Bias and Causation: Models and Judgment for Valid Comparisons

Preface xi

1. What Is Bias? 1

1.1 Apples and Oranges, 2

1.2 Statistics vs. Causation, 3

1.3 Bias in the Real World, 6

Guidepost 1, 23

2. Causality and Comparative Studies 24

2.1 Bias and Causation, 24

2.2 Causality and Counterfactuals, 26

2.3 Why Counterfactuals? 32

2.4 Causal Effects, 33

2.5 Empirical Effects, 38

Guidepost 2, 46

3. Estimating Causal Effects 47

3.1 External Validity, 48

3.2 Measures of Empirical Effects, 50

3.3 Difference of Means, 52

3.4 Risk Difference and Risk Ratio, 55

3.5 Potential Outcomes, 57

3.6 Time-Dependent Outcomes, 60

3.7 Intermediate Variables, 63

3.8 Measurement of Exposure, 64

3.9 Measurement of the Outcome Value, 68

3.10 Confounding Bias, 70

Guidepost 3, 71

4. Varieties of Bias 72

4.1 Research Designs and Bias, 73

4.2 Bias in Biomedical Research, 81

4.3 Bias in Social Science Research, 85

4.4 Sources of Bias: A Proposed Taxonomy, 90

Guidepost 4, 92

5. Selection Bias 93

5.1 Selection Processes and Bias, 93

5.2 Traditional Selection Model: Dichotomous Outcome, 100

5.3 Causal Selection Model: Dichotomous Outcome, 102

5.4 Randomized Experiments, 104

5.5 Observational Cohort Studies, 108

5.6 Traditional Selection Model: Numerical Outcome, 111

5.7 Causal Selection Model: Numerical Outcome, 114

Guidepost 5, 121

Appendix, 122

6. Confounding: An Enigma? 126

6.1 What is the Real Problem? 127

6.2 Confounding and Extraneous Causes, 127

6.3 Confounding and Statistical Control, 131

6.4 Confounding and Comparability, 137

6.5 Confounding and the Assignment Mechanism, 139

6.6 Confounding and Model Specifi cation, 141

Guidepost 6, 144

7. Confounding: Essence, Correction, and Detection 145

7.1 Essence: The Nature of Confounding, 146

7.2 Correction: Statistical Control for Confounding, 172

7.3 Detection: Adequacy of Statistical Adjustment, 180

Guidepost 7, 191

Appendix, 192

8. Intermediate Causal Factors 195

8.1 Direct and Indirect Effects, 195

8.2 Principal Stratifi cation, 200

8.3 Noncompliance, 209

8.4 Attrition, 214

Guidepost 8, 215

9. Information Bias 217

9.1 Basic Concepts, 218

9.2 Classical Measurement Model: Dichotomous Outcome, 223

9.3 Causal Measurement Model: Dichotomous Outcome, 230

9.4 Classical Measurement Model: Numerical Outcome, 239

9.5 Causal Measurement Model: Numerical Outcome, 242

9.6 Covariates Measured with Error, 246

Guidepost 9, 250

10. Sources of Bias 252

10.1 Sampling, 254

10.2 Assignment, 260

10.3 Adherence, 266

10.4 Exposure Ascertainment, 269

10.5 Outcome Measurement, 273

Guidepost 10, 277

11. Contending with Bias 279

11.1 Conventional Solutions, 280

11.2 Standard Statistical Paradigm, 286

11.3 Toward a Broader Perspective, 288

11.4 Real-World Bias Revisited, 293

11.5 Statistics and Causation, 303

Glossary 309

Bibliography 321

Index 340