Bias and Causation: Models and Judgment for Valid Comparisons

Bias and Causation: Models and Judgment for Valid Comparisons

Volumn , Issue , 2010, Pages 1-348

  Weisberg, Herbert I ^a  

^a Correlation Research Inc ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84870405711     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470631102     Document Type: Book

References (357)

1. Abbott, E. A. (1884). Flatland: A Romance of Many Dimensions. London: Seeley & Company.
2. Allard, R., and Boivin, J.-F. (2007). Using causal models to show the effect of untestable assumptions on effect estimates in randomized controlled trials. Clinical Trials, 4, 611-620.
3. Al-Shahi, R., Vousden, C., and Warlow, C. (2005). Bias from requiring explicit consent from all participants in observational research: prospective population based study. British Medical Journal, 331, 942-947.
4. Amery, W., and Dony, J. (1975). A clinical trial design avoiding undue placebo treatment. Journal of Clinical Pharmacology, 10, 674-679.
5. Anderson, S., Auquier, A., Hauck, W., Oakes, D., Vandaele, W., and Weisberg, H. (1980). Statistical Methods for Comparative Studies. New York: John Wiley & Sons.
6. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables (with discussion). Journal of the American Statistical Association, 91, 444-472.
7. Antithrombotic Trialists Collaboration (ATT). (2009). Aspirin in the primary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials. Lancet, 373, 1849-1860.
8. Austin, P. C. (2008). A critical appraisal of propensity-score matching in the medical literature (with discussion). Statistics in Medicine, 27, 2037-2049.
9. Baldus, D. C., Woodworth, G. G., and Pulaski, C. A. (1990). Equal Justice and the Death Penalty: A Legal and Empirical Analysis. Boston: Northeastern University Press.
10. Barnard, J., Frangakis, C. E., Hill, J. L., and Rubin, D. B. (2003). Principal stratification approach to broken randomized experiments: a case study of school choice vouchers in New York City. Journal of the American Statistical Association, 98, 299-323.
11. Barnow, B. (1987). The impact of CETA programs on earnings. Journal of Human Resources, 22, 157-193.
12. Baron, R. M., and Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
13. Barrett-Connor, E. (1998). Hormone replacement therapy. British Medical Journal, 317, 457-461.
14. Barrett-Connor, E., and Grady, D. (1998). Hormone replacement therapy, heart disease and other considerations. Annual Review of Public Health, 19, 55-72.
15. Barron, B. A. (1977). The effects of misclassification on the estimation of relative risk. Biometrics, 33, 414-418.
16. Becher, H. (1992). The concept of residual confounding in regression models and some applications. Statistics in Medicine, 11, 1747-1758.
17. Bell, J. (1998). The new genetics in clinical practice. British Medical Journal, 316, 618-620.
18. Berger, V. W. (2005). Selection Bias and Covariate Imbalances in Randomized Clinical Trials. Chichester: John Wiley & Sons.
19. Berk, R. A. (1983). Introduction to sample selection bias in sociological data. American Sociological Review, 48, 386-398.
20. Berk, R. A., and Ray, S. C. (1982). Selection biases in sociological data. Social Sciences Review, 11, 352-398
21. Berkson, J. (1946). Limitations of the application of fourfold table analysis to hospital data. Biometrics Bulletin, 2, 47-53.
22. Bitler, M. P., Gelbach, J. B., and Hoynes, H. W. (2006). What mean impacts miss: distributional effects of welfare reform experiments. American Economic Review, 96, 988-1012.
23. Bodmer, W. (2003). RA Fisher, statistician and geneticist extraordinary: a personal view. International Journal of Epidemiology, 32, 938-942.
24. Boissel, J. (1998). Individualizing aspirin therapy for prevention of cardiovascular events. Journal of the American Medical Association, 280, 1949-1950.
25. Boissel, J., Collet, J., Lievre, M., and Girard, P. (1993). An effect model for the assessment of drug benefit: example of antiarrhythmic drugs in postmyocardial infarction patients. Journal of Cardiovascular Pharmacology, 22, 356-363.
26. Box, G. E. P., Hunter, J. S., and Hunter, W. G. (1978). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. New York: John Wiley & Sons.
27. Brenner, H. (1996). How independent are multiple " independent " diagnostic classifications? Statistics in Medicine, 15, 1377-1386.
28. Bridge, J. A., Barbe, R. P., Birmaher, B., Kolko, D. J., and Brent, D. A. (2005). Emergent suicidality in a clinical psychotherapy trial for adolescent depression. American Journal of Psychiatry, 162, 2173-2175.
29. Bridge, J. A., Iyengar, S., Salary, C. B., Barbe, S. P., Birmaher, B., Pincus, H. A., Ren, L., and Brent, D. A. (2007). Clinical response and risk for reported suicide ideation and suicide attempts in pediatric antidepressant treatment. Journal of the American Medical Association, 297, 1683-1696.
30. Brittain, E., and Wittes, J. (1990). The run-in period in clinical trials: the effect of misclassification on efficacy. Controlled Clinical Trials, 11, 327-338.
31. Britton, A., McKee, M., Black, N., McPherson, K., Sanderson, C., and Bain, C. (1999). Threats to applicability of randomised trials: exclusions and selective participation. Journal of Health Services Research Policy, 4, 112-121.
32. Bross, I. D. (1954). Misclassification in 2 ×2 tables. Biometrics, 10, 478-486.
33. Bryk, A. S., and Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage Publications.
34. Bryk, A. S., and Weisberg, H. I. (1977). Use of the nonequivalent control group design when subjects are growing. Psychological Bulletin, 84, 950-962.
35. Bryk, A. S., Lee, V. E., and Holland, P. B. (1993). Catholic Schools and the Common Good. Cambridge, MA: Harvard University Press.
36. Burtless, G., and Orr, L. (1986). Are classical experiments needed for manpower policy? Journal of Human Resources, 21, 606-639.
37. Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54, 297-312.
38. Campbell, D. T. (1969). Reforms as experiments. American Psychologist, 24, 409-429.
39. Campbell, D. T., and Kenny, D. A. (1999). A Primer on Regression Artifacts. New York:The Guilford Press.
40. Campbell, D. T., and Stanley, J. C. (1963). Experimental and Quasi-experimental Designs for Research. Chicago: Rand McNally.
41. Chalmers, T. C. (1975). Effects of ascorbic acid on the common cold: an evaluation of the evidence. American Journal of Medicine, 58, 532-536.
