Clinical Medicine (8th Edition)

Description

Featuring updated content throughout, this new edition of Clinical Medicine Lecture Notes is a concise guide to both history taking and examination, and to the essentials of clinical medicine on a system-by-system basis.

The text is divided into two sections, with part one exploring communication and physical examination techniques, supported by the core knowledge required for assessing and diagnosing diseases in the main systems of the body. The second part of the text covers a range of common diseases, although accounts of rare conditions are also given. The level of information provided will equip junior clinicians with the necessary knowledge required to succeed in any clinical situation.

• A concise approach that contains all that medical students and junior doctors need to know, covering both the clinical approach and the essential background knowledge
• Summary and evidence-based medicine boxes to assist revision and learning
• Includes OSCE exam summaries
• Fully updated content throughout, with full colour illustrations and photographs

Whether you need to develop your knowledge for clinical practice, or refresh that knowledge in the run up to examinations, Clinical Medicine Lecture Notes will help foster a systematic approach to the clinical situation for all medical students and junior doctors.

Table of Contents

1.  Data and Case Studies 1
   1. Case Study: Flight Delays 1
   2. Case Study: BirthWeights of Babies 2
   3. Case Study: Verizon Repair Times 3
   4. Case Study: Iowa Recidivism 4
   5. Sampling 5
   6. Parameters and Statistics 6
   7. Case Study: General Social Survey 7
   8. Sample Surveys 8
   9. Case Study: Beer and HotWings 9
   10. Case Study: Black Spruce Seedlings 10
   11. Studies 10
   12. Google Interview Question: Mobile Ads Optimization 12
   13. Exercises 16
2. Exploratory Data Analysis 21
   1. Basic Plots 21
   2. Numeric Summaries 25
   3. Center 25
   4. Spread 26
   5. Shape 27
   6. Boxplots 28
   7. Quantiles and Normal Quantile Plots 29
   8. Empirical Cumulative Distribution Functions 35
   9. Scatter Plots 38
   10. Skewness and Kurtosis 40
3. Introduction to Hypothesis Testing: Permutation Tests 47
   1. Introduction to Hypothesis Testing 47
   2. Hypotheses 48
   3. Permutation Tests 50
   4. Implementation Issues 55
   5. One-sided and Two-sided Tests 61
   6. Other Statistics 62
   7. Assumptions 64
   8. Remark on Terminology 68
   9. Matched Pairs 68
   10. Exercises 70
4. Sampling Distributions 75
   1. Sampling Distributions 75
   2. Calculating Sampling Distributions 80
   3. The Central LimitTheorem 84
   4. CLT for Binomial Data 86
   5. Continuity Correction for Discrete Random Variables 89
   6. Accuracy of the Central Limit Theorem∗ 91
   7. CLT for SamplingWithout Replacement 92
   8. Exercises 93
5. Introduction to Confidence Intervals: The Bootstrap 103
   1. Introduction to the Bootstrap 103
   2. The Plug-in Principle 110
   3. Estimating the Population Distribution 112
   4. How Useful Is the Bootstrap Distribution? 113
   5. Bootstrap Percentile Intervals 118
   6. Two-Sample Bootstrap 119
   7. Matched Pairs 124
   8. Other Statistics 128
   9. Bias 131
   10. Monte Carlo Sampling: The “Second Bootstrap Principle” 134
   11. Accuracy of Bootstrap Distributions 135
   12. Sample Mean: Large Sample Size 135
   13. Sample Mean: Small Sample Size 137
   14. Sample Median 138
   15. Mean–Variance Relationship 138
   16. HowMany Bootstrap Samples Are Needed? 140
   17. Exercises 141
6. Estimation 149
   1. Maximum Likelihood Estimation 149
   2. Maximum Likelihood for Discrete Distributions 150
   3. Maximum Likelihood for Continuous Distributions 153
   4. Maximum Likelihood for Multiple Parameters 157
   5. Method of Moments 161
   6. Properties of Estimators 163
   7. Unbiasedness 164
   8. Efficiency 167
   9. Mean Square Error 171
   10. Consistency 173
   11. Transformation Invariance∗ 175
   12. Asymptotic Normality of MLE∗ 177
   13. Statistical Practice 178
   14. Are You Asking the Right Question? 179
   15. Weights 179
   16. Exercises 180
7. More Confidence Intervals 187
   1. Confidence Intervals for Means 187
   2. Confidence Intervals for a Mean, Variance Known 187
   3. Confidence Intervals for a Mean, Variance Unknown 192
   4. Confidence Intervals for a Difference in Means 198
   5. Matched Pairs, Revisited 204
   6. Confidence Intervals in General 204
   7. Location and Scale Parameters∗ 208
   8. One-sided Confidence Intervals 212
   9. Confidence Intervals for Proportions 214
   10. Agresti–Coull Intervals for a Proportion 217
   11. Confidence Intervals for a Difference of Proportions 218
   12. Bootstrap Confidence Intervals 219
   13. t Confidence Intervals Using Bootstrap Standard Errors 219
   14. Bootstrap t Confidence Intervals 220
