Basic Biostatistics for Geneticists and Epidemiologists A Practical Approach

, by Elston, Robert C.; Johnson, William

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ISBN: 9780470024898 | 0470024895
Cover: Hardcover
Copyright: 12/3/2008

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Today, anyone who attempts to read genetics or epidemiology research literature intelligently, needs to understand the essentials of biostatistics. This booka revised new edition of the successful 'Essentials of Biostatistics'has been written to provide such an understanding to those who have little or no statistical background and who need to keep abreast of new findings in this fast moving field. Unlike many other elementary books of biostatistics, the main focus of this book is not so much teaching how to perform some of the simpler statistical procedures that may be necessary for a research paper, but rather on explaining basic and fundamental concepts without getting bogged down in computational details.

Preface	p. ix
Introduction: The Role and Relevance of Statistics, Genetics and Epidemiology in Medicine	p. 3
Why Biostatistics?	p. 3
What Exactly Is (Are) Statistics?	p. 5
Reasons for Understanding Statistics	p. 6
What Exactly is Genetics?	p. 8
What Exactly is Epidemiology?	p. 10
How Can a Statistician Help Geneticists and Epidemiologists?	p. 11
Disease Prevention versus Disease Therapy	p. 12
A Few Examples: Genetics, Epidemiology and Statistical Inference	p. 12
Summary	p. 14
References	p. 15
Populations, Samples, and Study Design	p. 19
The Study of Cause and Effect	p. 19
Populations, Target Populations and Study Units	p. 21
Probability Samples and Randomization	p. 23
Observational Studies	p. 25
Family Studies	p. 27
Experimental Studies	p. 28
Quasi-Experimental Studies	p. 36
Summary	p. 37
Further Reading	p. 38
Problems	p. 38
Descriptive Statistics	p. 45
Why Do We Need Descriptive Statistics?	p. 45
Scales of Measurement	p. 46
Tables	p. 47
Graphs	p. 49
Proportions and Rates	p. 55
Relative Measures of Disease Frequency	p. 58
Sensitivity, Specificity and Predictive Values	p. 61
Measures of Central Tendency	p. 62
Measures of Spread or Variability	p. 64
Measures of Shape	p. 67
Summary	p. 68
Further Reading	p. 70
Problems	p. 70
The Laws of Probability	p. 79
Definition of Probability	p. 79
The Probability of Either of Two Events: A or B	p. 82
The Joint Probability of Two Events: A and B	p. 83
Examples of Independence, Nonindependence and Genetic Counseling	p. 86
Bayes' Theorem	p. 89
Likelihood Ratio	p. 97
Summary	p. 98
Further Reading	p. 99
Problems	p. 99
Random Variables and Distributions	p. 107
Variability and Random Variables	p. 107
Binomial Distribution	p. 109
A Note about Symbols	p. 112
Poisson Distribution	p. 113
Uniform Distribution	p. 114
Normal Distribution	p. 116
Cumulative Distribution Functions	p. 119
The Standard Normal (Gaussian) Distribution	p. 120
Summary	p. 122
Further Reading	p. 123
Problems	p. 123
Estimates and Confidence Limits	p. 131
Estimates and Estimators	p. 131
Notation for Population Parameters, Sample Estimates, and Sample Estimators	p. 133
Properties of Estimators	p. 134
Maximum Likelihood	p. 135
Estimating Intervals	p. 137
Distribution of the Sample Mean	p. 138
Confidence Limits	p. 140
Summary	p. 146
Problems	p. 148
Significance Tests and Tests of Hypotheses	p. 155
Principle of Significance Testing	p. 155
Principle of Hypothesis Testing	p. 156
Testing a Population Mean	p. 157
One-Sided versus Two-Sided Tests	p. 160
Testing a Proportion	p. 161
Testing the Equality of Two Variances	p. 165
Testing the Equality of Two Means	p. 167
Testing the Equality of Two Medians	p. 169
Validity and Power	p. 172
Summary	p. 176
Further Reading	p. 178
Problems	p. 178
Likelihood Ratios, Bayesian Methods and Multiple Hypotheses	p. 187
Likelihood Ratios	p. 187
Bayesian Methods	p. 190
Bayes' Factors	p. 192
Bayesian Estimates and Credible Intervals	p. 194
The Multiple Testing Problem	p. 195
Summary	p. 198
Problems	p. 199
The Many Uses of Chi-Square	p. 203
The Chi-Square Distribution	p. 203
Goodness-of-Fit Tests	p. 206
Contingency Tables	p. 209
Inference About the Variance	p. 219
Combining p-Values	p. 220
Likelihood Ratio Tests	p. 221
Summary	p. 223
Further Reading	p. 225
Problems	p. 225
Correlation and Regression	p. 233
Simple Linear Regression	p. 233
The Straight-Line Relationship When There is Inherent Variability	p. 240
Correlation	p. 242
Spearman's Rank Correlation	p. 246
Multiple Regression	p. 246
Multiple Correlation and Partial Correlation	p. 250
Regression toward the Mean	p. 251
Summary	p. 253
Further Reading	p. 254
Problems	p. 255
Analysis of Variance and Linear Models	p. 265
Multiple Treatment Groups	p. 265
Completely Randomized Design with a Single Classification of Treatment Groups	p. 267
Data with Multiple Classifications	p. 269
Analysis of Covariance	p. 281
Assumptions Associated with the Analysis of Variance	p. 282
Summary	p. 283
Further Reading	p. 284
Problems	p. 285
Some Specialized Techniques	p. 293
Multivariate Analysis	p. 293
Discriminant Analysis	p. 295
Logistic Regression	p. 296
Analysis of Survival Times	p. 299
Estimating Survival Curves	p. 301
Permutation Tests	p. 304
Resampling Methods	p. 309
Summary	p. 312
Further Reading	p. 313
Problems	p. 313
Guides to a Critical Evaluation of Published Reports	p. 321
The Research Hypothesis	p. 321
Variables Studied	p. 321
The Study Design	p. 322
Sample Size	p. 322
Completeness of the Data	p. 323
Appropriate Descriptive Statistics	p. 323
Appropriate Statistical Methods for Inferences	p. 323
Logic of the Conclusions	p. 324
Meta-analysis	p. 324
Summary	p. 326
Further Reading	p. 327
Problems	p. 328
Epilogue	p. 329
Review Problems	p. 331
Answers to Odd-Numbered Problems	p. 345
Appendix	p. 353
Index	p. 365
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Basic Biostatistics for Geneticists and Epidemiologists A Practical Approach

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