42. Charlton, B. (1996). The scope and nature of epidemiology. Journal of Clinical Epidemiology, 49, 623-626.
43. Chin, R., and Lee, B. Y. (2008). Principles and Practice of Clinical Trial Medicine. London: Elsevier.
44. Choi, B. (2000). Bias, overview. In M. Gail and J. Benichou (Eds.), Encyclopedia of Epidemiologic Methods (pp. 74-82). Chichester: John Wiley & Sons.
45. Choi, B., and Noseworthy, A. (1992). Classification, detection and prevention of bias in epidemiologic research. Journal of Occupational Medicine, 34, 265-271.
46. Christenfeld, N. J., Sloan, R. P., Carrol, D., and Greenland, S. (2004). Risk factors, confounding and the illusion of control. Psychosomatic Medicine, 66, 868-875.
47. Cvjetanovic, B. B. (1957). Field trials of typhoid vaccines. American Journal of Public Health, 47, 578-581.
48. Cochran, W. G. (1965a). Sampling Techniques (3rd ed.). New York: John Wiley & Sons.
49. Cochran, W. G. (1965b). The planning of observational studies of human populations. Journal of the Royal Statistical Society (Series A), 128, 234-266.
50. Cochran, W. G. (1968a). Errors of measurement in statistics. Technometrics, 10, 637-666.
51. Cochran, W. G. (1968b). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics, 24, 295-313.
52. Cochran, W. G., and Cox, G. M. (1957). Experimental Designs (2nd ed.). New York: John Wiley & Sons.
53. Cockburn, W. C. (1955). The early history of typhoid vaccination. Journal of the Royal Army Medical Corps, 101, 171-185.
54. Cole, P. (1979). The evolving case-control study (with comment by E. D. Acheson and discussion). Journal of Chronic Diseases, 32, 15-34.
55. Cook, R. J., and Wei, W. (2002). Selection effects in randomized trials with count data. Statistics in Medicine, 21, 515-531.
56. Cook, T. D., and Campbell, D. T. (1979). Quasi-experimentation: Design and Analysis Issues for Field Settings. Boston: Houghton Mifflin.
57. Copeland, K. T., Checkoway, H., Holbrook, H. R., and McMichael, A. J. (1977). Bias due to misclassification in the estimation of relative risk. American Journal of Epidemiology, 105, 488-495.
58. Cornfield, J. (1951). A method of estimating comparative rates from clinical data:applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute, 11, 1269-1275.
59. Cox, D. R. (1958). Planning of Experiments. New York: John Wiley & Sons.
60. Cox, D. R. (1972). Regression models and life-tables (with discussion). Journal of the Royal Statistical Society (Series B), 34, 187-220.
61. Cox, D. R., and Oakes, D. (1984). Analysis of Survival Data. London: Chapman & Hall.
62. Cox, D. R., and Wermuth, N. (2004). Causality: a statistical view. International Statistical Review, 72, 285-305.
63. Cronbach, L. J. (1970). Essentials of Psychological Testing. New York: Harper & Row.
64. Cronbach, L. J. (1982). Designing Evaluations of Educational and Social Programs. San Francisco: Jossey-Bass.
65. Cronbach, L. J., and Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
66. Cuddleback, G., Wilson, E., Orme, J. G., and Combs-Orme, T. (2004). Detecting and statistically correcting sample selection bias. Journal of Social Services Research, 30, 19-33.
67. D'Agostino, R. B. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265-2281.
68. Dawid, A. P. (2000). Causal inferences without counterfactuals (with discussion). Journal of the American Statistical Association, 95, 407-448.
69. Deeks, J. J., Dinnes, J., D'Amico, R., Sowden, A. J., Sakarovitch, C., Song, F., Petticrew, M., and Altman, D. G. (2003). Evaluating non-randomised intervention studies. Health Technology Assessment, 7, 1-173.
70. Dehejia, R. H. (2005). Practical propensity score matching: a reply to Smith and Todd. Journal of Econometrics, 125, 355-364.
71. Dehejia, R. H., and Wahba, S. (1999). Causal effects in nonexperimental studies:reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053-1062.
72. Delgado-Rodriguez, M., and Llorca, J. (2004). Bias. Journal of Epidemiology and Community Health, 58, 635-641.
73. Dempster, A. P. (2002). John W. Tukey as " Philosopher. " Annals of Statistics, 30, 1619-1628.
74. Dickinson, K., and Maynard, R. (1981). The Impact of Supported Work on Ex-Addicts. New York: Manpower Demonstration Research Corporation.
75. Dubicka, B., and Goodyer, I. (2005). Should we prescribe antidepressants to children? Psychiatric Bulletin, 29, 164-167.
76. Dubicka, B., Hadley, S., and Roberts, C. (2006). Suicidal behavior in youths with depression treated with new generation antidepressants. British Journal of Psychiatry, 189, 393-398.
77. Elashoff, J. D. (1969). Analysis of covariance: a delicate instrument. American Educational Research Journal, 6, 383-402.
78. Ellenberg, J. H. (1994). Selection bias in observational and experimental studies. Statistics in Medicine, 13, 557-567.
79. Feinstein, A. R. (1973). Clinical biostatistics, XX: the epidemiologic trohoc, the ablative risk ratio, and " retrospective " research. Clinical Pharmacology and Therapeutics, 14, 291-307.
80. Feinstein, A. R. (1985). Clinical Epidemiology: The Architecture of Clinical Research. Philadelphia: WB Saunders Company.
81. Feinstein, A. R., Walter, S. D., and Horwitz, R. I. (1986). An analysis of Berkson's bias in case-control studies. Journal of Chronic Diseases, 39, 495-504.
82. Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver & Boyd.
83. Fisher, R. A. (1959). Statistical Methods and Scientific Inference (2nd ed.) Edinburgh:Oliver & Boyd.
84. Flanders, W. D. (2006). On the relationship of sufficient component cause models with potential outcome (counterfactual) models (with discussion). European Journal of Epidemiology, 21, 855-878.
85. Flanders, W. D., Boyle, C. A., and Boring, J. R. (1989). Bias associated with differential hospitalization rates in incident case-control studies. Journal of Clinical Epidemiology, 42, 395-401.
86. Flegal, K. M., Browne, C., and Haas, J. D. (1986). The effects of exposure misclassification on estimates of relative risk. American Journal of Epidemiology, 123, 736-751.
87. Fleming, T. R., and DeMets, D. (1996). Surrogate end points in clinical trials: are we being misled? Annals of Internal Medicine, 125, 605-613.
88. Fossa, S. D., and Skovlund, E. (2002). Selection of patients may limit the generalizability of results from cancer trials. Acta Oncologia, 41, 131-137.