   15. Comparing Bootstrap t and Formula t Confidence Intervals 224
   16. Confidence Interval Properties 226
   17. Confidence Interval Accuracy 226
   18. Confidence Interval Length 227
   19. Transformation Invariance 227
   20. Ease of Use and Interpretation 227
   21. Research Needed 228
   22. Exercises 228
8. More Hypothesis Testing 241
   1. Hypothesis Tests for Means and Proportions: One Population 241
   2. A Single Mean 241
   3. One Proportion 244
   4. Bootstrap t-Tests 246
   5. Hypothesis Tests for Means and Proportions: Two Populations 248
   6. Comparing Two Means 248
   7. Comparing Two Proportions 251
   8. Matched Pairs for Proportions 254
   9. Type I and Type II Errors 255
   10. Type I Errors 257
   11. Type II Errors and Power 261
   12. P-Values versus Critical Regions 266
   13. Interpreting Test Results 267
   14. P-Values 267
   15. On Significance 268
   16. Adjustments for Multiple Testing 269
   17. Likelihood Ratio Tests 271
   18. Simple Hypotheses and the Neyman–Pearson Lemma 271
   19. Likelihood Ratio Tests for Composite Hypotheses 275
   20. Statistical Practice 279
   21. More Campaigns with No Clicks and No Conversions 284
   22. Exercises 285
9. Regression 297
   1. Covariance 297
   2. Correlation 301
   3. Least-Squares Regression 304
   4. Regression Toward the Mean 308
   5. Variation 310
   6. Diagnostics 311
   7. Multiple Regression 317
   8. The Simple LinearModel 317
   9. Inference for 𝛼 and 𝛽 322
   10. Inference for the Response 326
   11. Comments about Assumptions for the Linear Model 330
   12. Resampling Correlation and Regression 332
   13. Permutation Tests 335
   14. Bootstrap Case Study: Bushmeat 336
   15. Logistic Regression 340
   16. Inference for Logistic Regression 346
   17. Exercises 350
10. Categorical Data 359
    1. Independence in Contingency Tables 359
    2. Permutation Test of Independence 361
    3. Chi-square Test of Independence 365
    4. Model for Chi-square Test of Independence 366
    5. 2 × 2 Tables 368
    6. Fisher’s Exact Test 370
    7. Conditioning 371
    8. Chi-square Test of Homogeneity 372
    9. Goodness-of-fit Tests 374
    10. All Parameters Known 374
    11. Some Parameters Estimated 377
    12. Chi-square and the Likelihood Ratio∗ 379
    13. Exercises 380
11. Bayesian Methods 391
    1. Bayes Theorem 392
    2. Binomial Data: Discrete Prior Distributions 392
    3. Binomial Data: Continuous Prior Distributions 400
    4. Continuous Data 406
    5. Sequential Data 409
    6. Exercises 414
12. One-way ANOVA 419
    1. Comparing Three or More Populations 419
    2. The ANOVA F-test 419
    3. A Permutation Test Approach 428
    4. Exercises 429
13. Additional Topics 433
    1. Smoothed Bootstrap 433
    2. Kernel Density Estimate 435
    3. Parametric Bootstrap 437
    4. The Delta Method 441
    5. Stratified Sampling 445
    6. Computational Issues in Bayesian Analysis 446
    7. Monte Carlo Integration 448
    8. Importance Sampling 452
    9. Ratio Estimate for Importance Sampling 458
    10. Importance Sampling in Bayesian Applications 461
    11. The EM Algorithm 467
    12. General Background 469
    13. Exercises 472
14. Appendix A Review of Probability 477
    1. Basic Probability 477
    2. Mean and Variance 478
    3. The Normal Distribution 480
    4. The Mean of a Sample of RandomVariables 481
    5. Sums of Normal Random Variables 482
    6. The Law of Averages 483
    7. Higher Moments and the Moment-generating Function 484
15. Appendix B Probability Distributions 487
    1. The Bernoulli and Binomial Distributions 487
    2. The Multinomial Distribution 488
    3. The Geometric Distribution 490
    4. The Negative Binomial Distribution 491
    5. The Hypergeometric Distribution 492
    6. The Poisson Distribution 493
    7. The Uniform Distribution 495
    8. The Exponential Distribution 495
    9. The Gamma Distribution 497
    10. The Chi-square Distribution 499
    11. The Student’s t Distribution 502
    12. The Beta Distribution 504
    13. The F Distribution 505
    14. Exercises 507
16. Appendix C Distributions Quick Reference 509
    1. Solutions to Selected Exercises 513
    2. References 525
    3. Index 531

Author Biography

John R. Bradley, CBE MA DM FRCP, Consultant Physician and Honorary Professor of Experimental Medicine, University of Cambridge School of Clinical Medicine, Cambridge University Hospitals, Cambridge

Mark Gurnell, MA (MedEd) PhD FAcadMEd FRCP, Clinical SubDean, Senior Lecturer and Honorary Consultant Physician, University of Cambridge School of Clinical Medicine, Cambridge University Hospitals, Cambridge

Diana F. Wood, MA MD FRCP, Director of Medical Education, Clinical Dean and Honorary Consultant Physician, University of Cambridge School of Clinical Medicine, Cambridge University Hospitals, Cambridge