Applied Biostatistics for the Health Sciences

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ISBN: 9780470147641 | 0470147644
Cover: Hardcover
Copyright: 12/9/2009

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Biostatistics is quickly becoming one of the most important areas of statistics due to the tremendous increase in health care needs. This book successfully introduces the terminology, concepts, and correct uses and interpretation of biostatistics. It is ideal for practitioners as well as students going into health care fields. Pedagogical features include formulas highlighted in text boxes and chapter summaries that highlight key vocabulary and concepts for the chapter. An accompanying Web site provides both MINITABr and Microsoftr Office Excelr data files data for the case studies and exercises that are contained in the text.

Preface	p. xi
Introduction to Biostatistics	p. 1
What is Biostatistics?	p. 1
Populations, Samples, and Statistics	p. 2
The Basic Biostatistical Terminology	p. 3
Biomedical Studies	p. 5
Observational Studies Versus Experiments	p. 7
Clinical Trials	p. 9
Safety and Ethical Considerations in a Clinical Trial	p. 9
Types of Clinical Trials	p. 10
The Phases of a Clinical Trial	p. 11
Data Set Descriptions	p. 12
Birth Weight Data Set	p. 12
Body Fat Data Set	p. 12
Coronary Heart Disease Data Set	p. 12
Prostate Cancer Study Data Set	p. 13
Intensive Care Unit Data Set	p. 14
Mammography Experience Study Data Set	p. 14
Benign Breast Disease Study	p. 15
Glossary	p. 17
Exercises	p. 18
Describing Populations	p. 23
Populations and Variables	p. 23
Qualitative Variables	p. 24
Quantitative Variables	p. 25
Multivariate Data	p. 27
Population Distributions and Parameters	p. 28
Distributions	p. 29
Describing a Population with Parameters	p. 33
Proportions and Percentiles	p. 33
Parameters Measuring Centrality	p. 35
Measures of Dispersion	p. 38
The Coefficient of Variation	p. 41
Parameters for Bivariate Populations	p. 43
Probability	p. 46
Basic Probability Rules	p. 48
Conditional Probability	p. 50
Independence	p. 52
Probability Models	p. 53
The Binomial Probability Model	p. 54
The Normal Probability Model	p. 57
Z Scores	p. 63
Glossary	p. 64
Exercises	p. 65
Random Sampling	p. 76
Obtaining Representative Data	p. 76
The Sampling Plan	p. 78
Probability Samples	p. 78
Commonly Used Sampling Plans	p. 80
Simple Random Sampling	p. 80
Stratified Random Sampling	p. 84
Cluster Sampling	p. 86
Systematic Sampling	p. 88
Determining the Sample Size	p. 89
The Sample Size for a Simple Random Sample	p. 89
The Sample Size for a Stratified Random Sample	p. 93
Determining the Sample Size in a Systematic Random Sample	p. 99
Glossary	p. 100
Exercises	p. 102
Summarizing Random Samples	p. 109
Samples and Inferential Statistics	p. 109
Inferential Graphical Statics	p. 110
Bar and Pie Charts	p. 111
Boxplots	p. 114
Histograms	p. 120
Normal Probability Plots	p. 126
Numerical Statistics for Univariate Data Sets	p. 129
Estimating Population Proportions	p. 129
Estimating Population Percentiles	p. 136
Estimating the Mean, Median, and Mode	p. 137
Estimating the Variance and Standard Deviation	p. 143
Linear Transformations	p. 148
The Plug-in Rule for Estimation	p. 151
Statistics for Multivariate Data Sets	p. 153
Graphical Statistics for Bivariate Data Sets	p. 154
Numerical Summaries for Bivariate Data Sets	p. 156
Fitting Lines to Scatterplots	p. 161
Glossary	p. 163
Exercises	p. 166
Measuring the Reliability of Statistics	p. 181
Sampling Distributions	p. 181
Unbiased Estimators	p. 183
Measuring the Accuracy of an Estimator	p. 184
The Bound on the Error of Estimation	p. 186
The Sampling Distribution of a Sample Proportion	p. 187
The Mean and Standard Deviation of the Sampling Distribution of &pcirc;	p. 187
Determining the Sample Size for a Prespecified Value of the Bound on the Error Estimation	p. 190
The Central Limit Theorem for &pcirc;	p. 191
Some Final Notes on the Sampling Distribution of &pcirc;	p. 192
The Sampling Distribution of &xbar;	p. 193
The Mean and Standard Deviation of the Sampling Distribution of &xbar;	p. 193
Determining the Sample Size for a Prespecified Value of the Bound on the Error Estimation	p. 196
The Central Limit Theorem for &xbar;	p. 197
The t Distribution	p. 199
Some Final Notes on the Sampling Distribution of &xbar;	p. 201
Comparisons Based on Two Samples	p. 202
Comparing Two Population Proportions	p. 203
Comparing Two Population Means	p. 209
Bootstrapping the Sampling Distribution of a Statistic	p. 215
Glossary	p. 218
Exercises	p. 219
Confidence Intervals	p. 229
Interval Estimation	p. 229
Confidence Intervals	p. 230
Single Sample Confidence Intervals	p. 232
Confidence Intervals for Proportions	p. 233
Confidence Intervals for a Mean	p. 236
Large Sample Confidence Intervals for µ	p. 237
Small Sample Confidence Intervals for µ	p. 238
Determining the Sample Size for a Confidence Interval for the Mean	p. 241
Bootstrap Confidence Intervals	p. 243
Two Sample Comparative Confidence Intervals	p. 244
Confidence Intervals for Comparing Two Proportions	p. 244
Confidence Intervals for the Relative Risk	p. 249
Glossary	p. 252
Exercises	p. 253
Testing Statistical Hypotheses	p. 265