89. Fraker, T., and Maynard, R. (1987). The adequacy of comparison group design for evaluations of employment-related programs. Journal of Human Resources, 22, 194-227.
90. Frangakis, C. E. (2009). The calibration of treatment effects from clinical trials to target populations. Clinical Trials, 6, 136-140.
91. Frangakis, C. E., and Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58, 21-29.
92. Frankel, A. (2006). The mass tort bonanza that wasn't. The American Lawyer, January 6.
93. Freedman, D. A. (2006). Statistical methods for causation: a critical review. In Encyclopedia of Statistics in Behavioral Science. New York: John Wiley & Sons.
94. Freedman, D. A., and Berk, R. A. (2008). Weighting regressions by propensity scores. Evaluation Review, 32, 392-409.
95. Fugh-Berman, A., and Bythrow, J. (2007). Bioidentical hormones for menopausal hormone therapy: variation on a theme. Journal of General Internal Medicine, 22, 1030-1034.
96. Galton, F. (1889). Co-relations and their measurement. Proceedings of the Royal Society, 45, 135-145.
97. Garbe, E., and Suissa, S. (2004). Hormone replacement therapy and acute coronary outcomes: methodological issues between randomized and observational studies. Human Reproduction, 19, 8-13.
98. Gelman, A., and Hill, J. (2007). Data Analysis Using Regression and Multivariate/ Hierarchical Models. New York: Cambridge University Press.
99. Geng, Z., Guo, J., and Fung, W. (2002). Criteria for confounders in epidemiological studies. Journal of the Royal Statistical Society (Series B), 64, 3-15.
100. Geng, Z., Guo, J., Lau, T. S., and Fung, W. (2001). Confounding, homogeneity and collapsibility for causal effects in epidemiologic studies. Statistica Sinica, 11, 63-75.
101. Gerhard, T. (2008). Bias: considerations for research practice. American Journal of Health System Pharmacists, 65, 2159-2168.
102. Gibbons, R. D., Brown, C. H., Hur, K., Marcus, S. M., Bhaumik, D. K., Erkens, J. A., Herings, R., and Mann, J. J. (2007). Early evidence on the effects of regulators 'suicide warnings on SSRI prescriptions and suicide in adolescents. American Journal of Psychiatry, 164, 1356-1363.
103. Gladen, B., and Rogan, W. J. (1979). Misclassification and the design of environmental studies. American Journal of Epidemiology, 109, 607-616.
104. Glasziou, P. P., and Irwig, L. M. (1995). An evidence based approach to individualising treatment. British Medical Journal, 311, 1356-1359.
105. Gluud, L. L. (2006). Bias in clinical intervention research. American Journal of Epidemiology, 163 (6), 493-501.
106. Glymour, C. (1998). Review of " The Art of Causal Conjecture, " by G Shafer. Journal of the American Statistical Association, 93, 1513-1515.
107. Goldberger, A. S. (1981). Linear regression after selection. Journal of Econometrics, 15, 357-366.
108. Gordis, L. (2004). Epidemiology (3rd ed.). Philadelphia: Elsevier Saunders.
109. Grady, D., Herrington, D., Bittner, V., Blumenthal, R., Davidson, M., Hlatky, M., Hsia, J., Hulley, S., Herd, A., Khan, S., Newby, L. K., Waters, D., Vittinghoff, E., and Wenger, N. (2002). Cardiovascular disease outcomes during 6.8 years of hormone therapy. Journal of the American Medical Association, 288, 49-57.
110. Grayson, D. A. (1987). Confounding confounding. American Journal of Epidemiology, 126, 546-553.
111. Greenhouse, J. B., Kaizar, E. E., Kelleher, K., Seltman, H., and Gardner, W. (2008). Generalizing from clinical trial data: a case study. The risk of suicidality among pediatric antidepressant users. Statistics in Medicine, 27, 1801-1813.
112. Greenland, S. (1980). The effect of misclassification in the presence of covariates. American Journal of Epidemiology, 112, 564-569.
113. Greenland, S. (Ed.). (1987a). Evolution of Epidemiologic Ideas: Annotated Readings on Concepts and Methods. Chestnut Hill, MA: Epidemiology Resources.
114. Greenland, S. (1987b). Interpretation and choice of effect measures in epidemiologic analysis. American Journal of Epidemiology, 125, 761-768.
115. Greenland, S. (1988). Statistical uncertainty due to misclassification: implications for validation substudies. Journal of Clinical Epidemiology, 41, 1167-1174.
116. Greenland, S. (1989). Modeling and variable selection in epidemiologic analysis. American Journal of Public Health, 79, 340-349.
117. Greenland, S. (1990). Randomization, statistics and causal inference. Epidemiology, 1, 421-429.
118. Greenland, S. (1993). Basic problems in interaction assessment. Environmental Health Perspectives, 101 (Suppl. 4), 59-66.
119. Greenland, S. (1996a). Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology, 7, 498-501.
120. Greenland, S. (1996b). Basic methods for sensitivity analysis of biases. International Journal of Epidemiology, 25, 1107-1116.
121. Greenland, S. (2005a). Epidemiologic measures and policy formulation: lessons from potential outcomes. Emerging Themes in Epidemiology, 2, 1-7.
122. Greenland, S. (2005b). Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society (Series A), 168, 267-306.
123. Greenland, S., and Brumback, B. (2002). An overview of relations among causal modelling methods. International Journal of Epidemiology, 31, 1030-1037.
124. Greenland, S., and Poole, C. (1988). Invariants and noninvariants in the concept of interdependent effects. Scandinavian Journal of Work and Environmental Health, 14, 125-129.
125. Greenland, S., and Robins, J. M. (1986). Identifiability, exchangeability, and epidemiological confounding. International Journal of Epidemiology, 15, 413-419.
126. Greenland, S., and Robins, J. M. (1988). Conceptual problems in the definition and interpretation of attributable fractions. American Journal of Epidemiology, 128, 1185-1197.
127. Greenland, S., Pearl, J., and Robins, J. M. (1999). Causal diagrams for epidemiological research. Epidemiology, 10, 37-48.
128. Greenland, S., Robins, J. M., and Pearl, J. (1999). Confounding and collapsibility in causal inference. Statistical Science, 14, 29-46.
129. Greenwood, M., and Yule, G. U. (1915). The statistics of anti-typhoid and anti-cholera inoculation, and the interpretation of such statistics in general. Proceedings of the Royal Society of Medicine, 8, 113-194.
