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…mehr
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.
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
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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
