155
9.1;6.1 Features of Randomized Clinical Trials;155
9.2;6.2 Purposes of Randomization;157
9.3;6.3 How to Perform Randomized Assignment;158
9.4;6.4 Two-Tailed Tests Versus One-Tailed Test;159
9.5;6.5 Clinical Trial as “Gold Standard”;160
9.6;6.6 Regression Toward the Mean;161
9.7;6.7 Intention-to-Treat Analysis;164
9.8;6.8 How Large Should the Clinical Trial Be?;165
9.9;6.9 What Is Involved in Sample Size Calculation?;167
9.10;6.10 How to Calculate Sample Size for the Difference Between Two Proportions;171
9.11;6.11 How to Calculate Sample Size for Testing the Difference Between Two Means;172
10;MOSTLY ABOUT QUALITY OF LIFE;174
10.1;7.1 Scale Construction;175
10.2;7.2 Reliability;175
10.3;7.3 Validity;177
10.4;7.4 Responsiveness;178
10.5;7.5 Some Potential Pitfalls;180
11;MOSTLY ABOUT GENETIC EPIDEMIOLOGY;183
11.1;8.1 A New Scientific Era;183
11.2;8.2 Overview of Genetic Epidemiology;184
11.3;8.3 Twin Studies;185
11.4;8.4 Linkage and Association Studies;187
11.5;8.5 LOD Score: Linkage Statistic;190
11.6;8.6 Association Studies;191
11.7;8.7 Transmission Disequilibrium Tests (TDT);193
11.8;8.8 Some Additional Concepts and Complexities of Genetic Studies;197
12;RESEARCH ETHICS AND STATISTICS;200
12.1;9.1 What does statistics have to do with it?;200
12.2;9.2 Protection of Human Research Subjects;201
12.3;9.3 Informed Consent;203
12.4;9.4 Equipoise;205
12.5;9.5 Research Integrity;205
12.6;9.6 Authorship policies;206
12.7;9.7 Data and Safety Monitoring Boards;207
12.8;9.8 Summary;207
13;Postscript: A FEW PARTING COMMENTS ON THE IMPACT OF EPIDEMIOLOGY ON HUMAN LIVES;208
14;Appendix A CRITICAL VALUES OF CHI-SQUARE, Z, AND t;210
15;Appendix B: FISHER’S EXACT TEST;211
16;Appendix C: KRUSKAL – WALLIS NONPARAMETRIC TEST TO COMPARE SEVERAL GROUPS;213
17;Appendix D: HOW TO CALCULATE A CORRELATION COEFFICIENT;215
18;Appendix E: AGE- ADJUSTMENT;217
19;Appendix F: CONFIDENCE LIMITS ON ODDS RATIOS;220
20;Appendix G: “J” OR “U” SHAPED RELATIONSHIP BETWEEN TWO VARIABLES;221
21;Appendix H: DETERMINING APPROPRIATENESS OF CHANGE SCORES;224
22;Appendix I: GENETIC PRINCIPLES;228
23;REFERENCES;234
24;SUGGESTED READINGS;239
25;INDEX;243

Leseproben

Postscript
A FEW PARTING COMMENTS ON THE IMPACT OF EPIDEMIOLOGY ON HUMAN LIVES (p. 197-198)

Ten years ago a woman with breast cancer would be likely to have a radical mastectomy, which in addition to removal of the breast and the resulting disfigurement, would also include removal of much of the muscle wall in her chest and leave her incapacitated in many ways. Today, hardly anyone gets a radical mastectomy and many don't even get a modified mastectomy, but, depending on the cancer, may get a lumpectomy which just removes the lump, leaving the breast intact. Years ago, no one paid much attention to radon, an inert gas released from the soil and dissipated through foundation cracks into homes. Now it is recognized as a leading cause of lung cancer. The role of nutrition in prevention of disease was not recognized by the scientific community. In fact, people who believed in the importance of nutrients in the cause and cure of disease were thought to be faddists, just a bit nutty. Now it is frequently the subject of articles, books, and news items, and substantial sums of research monies are invested in nutritional studies. Such studies influence legislation, as for example the regulations that processed foods must have standard labeling, easily understood by the public at large, of the fat content of the food as well as of sodium, vitamins, and other nutrients. All this has an impact on the changing eating habits of the population, as well as on the economics of the food industry.

In the health field changes in treatment, prevention, and prevailing knowledge come about when there is a confluence of circumstances: new information is acquired to supplant existing theories, there is dissemination of this information to the scientific community and to the public at large, and there is the appropriate psychological, economic, and political climate that would welcome the adoption of the new approaches. Epidemiology plays a major role by providing the methods by which new scientific knowledge is acquired. Often, the first clues to causality come long before a biological mechanism is known. Around 1850 in London, Dr. John Snow, dismayed at the suffering and deaths caused by epidemics of cholera, carefully studied reports of such epidemics and noted that cholera was much more likely to occur in certain parts of London than in other parts. He mapped the places where cholera was rampant and where it was less so, and he noted that houses supplied with water by one company, the Southwark and Vauxhall Company, had many more cases of cholera than those supplied by another company. He also knew that the Vauxhall Company used as its source an area heavily contaminated by sewage. Snow insisted that the city close the pump supplying the contaminated water, known as the Broad Street Pump. They did so and cholera abated. All this was 25 years before anyone isolated the cholera bacillus and long before people accepted the notion that disease could be spread by water. In modern times, the AIDS epidemic is one where the method of spread was identified before the infectious agent, the HIV virus, was known.

Epidemiologic techniques have been increasingly applied to chronic diseases, which differ from infectious diseases in that they may persist for a long time (whereas infections usually either kill quickly or are cured quickly) and also usually have multiple causes, many of which are difficult to identify. Here, also, epidemiology plays a central role in identifying risk factors, such as smoking for lung cancer. Such knowledge is translated into public action before the full biological pathways are elucidated. The action takes the form of educational campaigns, anti-smoking laws, restrictions on advertisement, and other mechanisms to limit smoking. The risk factors for heart disease have been identified through classic epidemiologic studies resulting in lifestyle changes for individuals as well as public policy consequences. Chronic diseases present different and challenging problems in analysis, and new statistical techniques continue to be developed to accommodate such problems. New statistical techniques are also being developed for the special problems encountered in genetics research. Thus the field of statistic is not static and the field of epidemiology is not fixed. Both adapt and expand to deal with the changing health problems of our society.