130. Griffin, B. A., McCaffery, D. F., and Morral, A. R. (2008). An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse programs. Annals of Applied Statistics, 2, 1034-1055.
131. Grodstein, F., Clarkson, T. B., and Manson, J. E. (2003). Understanding the divergent data on postmenopausal hormone therapy. New England Journal of Medicine, 348, 645-650.
132. Grodstein, F., Stampfer, M. J., Colditz, G. A., Willett, W. C., Manson, J. E., Joffe, M., Rosner, B., Fuchs, C., Hankinson, S. E., Hunter, D. J., Hennekens, C. H., and Speizer, F. E. (1997). Postmenopausal hormone therapy and mortality. New England Journal of Medicine, 336, 1769-1775.
133. Gullen, W. H., Bearman, J. E., and Johnson, E. A. (1968). Effects of misclassication in epidemiologic studies. Public Health Reports, 53, 1956-1965.
134. Gulliksen, H. (1950). Theory of Mental Tests. New York: John Wiley & Sons.
135. Guyatt, G. H., Sackett, D. L., and Cook, D. J. (1993). Evidence-Based Medicine Working Group. Users'guides to the medical literature, II: how to use an article about therapy or prevention; A: are the results of the study valid? Journal of the American Medical Association, 270, 2598-2601.
136. Guyatt, G. H., Sackett, D. L., and Cook, D. J. (1994). Evidence-Based Medicine Working Group. Users'guides to the medical literature, II: how to use an article about therapy or prevention; B: what were the results and will they help me in caring for my patients? Journal of the American Medical Association, 271, 59-63.
137. Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1-12.
138. Hafeman, D. M. (2008). A sufficient cause based approach to the assessment of mediation. European Journal of Epidemiology, 23, 711-721.
139. Halloran, M. E., and Struchiner, C. J. (1995). Causal inference for infectious diseases. Epidemiology, 6, 142-151.
140. Hamilton, M. A. (1979). Choosing a parameter for a 2 ×2 table or a 2 ×2 ×2 table analysis. American Journal of Epidemiology, 109, 362-375.
141. Hammad, T. A., Laughren, T., and Racoosin, J. (2006a). Suicidality in pediatric patients treated with antidepressant drugs. Archives of General Psychiatry, 63, 332-339.
142. Hammad, T. A., Laughren, T. P., and Racoosin, J. A. (2006b). Suicide rates in short -term randomized controlled trials of newer antidepressants. Journal of Clinical Psychopharmacology, 26 (2), 203-207.
143. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning:Data Mining, Inference and Prediction. (2nd ed.). New York: Springer.
144. Hauck, W. W., and Anderson, S. A. (1986). A proposal for interpreting and reporting negative studies. Statistics in Medicine, 5, 203-209.
145. Hauck, W. W., Neuhaus, J. M., Kalbfleisch, J. D., and Anderson, S. (1991). A consequence of omitted covariates when estimating odds ratios. Journal of Clinical Epidemiology, 44, 77-81.
146. Haynes, R. B., Sackett, D. L., Guyatt, G. H., and Tugwell, P. (2006). Clinical Epidemiology: How to Do Clinical Practice Research (3rd ed.). Philadelphia:Lippincott Williams & Wilkins.
147. Heckman, J. J., and Hotz, V. J. (1989). Choosing among alternative nonexperimental methods for estimating the impact of social programs: the case of manpower training (with discussion). Journal of the American Statistical Association, 84, 862-880.
148. Heckman, J. J., and Robb, R. (1985). Alternative methods for evaluating the effect of interventions. In J. Heckman and B. Singer (Eds.), Longitudinal Analysis of Labor Market Data (pp. 156-246). New York: Cambridge University Press.
149. Heckman, J. J., and Smith, J. A. (1995). Assessing the case for social experiments. Journal of Economic Perspectives, 9, 85-110.
150. Heckman, J. J., Ichimura, H., and Todd, P. E. (1997). Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Review of Economic Studies, 64, 605-654.
151. Heckman, J. J., Smith, J., and Clements, N. (1997). Making the most out of social experiments: accounting for heterogeneity in programme impacts. Review of Economic Studies, 64, 487-536.
152. Hernan, M. A. (2004). A definition of causal effect for epidemiological research. Journal of Epidemiology and Community Health, 58, 265-271.
153. Hill, A. B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58, 295-300.
154. Hill, J. (2008). Comment: can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association, 103, 1346-1350.
155. Hoffman, G. E., and Zup, S. L. (2003). Good versus evil: changing the approach to hormone therapy. Endocrinology, 144, 4698-4699.
156. Höfler, M. (2005a). Causal inference based on counterfactuals. BMC Medical Research Methodology, 5, 1-12.
157. Höfler, M. (2005b). The effect of misclassification on the estimation of association: a review. International Journal of Methods in Psychiatric Research, 14, 92-101.
158. Höfler, M., Lieb, R., and Wittchen, H. (2007). Estimating causal effects from observational data with a model for multiple bias. International Journal of Methods in Psychiatric Research, 16, 77-87.
159. Holford, T. R. (1995). Study design for epidemiologic studies with measurement error. Statistical Methods in Medical Research, 4, 339-358.
160. Holland, P. W. (1986). Statistics and causal inference (with discussion). Journal of the American Statistical Association, 81, 945-970.
161. Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equation models. Sociological Methodology, 18, 449-484.
162. Holland, P. W., and Rubin, D. B. (1988). Causal inference in retrospective studies. Evaluation Review, 12, 203-231.
163. Horwitz, R. I., and Feinstein, A. R. (1978). Alternate analytic methods for case-control studies of endometrial cancer. New England Journal of Medicine, 299, 1089-1094.
164. Hosmer, D., and Lemeshow, S. (2000). Applied Logistic Regression (2nd ed.). New York:John Wiley & Sons.
165. Hulley, S., Furberg, C., and Barrett-Conner, C. (2002). Noncardiovascular disease outcomes during 6.8 years of hormone therapy, Heart and Estrogen/Progestin Replacement Study Follow-up (HERS II). Journal of the American Medical Association, 288, 58-66.
166. Hulley, S., Grady, D., and Bush, T. E. (1998). Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. Journal of the American Medical Association, 280, 605-613.
167. Hume, D. (1748). An Enquiry Concerning Human Understanding. Repr., Lasalle, Ill.:Open Court Press, 1988.
168. Ibrahim, M. A., and Spitzer, W. O. (1979). The case-control study: the problem and the prospect. Journal of Chronic Diseases, 32, 139-144.
169. Imai, K., King, G., and Stuart, E. A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society (Series A), 171, 481-502.
