Causal inference, probability theory, and graphical insights

Statistics in Medicine

Volumn 32, Issue 25, 2013, Pages 4319-4330

  Baker, Stuart G ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

BK Plot; Causal graph; Confounder; Instrumental variable; Observational study; Simpson's paradox

Indexed keywords

ARTICLE; BINARY CONFOUNDER; BIOSTATISTICS; BK2 PLOT; CAUSAL INFERENCE; GRAPHICAL INSIGHT; INTERMETHOD COMPARISON; OBSERVATIONAL STUDY; PROBABILITY; SIMPSON PARADOX; STATISTICAL PARAMETERS; STUDY DESIGN; SYSTEM ANALYSIS; SYSTEMATIC ERROR; EPIDEMIOLOGY; HUMAN; METHODOLOGY; OBSERVATION; PROCEDURES; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS (EPIDEMIOLOGY); BIOSTATISTICS; CAUSALITY; HUMANS; MODELS, STATISTICAL; OBSERVATION; PROBABILITY THEORY; RESEARCH DESIGN;

EID: 84885175788     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5828     Document Type: Article

References (37)

1. Byar DP. Why data bases should not replace randomized clinical trials. Biometrics 1980; 35:337-342.
2. Pearl J. Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press: New York, NY, 2009.
3. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14:300-306.
4. Shrier D. Letter to the editor. Statistics in Medicine 2008; 27:2740-2741.
5. Rubin DB. Author's reply (to Ian Shrier's letter to the editor). Statistics in Medicine 2008; 27:2741-2742.
6. Shrier D. Letter to the editor: propensity scores. Statistics in Medicine 2009; 28:1317-1318.
7. Sjolander A. Letter to the editor: propensity scores and m-structures. Statistics in Medicine 2009; 28:1416-1420.
8. Pearl J. Letter to the editor: remarks on the method of propensity scores. Statistics in Medicine 2009; 28:1420-1423.
9. Rubin DB. Author's reply: Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups? Statistics in Medicine 2009; 28:1420-1423.
10. Liu W, Brookhart MA, Schneeweiss S, Mi X, Setoguchi S. Implications of M bias in epidemiologic studies: a simulation study. American Journal of Epidemiology 2012; 176:938-948.
11. Bhattacharya J, Vogt WB. Do instrumental variables belong in propensity scores? International Journal of Statistics and Economics 2012; 9:A12.
12. Pearl J. On a class of bias-amplifying variables that endanger effect estimates. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2010). Association for Uncertainty in Artificial Intelligence: Corvallis, 2010; 425-432.
13. Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Joffe MM, Glynn RJ. Effects of adjusting for instrumental variables on bias and precision of effect estimates. American Journal of Epidemiology 2011; 174:1213-1222.
14. Pearl J. Invited commentary: understanding bias amplification. American Journal of Epidemiology 2011; 174:1223-1227.
15. Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Glynn RJ. Myers et al. response to "understanding bias amplification". American Journal of Epidemiology 2011; 174:1228-1229.
16. VanderWeele TJ, Shpitser I. A new criterion for confounder selection. Biometrics 2011; 67:1406-1413.
17. Breslow NE, Day NE. Statistical Methods in Cancer Research. International Agency for Research on Cancer: Lyon, 1980. p 104.
18. Baker SG, Lindeman KS. Revisiting a discrepant result: a propensity score analysis, the paired availability design for historical controls, and a meta-analysis of randomized trials. Journal of Causal Inference 2013, DOI: 10.1515/jci-2013-0005.
19. Baker SG, Lindeman KS. The paired availability design: a proposal for evaluating epidural analgesia during labor. Statistics in Medicine 1994; 13:2269-2278.
20. Frangakis CE, Rubin DB. Principle stratification in causal inference. Biometrics 2002; 58:21-29.
21. Gardner M. Martin Gardner's Sixth Book of Mathematical Games from Scientific American. W.H. Freeman and Company: San Francisco, 1971. p 154.
22. Maor E. Trignometric Delights. Princeton University Press: Princeton, 1998. pp 122-123.
23. Soto AM, Sonnenschein C, Miquel PA. On physicalism and downward causation in developmental and cancer biology. Acta Biotheoretica 2008; 56(4):257-274.
24. Prehn RT. Cancers beget mutations versus mutations beget cancer. Cancer Research 1994; 54:5296-5300.
25. Baker SG. Paradoxes in carcinogenesis should spur new avenues of research: an historical perspective. Disruptive Science and Technology 2012; 1:100-107.
26. Baker SG. Paradox-driven cancer research. Disruptive Science and Technology 2013; 1:143-148.
27. Baker SG, Kramer BS. Good for women, good for men, bad for people: Simpson's paradox and the importance of sex-specific analysis in observational studies. Journal of Women's Health & Gender-Based Medicine 2001; 10:867-872.
28. Hernán MA, Clayton D, Keiding N. The Simpson's paradox unravelled. International Journal of Epidemiology 2011; 40:780-785.
29. Arah O. The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: covariate selection in the analysis of observational studies. Emerging Themes in Epidemiology 2008; 5:5.
30. Jeon JW, Chung HY, Bae JS. Chances of Simpson's paradox. Journal of the Korean Statistical Society 1987; 16:117-125.
31. Wainer H. The BK-Plot: making Simpson's paradox clear to the masses. Chance 2002; 15:60-62.
32. Baker SG, Freedman LS. A simple method for analyzing data from a randomized trial with a missing binary outcome. BMC Medical Research Methodology 2003; 3:8.
33. Baker SG, Kramer BS. The transitive fallacy for randomized trials: if A bests B and B bests C in separate trials, is A better than C. BMC Medical Research Methodology 2002; 2:13.
34. Baker SG, Kramer BS. Surrogate endpoint analysis: an exercise in extrapolation. Journal of the National Cancer Institute 2013; 105:316-320.
35. Baker SG, Kramer BS. Randomized trials, generalizability, and meta-analysis: graphical insights for binary outcomes. BMC Medical Research Methodology 2003; 3:10.
36. Gail MH, Wieand S, Piantadosie S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 1984; 71:431-444.
37. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Statistical Science 1999; 14:29-46.