Hypothesis Testing	p. 265
The Components of a Hypothesis Test	p. 265
P-Values and Significance Testing	p. 272
Testing Hypotheses about Proportions	p. 276
Single Sample Tests of a Population Proportion	p. 276
Comparing Two Population Proportions	p. 282
Tests of Independence	p. 287
Testing Hypotheses about Means	p. 295
t-Tests	p. 295
t-Tests for the Mean of a Population	p. 298
Paired Comparison t-Tests	p. 302
Two Independent Sample t-Tests	p. 307
Some Final Comments on Hypothesis Testing	p. 313
Glossary	p. 314
Exercises	p. 315
Simple Linear Regression	p. 333
Bivariate Data, Scatterplots, and Correlation	p. 333
Scatterplots	p. 333
Correlation	p. 336
The Simple Linear Regression Model	p. 340
The Simple Linear Regression Model	p. 341
Assumptions of the Simple Linear Regression Model	p. 343
Fitting a Simple Linear Regression Model	p. 344
Assessing the Assumptions and Fit of a Simple Linear Regression Model	p. 347
Residuals	p. 348
Residual Diagnostics	p. 348
Estimating and Assessing the Strength of the Linear Relationship	p. 355
Statistical Inferences based on a Fitted Model	p. 358
Inferences about ß₀	p. 359
Inferences about ß₁	p. 360
Inferences about the Response Variable	p. 363
Inferences About µy x	p. 363
Inferences for Predicting Values of Y	p. 365
Some Final Comments on Simple Linear Regression	p. 366
Glossary	p. 369
Exercises	p. 371
Multiple Regression	p. 383
Investigating Multivariate Relationships	p. 385
The Multiple Linear Regression Model	p. 387
The Assumptions of a Multiple Regression Model	p. 388
Fitting a Multiple Linear Regression Model	p. 390
Assessing the Assumptions of a Multiple Linear Regression Model	p. 390
Residual Diagnostics	p. 394
Detecting Multivariate Outliers and Influential Observations	p. 399
Assessing the Adequacy of Fit of a Multiple Regression Model	p. 401
Estimating ¿	p. 401
The Coefficient of Determination	p. 401
Multiple Regression Analysis of Variance	p. 403
Statistical Inferences-Based Multiple Regression Model	p. 406
Inferences about the Regression Coefficients	p. 406
Inferences about the Response Variable	p. 408
Comparing Multiple Regression Models	p. 410
Multiple Regression Models with Categorical Variables	p. 413
Regression Models with Dummy Variables	p. 415
Testing the Importance of Categorical Variables	p. 418
Variable Selection Techniques	p. 421
Model Selection Using Maximum R² _adj	p. 422
Model Selection using BIC	p. 424
Some Final Comments on Multiple Regression	p. 425
Glossary	p. 427
Exercises	p. 429
Logistic Regression	p. 446
Odds and Odds Ratios	p. 447
The Logistic Regression Model	p. 450
Assumptions of the Logistic Regression Model	p. 452
Fitting a Logistic Regression Model	p. 454
Assessing the Fit of a Logistic Regression Model	p. 456
Checking the Assumptions of a Logistic Regression Model	p. 456
Testing for the Goodness of Fit of a Logistic Regression Model	p. 458
Model Diagnostics	p. 459
Statistical Inferences Based on a Logistic Regression Model	p. 465
Inferences about the Logistic Regression Coefficients	p. 465
Comparing Models	p. 467
Variable Selection	p. 470
Some Final Comments on Logistic Regression	p. 473
Glossary	p. 474
Exercises	p. 476
Design Of Experiments	p. 487
Experiments versus Observational Studies	p. 487
The Basic Principles of Experimental Design	p. 490
Terminology	p. 490
Designing an Experiment	p. 491
Experimental Designs	p. 493
The Completely Randomized Design	p. 495
The Randomized Block Design	p. 498
Factorial Experiments	p. 500
Two-Factor Experiments	p. 502
Three-Factor Experiments	p. 504
Models for Designed Experiments	p. 506
The Model for a Completely Randomized Design	p. 506
The Model for a Randomized Block Design	p. 508
Models for Experimental Designs with a Factorial Treatment Structure	p. 509
Some Final Comments of Designed Experiments	p. 511
Glossary	p. 511
Exercises	p. 513
Analysis Of Variance	p. 520
Single-Factor Analysis of Variance	p. 521
Partitioning the Total Experimental Variation	p. 523
The Model Assumptions	p. 524
The F-test	p. 527
Comparing Treatment Means	p. 528
Randomized Block Analysis of Variance	p. 533
The ANOV Table for the Randomized Block Design	p. 534
The Model Assumptions	p. 536
The F-test	p. 538
Separating the Treatment Means	p. 539
Multifactor Analysis of Variance	p. 542
Two-factor Analysis of Variance	p. 542
Three-factor Analysis of Variance	p. 550
Selecting the Number of Replicates in Analysis of Variance	p. 555
Determining the Number of Replicates from the Power	p. 555
Determining the Number of Replicates from D	p. 556
Some Final Comments on Analysis of Variance	p. 557
Glossary	p. 558
Exercises	p. 559
Survival Analysis	p. 575
The Kaplan-Meier Estimate of the Survival Function	p. 576
The Proportional Hazards Model	p. 582
Logistic Regression and Survival Analysis	p. 586
Some Final Comments on Survival Analysis	p. 588
Glossary	p. 589
Exercises	p. 590
References	p. 599
	p. 605
Problem Solutions	p. 613
Index	p. 643
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Applied Biostatistics for the Health Sciences

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Applied Biostatistics for the Health Sciences

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