170. Imbens, G. W., and Rubin, D. B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance. The Annals of Statistics, 6, 34-58.
171. Jick, H., and Vessey, M. P. (1978). Case-control studies in the evaluation of drug -induced illnesses. American Journal of Epidemiology, 107, 1-7.
172. Jin, H., and Rubin, D. B. (2008). Principal stratification for causal inference with extended partial compliance. Journal of the American Statistical Association, 103, 101-111.
173. Joffe, M. M., and Colditz, G. A. (1998). Restriction as a method for reducing bias in the estimation of direct effects. Statistics in Medicine, 17, 2233-2249.
174. Johnston, J. (1963). Econometric Methods. New York: McGraw Hill.
175. Kamstrup, P. R., Tybjaerg-Hansen, A., and Steffensen, R. (2009). Genetically-elevated Lipoprotein(a) and increased risk of myocardial infarction. Journal of the American Medical Association, 301, 2331-2339.
176. Kaptchuk, T. J. (2001). The double-blind, randomized, placebo-controlled trial: gold standard or golden calf? Journal of Clinical Epidemiology, 54, 541-549.
177. Kaufman, J. S., and Cooper, R. S. (1999). Seeking causal explanations in social epidemiology. American Journal of Epidemiology, 150, 113-120.
178. Kaufman, J. S., MacLehose, R. F., and Kaufman, S. (2004). A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiologic Perspectives & Innovations, 1, 1-13.
179. Kernan, W. N., Viscoli, C. M., Brass, L. M., Broderick, J. P., Brott, T., Feldmann, E., Morgenstern, L. B., Wilterdink, L. D., and Horwitz, R. I. (2000). Phenylpropanolamine and the risk of hemorrhagic stroke. New England Journal of Medicine, 343, 1826-1832.
180. Keys, A., and Kihlberg, J. K. (1963). The effect of misclassification on estimated relative prevalence of a characteristic. American Journal of Public Health, 53, 1656-1665.
181. Kish, L. (1965). Survey Sampling. New York: John Wiley & Sons.
182. Kleinbaum, D. G., Kupper, L. L., and Morgenstern, H. (1982). Epidemiologic Research. Belmont, CA: Lifetime Learning Publications.
183. Kleinbaum, D. G., Morgenstern, H., and Kupper, L. L. (1981). Selection bias in epidemiologic studies. American Journal of Epidemiology, 113, 452-463.
184. Klungel, O. H., Martens, E. P., Psaty, B. M., Grobbee, D. E., Sullivan, S. D., Sticker, B. H., Leufkens, H. G. M., and de Boer, A. (2004). Methods to assess intended effects of drug treatment in observational studies are reviewed. Journal of Clinical Epidemiology, 57, 1223-1231.
185. Knoke, J. D., Burns, D. M., and Thun, M. J. (2008). The change in excess risk of lung cancer attributable to smoking following smoking cessation: an examination of different analytic approaches using CPS-I data. Cancer Causes Control, 19, 207-219.
186. Koh, K. K., Han, S. H., Shin, M., Ahn, J. Y., Lee, Y., and Shin, E. K. (2005). Significant differential aspects of lower doses of hormone therapy or tibolone on markers of cardiovascular disease in postmenopausal women: a randomized, double-blind, crossover study. European Heart Journal, 26, 1362-1368.
187. Koopmans, T. C., and Reiersol, O. (1950). The identification of structural characteristics. Annals of Mathematical Statistics, 21, 165-181.
188. Krieger, N. (1994). Epidemiology and the web of causation: has anyone seen the spider? Social Science Medicine, 39, 887-903.
189. Kristensen, P. (1992). Bias from nondifferential but dependent misclassification of exposure and outcome. Epidemiology, 3, 210-215.
190. Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
191. Lachin, J. M. (2000). Statistical considerations in the intent-to-treat principle. Controlled Clinical Trials, 21, 167-189.
192. LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604-620.
193. Lane-Claypon, J. E. (1926). A Further Report on Cancer of the Breast. Reports of Public Health and Medical Subjects, Ministry of Health. London: H.M.S.O.
194. Last, J. M. (Ed.). (2001). A Dictionary of Epidemiology (4th ed.). New York: Oxford University Press.
195. Laupacis, A., Sackett, D. L., and Roberts, R. S. (1988). An assessment of clinically useful measures of the consequences of treatment. New England Journal of Medicine, 318, 1728-1733.
196. Leishman, W. B. (1910). Anti-typhoid inoculation. Journal of the Royal Institute of Public Health and Hygiene, 10, 385.
197. Leslie, L. K., Newman, T. B., Chesney, P. J., and Perrin, J. M. (2005). The Food and Drug Administration's deliberations on antidepressant use in pediatric patients. Pediatrics, 116, 195-202.
198. Lewbel, A. (2007). Estimation of average treatment effects with misclassification. Econometrica, 75, 537-551.
199. Little, R. J., and Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annual Review of Public Health, 21, 121-145.
200. Lonn, E. (2001). The use of surrogate endpoints in clinical trials: focus on clinical trials in cardiovascular disease. Pharmacoepidemiological Drug Safety, 10, 497-508.
201. Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304-305.
202. Lord, F. M., and Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Reading, MA: Addison Wesley.
203. Lyles, R. H. (2002). A note on estimating crude odds ratios in case-control studies with differentially misclassified responses in binary regression. Biometrics, 58, 1034-1037.
204. Machens, K., and Schmidt-Gollwitzer, K. (2003). Issues to debate on the women's health initiative (WHI) study. Hormone replacement therapy: an epidemiological dilemma? Human Reproduction, 18 (10), 1992-1999.
205. Mackie, J. L. (1965). Causes and conditions. American Philosophical Quarterly, 2, 245-264.
206. Mackie, J. L. (1974). The Cement of the Universe: A Study of Causation. Oxford, U.K.:Clarendon Press.
207. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., and Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
208. Maclure, M., and Schneeweiss, S. (2001). Causation of bias: the episcope. Epidemiology, 12, 114-122.
209. Maldonado, G., and Greenland, S. (2002). Estimating causal effects. International Journal of Epidemiology, 31, 422-429.
210. Manpower Demonstration Research Corporation. (1983). Summary and Findings of the National Supported Work Demonstration. Cambridge, MA: Ballinger.
211. Manski, C. F. (1989). Anatomy of the selection problem. Journal of Human Resources, 24, 343-360.
212. Manski, C. F. (1990). Nonparametric bounds on treatment effects. American Economic Review, Papers and Proceedings, 80, 319-323.
