Main 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.

Contents:

1 Data and Case Studies 1

1.1 Case Study: Flight Delays 1

1.2 Case Study: BirthWeights of Babies 2

1.3 Case Study: Verizon Repair Times 3

1.4 Case Study: Iowa Recidivism 4

1.5 Sampling 5

1.6 Parameters and Statistics 6

1.7 Case Study: General Social Survey 7

1.8 Sample Surveys 8

1.9 Case Study: Beer and HotWings 9

1.10 Case Study: Black Spruce Seedlings 10

1.11 Studies 10

1.12 Google Interview Question: Mobile Ads Optimization 12

Exercises 16

2 Exploratory Data Analysis 21

2.1 Basic Plots 21

2.2 Numeric Summaries 25

2.2.1 Center 25

2.2.2 Spread 26

2.2.3 Shape 27

2.3 Boxplots 28

2.4 Quantiles and Normal Quantile Plots 29

2.5 Empirical Cumulative Distribution Functions 35

2.6 Scatter Plots 38

2.7 Skewness and Kurtosis 40

3 Introduction to Hypothesis Testing: Permutation Tests 47

3.1 Introduction to Hypothesis Testing 47

3.2 Hypotheses 48

3.3 Permutation Tests 50

3.3.1 Implementation Issues 55

3.3.2 One-sided and Two-sided Tests 61

3.3.3 Other Statistics 62

3.3.4 Assumptions 64

3.3.5 Remark on Terminology 68

3.4 Matched Pairs 68

Exercises 70

4 Sampling Distributions 75

4.1 Sampling Distributions 75

4.2 Calculating Sampling Distributions 80

4.3 The Central LimitTheorem 84

4.3.1 CLT for Binomial Data 86

4.3.2 Continuity Correction for Discrete Random Variables 89

4.3.3 Accuracy of the Central Limit Theorem 91

4.3.4 CLT for SamplingWithout Replacement 92

Exercises 93

5 Introduction to Confidence Intervals: The Bootstrap 103

5.1 Introduction to the Bootstrap 103

5.2 The Plug-in Principle 110

5.2.1 Estimating the Population Distribution 112

5.2.2 How Useful Is the Bootstrap Distribution? 113

5.3 Bootstrap Percentile Intervals 118

5.4 Two-Sample Bootstrap 119

5.4.1 Matched Pairs 124

5.5 Other Statistics 128

5.6 Bias 131

5.7 Monte Carlo Sampling: The "Second Bootstrap Principle" 134

5.8 Accuracy of Bootstrap Distributions 135

5.8.1 Sample Mean: Large Sample Size 135

5.8.2 Sample Mean: Small Sample Size 137

5.8.3 Sample Median 138

5.8.4 Mean-Variance Relationship 138

5.9 HowMany Bootstrap Samples Are Needed? 140

Exercises 141

6 Estimation 149

6.1 Maximum Likelihood Estimation 149

6.1.1 Maximum Likelihood for Discrete Distributions 150

6.1.2 Maximum Likelihood for Continuous Distributions 153

6.1.3 Maximum Likelihood for Multiple Parameters 157

6.2 Method of Moments 161

6.3 Properties of Estimators 163

6.3.1 Unbiasedness 164

6.3.2 Efficiency 167

6.3.3 Mean Square Error 171

6.3.4 Consistency 173

6.3.5 Transformation Invariance 175

6.3.6 Asymptotic Normality of MLE 177

6.4 Statistical Practice 178

6.4.1 Are You Asking the Right Question? 179

6.4.2 Weights 179

Exercises 180

7 More Confidence Intervals 187

7.1 Confidence Intervals for Means 187

7.1.1 Confidence Intervals for a Mean, Variance Known 187

7.1.2 Confidence Intervals for a Mean, Variance Unknown 192

7.1.3 Confidence Intervals for a Difference in Means 198

7.1.4 Matched Pairs, Revisited 204

7.2 Confidence Intervals in General 204

7.2.1 Location and Scale Parameters 208

7.3 One-sided Confidence Intervals 212

7.4 Confidence Intervals for Proportions 214

7.4.1 Agresti-Coull Intervals for a Proportion 217

7.4.2 Confidence Intervals for a Difference of Proportions 218

7.5 Bootstrap Confidence Intervals 219

7.5.1 t Confidence Intervals Using Bootstrap Standard Errors 219

7.5.2 Bootstrap t Confidence Intervals 220

7.5.3 Comparing Bootstrap t and Formula t Confidence Intervals 224

7.6 Confidence Interval Properties 226

7.6.1 Confidence Interval Accuracy 226

7.6.2 Confidence Interval Length 227

7.6.3 Transformation Invariance 227

7.6.4 Ease of Use and Interpretation 227

7.6.5 Research Needed 228

Exercises 228

8 More Hypothesis Testing 241

8.1 Hypothesis Tests for Means and Proportions: One Population 241

8.1.1 A Single Mean 241

8.1.2 One Proportion 244

8.2 Bootstrap t-Tests 246

8.3 Hypothesis Tests for Means and Proportions: Two Populations 248

8.3.1 Comparing Two Means 248

8.3.2 Comparing Two Proportions 251

8.3.3 Matched Pairs for Proportions 254

8.4 Type I and Type II Errors 255

8.4.1 Type I Errors 257

8.4.2 Type II Errors and Power 261

8.4.3 P-Values versus Critical Regions 266

8.5 Interpreting Test Results 267

8.5.1 P-Values 267

8.5.2 On Significance 268

8.5.3 Adjustments for Multiple Testing 269

8.6 Likelihood Ratio Tests 271

8.6.1 Simple Hypotheses and the Neyman-Pearson Lemma 271

8.6.2 Likelihood Ratio Tests for Composite Hypotheses 275

8.7 Statistical Practice 279

8.7.1 More Campaigns with No Clicks and No Conversions 284

Exercises 285

9 Regression 297

9.1 Covariance 297

9.2 Correlation 301

9.3 Least-Squares Regression 304

9.3.1 Regression Toward the Mean 308

9.3.2 Variation 310

9.3.3 Diagnostics 311

9.3.4 Multiple Regression 317

9.4 The Simple LinearModel 317

9.4.1 Inference for