Statistical Remedies For Medical Researchers

Preface......Page 7
Contents......Page 9
1 Why Bother with Statistics?......Page 12
1.1 Some Unexpected Problems......Page 13
1.2 Expert Opinion......Page 14
1.3 The Innocent Bystander Effect......Page 16
1.4 Gambling and Medicine......Page 20
1.5 Testing Positive......Page 23
1.6 Bayes' Law and Hemophilia......Page 27
2 Frequentists and Bayesians......Page 30
2.1 Statistical Inference......Page 31
2.2 Frequentist Statistics......Page 34
2.3 Bayesian Statistics......Page 39
3 Knocking Down the Straw Man......Page 50
3.1 Designing Clinical Trials......Page 51
3.2 A Common Phase II Design......Page 53
3.3 A Common Misinterpretation......Page 56
3.4 Not Testing Hypotheses......Page 58
3.5 Random Standards......Page 61
3.6 A Fake Null Hypothesis......Page 62
3.7 Monitoring Toxicity and Response......Page 66
4 Science and Belief......Page 70
4.1 Theory Versus Practice......Page 71
4.2 Technology and Cherry-Picking......Page 74
4.3 Is a New Treatment Any Good?......Page 79
4.4 The Las Vegas Effect......Page 82
5 The Perils of P-Values......Page 86
5.1 Counting Cows......Page 87
5.2 A Sacred Ritual......Page 88
5.3 A Dataset with Four P-Values......Page 93
5.4 Bayes Factors......Page 95
5.5 Computing Sample Sizes......Page 97
5.6 Not-So-Equivalent Studies......Page 101
5.7 Acute Respiratory Distress Syndrome......Page 102
5.8 The Multiple Testing Problem......Page 106
5.9 Type S Error......Page 113
5.10 A Simple Bayesian Alternative to P-Values......Page 115
5.11 Survival Analysis Without P-Values......Page 118
5.12 The P-Value War......Page 120
6 Flipping Coins......Page 125
6.1 Farming and Medicine......Page 126
6.2 How Not to Compare Treatments......Page 127
6.3 Counterfactuals and Causality......Page 129
6.4 Why Randomize?......Page 134
6.5 Stratifying by Subgroups......Page 136
6.6 Inverse Probability-Weighted Survival Analysis......Page 139
6.7 Bias Correction by Matching......Page 143
6.8 A Bayesian Rationale for Randomization......Page 148
6.9 Outcome-Adaptive Randomization......Page 150
7 All Mixed Up......Page 159
7.1 The Billion Dollar Computation......Page 160
7.2 Accounting for Uncertainty and Bias......Page 164
7.3 Predicting Phase III Success......Page 165
7.4 A Paradoxical Clinical Trial......Page 168
8 Sex, Biomarkers, and Paradoxes......Page 173
8.1 A Paradox......Page 174
8.2 Batting Averages......Page 176
8.3 A Magic Biomarker......Page 177
8.4 Plotting Regression Data......Page 181
9 Crippling New Treatments......Page 192
9.1 Phase I Trials......Page 193
9.2 Choosing the Wrong Dose in Phase I......Page 197
9.3 Phase I–II Designs......Page 204
10 Just Plain Wrong......Page 210
10.1 Clinical Trial Design, Belief, and Ethics......Page 211
10.2 A Futile Futility Rule......Page 212
10.3 The Evaluability Game......Page 216
10.4 The Fox, the Farmer, and the Chickens......Page 220
10.5 Planned Confounding......Page 222
10.6 Select-and-Test Designs......Page 227
11 Getting Personal......Page 233
11.1 From Bench to Bedside......Page 234
11.2 Age Discrimination......Page 236
11.3 Comparing Treatments Precisely......Page 240
11.4 A Subgroup-Specific Phase II–III Design......Page 245
11.5 Precision Pharmacokinetic Dosing......Page 247
12 Multistage Treatment Regimes......Page 254
12.1 The Triangle of Death......Page 255
12.2 Observe, Act, Repeat......Page 262
12.3 SMART Designs......Page 265
12.4 Repeat or Switch Away......Page 271
12.5 A Semi-SMART Design......Page 277
BookmarkTitle:......Page 284
Index......Page 295