213. Manski, C. F. (1999). Comment: Choice as an alternative to control in observational studies, by P Rosenbaum. Statistical Science, 14, 279-281.
214. Manski, C. F. (2007). Identification for Prediction and Decision. Cambridge, MA:Harvard University Press.
215. Manski, C. F., Sandefur, G. D., and McLanahan, S. (1992). Alternative estimates of the effect of family structure during adolescence on high school graduation. Journal of the American Statistical Association, 87, 25-37.
216. Manson, J. E., Hsia, J., Johnson, K. C., Roussouw, J. E., Assaf, A. R., Lasser, N. L., Trevisan, N., Black, H. R., Heckbert, S. R., Detrano, R., Strickland, O. L., Wong, N. D., Crouse, J. R., Stein, E., Cushman, M., and the Women's Health Initiative Investigators (2003). Estrogen plus progestin and risk of coronary heart disease. New England Journal of Medicine, 349, 523-534.
217. Mant, D. (1999). Can randomised trials inform clinical decisions about individual patients? Lancet, 353, 743-748.
218. Mark, D. H. (1997). Interpreting the term selection bias in medical research. Family Medicine, 29, 132-136.
219. Marks, H. M. (1997). The Progress of Experiment. Cambridge, U.K.: Cambridge University Press.
220. Masters, S., and Maynard, R. (1981). The Impact of Supported Work on Long-Term Recipients of AFDC Benefits. New York: Manpower Demonstration Research Corporation.
221. McFarlane, M. J., Brennan, T. A., Horwitz, R. I., and Feinstein, A. R. (1985). The role of susceptibility bias in epidemiological research. Archives of Internal Medicine, 145, 909-912.
222. McNamee, R. (2003). Confounding and confounders. O ccupational and Environmental Medicine, 60, 227-234.
223. Meehl, P. E. (1977). Specific etiology and other forms of strong influence: some quantitative meanings. The Journal of Medicine and Philosophy, 2, 33-53.
224. Menzies, P. (2004). Difference-making in context. In J. Collins, N. Hall, and L. A. Paul (Eds.), Causation and Counterfactuals (pp. 139-180). Cambridge, MA: The MIT Press.
225. Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13, 151-161.
226. Michels, K. B. (2001). A renaissance for measurement error. International Journal of Epidemiology, 30, 421-422.
227. Michels, K. B. (2003). Hormone replacement therapy in epidemiologic studies and randomized clinical trials: are we checkmate? Epidemiology, 14, 3-5.
228. Miettinen, O. S. (1985). The " case control " study: valid selection of subjects. Journal of Chronic Diseases, 38, 543-548.
229. Miettinen, O. S., and Cook, E. F. (1981). Confounding: essence and detection. American Journal of Epidemiology, 114, 593-603.
230. Mill, J. S. (1874). A System of Logic (8th ed.). New York: Harper and Brothers.
231. Million Women Study Collaborators. (2003). Breast cancer and hormone replacement therapy in the Million Women Study. Lancet, 362, 419-427.
232. Moffitt, R. (1991). Program evaluation with nonexperimental data. Evaluation Review, 15, 291-314.
233. Morgan, S. L., and Winship, C. (2007). Counterfactuals and Causal Inference:Methods and Principles for Social Research. New York: Cambridge University Press.
234. Moride, Y., and Abenhaim, L. (1994). Evidence of the depletion of susceptibles effect in non-experimental pharmacological research. Journal of Clinical Epidemiology, 47, 731-737.
235. Morrison, D. (2008). Clinical inference: critically weighing the evidence from trials and registries to make clinical decisions. Catheterization and Cardiovascular Interventions, 72, 381-385.
236. Mosca, L., Collins, P., Herrington, D. M., Mendelsohn, M. E., Pasternack, R. C., Robertson, R. M., Schenck-Gustaffson, K., Smith, S. C., Taubert, K. A., and Wenger, N. K. (2001). Hormone replacement therapy and cardiovascular disease:a statement for healthcare professionals from the American Heart Association. Circulation, 104, 499-503.
237. Moses, L. E. (1995). Measuring effects without randomized trials? Options, problems and challenges. Medical Care, 33, AS8-AS14.
238. Moskowitz, D. (2006). A comprehensive review of the safety and efficacy of bioidentical hormones for the management of menopause and related health risks. Alternative Medicine Review, 11, 208-223.
239. Naftolin, F., Taylor, H. S., Karas, R., Brinton, E., Newman, I., Clarkson, T. B., Mendelsohn, M., Lobo, R. A., Judelson, D. R., Nachtigall, L. E., Heward, C. B., Hecht, H., Jaff, M. R., and Harman, S. M. (2004). The Women's Health Initiative could not have detected cardioprotective effects of starting hormone therapy during the menopausal transition. Fertility and Sterility, 81, 1498-1450.
240. Neuhaus, J. M. (1999). Bias and efficiency loss due to misclassified responses in binary regression. Biometrika, 86, 843-855.
241. Newell, D. J. (1962). Errors in the interpretation of errors in epidemiology. American Journal of Public Health, 52, 1925-1928.
242. New York Times. (1910). Wholesale vaccination to prevent typhoid fever. June 5. New York Times. (1991). Editorial. September 29.
243. Neyman, J. (1923). On the application of probability theory to agricultural experiments:essay on principles. Annals of Agricultural Science. Translated (1990) in Statistical Science, 5, 465-480.
244. Olfson, M., Shaffer, D., Marcus, S. C., and Greenberg, T. (2003). Relationship between antidepressant medication treatment and suicide in adolescents. Archives of General Psychiatry, 60, 978-982.
245. Paradis, C. (2008). Bias in surgical research. Annals of Surgery, 248, 180-188.
246. Pearce, N., Checkoway, H., and Kriebel, D. (2007). Bias in occupational epidemiological studies. Occupational and Envrionmental Medicine, 64, 562-568.
247. Pearl, J. (2000). Causality: Models, Reasoning and Inference. New York: Cambridge University Press.
248. Pearson, K. (1900). Grammar of Science. London: Adam and Charles Black.
249. Pearson, K. (1904). Report on certain enteric fever inoculation statistics. British Medical Journal, 2, 1243-1246.
250. Penston, J. (2005). Large-scale randomised trials-a misguided approach to clinical research. Medical Hypotheses, 64, 651-657.
251. Pentti, K., Honkanon, R., Tuppurainen, M. T., Sandini, L., Kroger, H., and Saarikoski, S. (2006). Hormone replacement therapy and mortality in 52-to 70-year-old women: the Kuopo Osteoporosis Risk Factor and Prevention Study. European Journal of Endocrinology, 154, 101-107.
252. Petitti, D. B. (1998). Hormone replacement therapy and heart disease prevention:experimentation trumps observation. Journal of the American Medical Association, 280, 650-652.
253. Phillips, C. V., and Goodman, K. J. (2006). Causal criteria and counterfactuals; nothing more (or less) than scientific common sense. Emerging Themes in Epidemiology, 3, 1-7.
254. Popper, K. R. (1959). The Logic of Scientific Discovery. New York: Basic Books.
255. Prentice, R. L., Langer, R. D., Stefanick, M. L., Howard, B. V., Pettinger, M., Anderson, G. L., Barad, D., Curb, J. D., Kotchen, J., Kuller, L., Limacher, M., Wactawski -Wende, J., and the Womens Health Initiative Investigators (2006). Combined analysis of Women's Health Initiative observational and clinical trial data on postmenopausal hormone therapy and cardiovascular disease. American Journal of Epidemiology, 163, 589-599.
256. President's Council of Advisors on Science and Technology. (2008). Priorities for Personalized Medicine. Washington, D.C.: U.S. Government Printing Office.
257. Reynolds, K. D., and West, S. G. (1987). A multiplist strategy for strengthening nonequivalent control group designs. Evaluation Review, 11, 691-714.
258. Rice, K. (2003). Full-likelihood approaches to misclassification of a binary exposure in matched case-control studies. Statistics in Medicine, 22, 3177-3194.
259. Roberts, R. S., Spitzer, W. O., Delmore, T., and Sackett, D. L. (1978). An empirical demonstration of Berkson's bias. Journal of Chronic Diseases, 31, 119-128.
260. Robins, J. M. (1989). The control of confounding by intermediate variables. Statistics in Medicine, 8, 679-701.
261. Robins, J. M., and Greenland, S. (1986). The role of model selection in causal inference from nonexperimental data. American Journal of Epidemiology, 123, 392-402.
262. Robins, J. M., and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.
263. Robins, J. M., and Morgenstern, H. (1987). The mathematical foundations of confounding in epidemiology. Computers and Mathematical Applications, 14, 869-916.
264. Rogosa, D., Brandt, D., and Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726-748.
265. Rosenbaum, P. R. (1984). From association to causation in observational studies: the role of tests of strongly ignorable treatment assignment. Journal of the American Statistical Association, 79, 41-48.
266. Rosenbaum, P. R. (1999). Choice as an alternative to control in observational studies (with discussion). Statistical Science, 14, 259-304.
267. Rosenbaum, P. R. (2001). Replicating effects and biases. American Statistician, 55, 223-227.
268. Rosenbaum, P. R. (2002). Observational Studies. New York: Spinger-Verlag.
269. Rosenbaum, P. R., and Rubin, D. B. (1983a). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
270. Rosenbaum, P. R., and Rubin, D. B. (1983b). Assessing sensitivity to an unobserved covariate in an observational study with binary outcome. Journal of the Royal Statistical Society (Series B), 45, 212-218.
271. Rothman, K. J. (1976). Causes. American Journal of Epidemiology, 104, 587-592.
272. Rothman, K. J. (Ed.). (1988). Causal Inference. Chestnut Hill, MA: Epidemiology Resources Inc.
273. Rothman, K. J., and Greenland, S. (1998). Modern Epidemiology (2nd ed.). Boston:Lippincott, Williams & Wilkins.
274. Rothman, K. J., and Greenland, S. (2005). Causation and causal inference. American Journal of Public Health, 95 (Suppl. 1), S144-S150.
275. Rothman, K. J., Greenland, S., and Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Boston: Lippincott, Williams & Wilkins.
276. Rothwell, P. M. (2005). External validity of randomised controlled trials: to whom do the results of this trial apply? Lancet, 365, 82-93.
277. Roussouw, J. E., Prentice, R. L., Manson, J. E., Wu, L., Barad, D., Barnabei, V. M., Ko, M., LaCroix, A. Z., Margolis, K. L., and Stefanick, M. L. (2007). Postmenopausal hormone therapy and risk of cardiovascular disease by age and years since menopause. Journal of the American Medical Association, 297, 1465-1477.
278. Roy, J., Hogan, J. W., and Marcus, B. H. (2008). Principal stratification with predictors of compliance for randomized trials with 2 active treatments. Biostatistics, 2, 277-289.
279. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
280. Rubin, D. B. (1976). Inference and missing data (with Discussion and Reply). Biometrika, 63 (3), 581-592.
281. Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 1-26.
282. Rubin, D. B. (1978). Bayesian inference for causal effects: the role of randomization. Annals of Statistics, 6, 34-58.
283. Rubin, D. B. (1984). William G. Cochran's contributions to the design, analysis and evaluation of observational studies. In P. S. R. S. Rao and J. Sedransk (Eds.), W.G. Cochran's Impact on Statistics (pp. 37-69). New York: John Wiley & Sons.
284. Rubin, D. B. (1986). Which ifs have causal answers? Journal of the American Statistical Association, 81, 961-962.
285. Rubin, D. B. (1990a). Formal models of statistical inference for causal effects. Journal of Statistical Planning and Inference, 25, 279-292.
286. Rubin, D. B. (1990b). Neyman (1923) and causal inference in experiments and observational studies. Statistical Science, 5, 472-489.
287. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757-763.
288. Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 34, 161-170.
289. Rubin, D. B. (2006). Causal inference through potential outcomes and principal stratification: application to studies with " censoring " due to death. Statistical Science, 21, 299-309.
290. Rubin, D. B. (2007). The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Statistics in Medicine, 26, 20-36.
291. Rubin, D. B., and Thomas, N. (1996). Matching using estimated propensity scores:relating theory to practice. Biometrics, 52, 249-264.
292. Rubin, D. B., Stuart, E. A., and Zanutto, E. L. (2004). A potential outcomes view of value-added assessment in education. Journal of Educational and Behavioral Statistics, 29, 103-116.
293. Sackett, D. L. (1979). Bias in analytic research. Journal of Chronic Diseases, 32, 51-63.
294. Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B., and Richardson, W. S. (1996). Evidence based medicine: what it is and what it isn't. British Medical Journal, 312, 71-72.
295. Salas, M., Hofman, A., and Stricker, B. H. (1999). Confounding by indication: an example of variation in the use of epidemiologic terminology. American Journal of Epidemiology, 149, 981-983.
296. Savitz, D. A. (2003). Interpreting Epidemiologic Evidence: Strategies for Study Design and Analysis. New York: Oxford University Press.
297. Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. New York:Oxford University Press.
298. Schulz, K. F. (1995). Subverting randomization in controlled trials. Journal of the American Medical Association, 274, 1456-1458.
299. Schulz, K. F., and Grimes, D. A. (2002). Allocation concealment in randomised trials:defending against deciphering. Lancet, 359, 614-618.
300. Schulz, K. F., Chalmers, I., Hayes, R. J., and Altman, D. G. (1995). Empirical evidence of bias: dimensions of methodological quality associated with estimates of treatment effects in controlled trials. Journal of the American Medical Association, 273, 408-412.
301. Shadish, W. R., and Cook, T. D. (1999). Comment: Design rules: more steps toward a complete theory of quasi-experimentation. Statistical Science, 14, 294-300.
302. Shadish, W. R., Clark, M. H., and Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association, 103, 1334-1343.
303. Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002). Experimental and Quasi -Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.
304. Shafer, G. (1996). The Art of Causal Conjecture. Cambridge, MA: MIT Press.
305. Shah, B. R., Laupacis, A., Hux, J. E., and Austin, P. C. (2005). Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. Journal of Clinical Epidemiology, 58, 550-559.
306. Shonkoff, J. P., and Phillips, D. A. (Eds.). (2000). From Neurons to Neighborhoods:The Science of Early Childhood Development. Washington, D.C.: National Academy Press.
307. Siddiqui, A. E., Sikorskii, A., Given, C. W., and Given, B. (2008). Early participant attrition from clinical trials: role of trial design and logistics. Clinical Trials, 5, 328-335.
308. Siegmund, K. D., and Langholz, B. (2002). Ascertainment bias in family-based case -control studies. American Journal of Epidemiology, 155, 875-880.
309. Simpson, E. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society (Series B), 13, 238-241.
310. Sloman, S. (2005). Causal Models: How People Think about the World and Its Alternatives. New York: Oxford University Press.
311. Smith, G. D., and Ebrahim, S. (2003). " Mendelian Randomization ": can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32, 1-22.
312. Smith, J., and Todd, P. (2005a). Does matching overcome LaLonde's critique of nonexperimental estimators? (with reply by R Dehejia and rejoinder). Journal of Econometrics, 125, 305-375.
313. Smith, J., and Todd, P. (2005b). Rejoinder (to Dehejia reply to Smith and Todd). Journal of Econometrics, 125, 365-375.
314. Sobel, M. E. (1990). Effect analysis and causation in linear structural equation models. Psychometrika, 55, 495-515.
315. Sommer, A., and Zeger, S. L. (1991). On estimating efficacy from clinical trials. Statistics in Medicine, 10, 45-52.
316. Steineck, G., and Albohm, A. (1992). A definition of bias founded on the concept of the study base. Epidemiology, 477-482.
317. Steyer, R. (2005). Analyzing individual and average causal effects via structural equation models. Methodology, 1, 39-54.
318. Stone, R. (1993). The assumptions on which causal inference rest. Journal of the Royal Statistical Society (Series B), 55, 455-486.
319. Strenio, J., Weisberg, H. I., and Bryk, A. S. (1983). Empirical Bayes estimation of individual growth parameters and their relationship to covariates. Biometrics, 39, 71-88.
320. Strom, B. (2007). Methodologic challenges to studying patient safety and comparative effectiveness. Medical Care, 45 (10 Suppl. 2), S13-S15.
321. Susser, M. (1977). Judgment and causal inference: criteria in epidemiologic studies. American Journal of Epidemiology, 105, 1-15.
322. Susser, M. (1988). Falsification, verification, and causal inference in epidemiology. In K. J. Rothman (Ed.), Causal Inference. Chestnut Hill, MA: Epidemiology Resources.
323. Szklo, M., and Nieto, F. H. (2007). Epidemiology: Beyond the Basics. Sudbury, MA:Jones and Bartlett.
324. Thanassoulis, G., and O'Donnell, C. J. (2009). Mendelian randomization: nature's randomized trial in the post-genome era. Journal of the American Medical Association, 301, 2386-2388.
325. Tukey, J. W. (1960). Conclusions vs decisions. Technometrics, 2, 423-433.
326. Tukey, J. W. (1962). The future of data analysis. Annals of Mathematical Statistics, 33, 1-67.
327. Tukey, J. W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley.
328. U.S. Food and Drug Administration. (2006). Relationship between psychotropic drugs and pediatric suicidality. November 16, 2006. Retrieved January 29, 2008, from http://www.fda.gov/ohrms/dockets/ac/briefing/2004-4065b1-10-TAB08-Hammads -Review.pdf Valabhji, J., and Elkeles, R. S. (2000). Debate: are surrogate end-point studies worth the effort ? Cardiovascular Medicine, 1, 72-75.
329. van der Schouw, Y. T., and Grobbee, D. E. (2005). Menopausal complaints, oestrogens, and heart disease risk: an explanation for discrepant findings on the benefits of post-menopausal therapy. European Heart Journal, 26, 1358-1361.
330. Van Spall, H. G., Toren, A., Kiss, A., and Fowler, R. A. (2007). Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. Journal of the American Medical Association, 297, 1233-1240.
331. Vandenbroucke, J. P. (1987). Should we abandon statistical modeling altogether? American Journal of Epidemiology, 126, 10-13.
332. VanderWeele, T. J., and Robins, J. M. (2007). The identification of synergism in the SCC framework. Epidemiology, 18, 329-339.
333. VanderWeele, T. J., and Robins, J. M. (2008). Empirical and counterfactual conditions for sufficient cause interactions. Biometrika, 95, 49-61.
334. Vickers, M. R., MacLennan, A. H., Lawton, B., Ford, D., Martin, J., Meredith, S. K., DeStavola, B. L, Rose, S., Dowell, A., Wilkes, H. C., Darbyshire, J. H., and Meade, T. W. (2007). Main morbidities recorded in the women's international study of long duration oestrogen after menopause (WISDOM): a randomized controlled trial of hormone replacement therapy in postmenopausal women. British Medical Journal, 338, 239-250.
335. Vineis, P. (2003). Causality in epidemiology. Praventivmed, 48, 80-87.
336. Vineis, P., and McMichael, A. J. (1998). Bias and confounding in molecular epidemiological studies: special considerations. Carcinogenesis, 19, 2063-2067.
337. Wacholder, S., McLaughlin, J. K., Silverman, D. T., and Mandel, J. S. (1992). Selection of controls in case-control studies, I: principles. American Journal of Epidemiology, 135, 1019-1028.
338. Wacholder, S., Silverman, D. T., McLaughlin, J. K., and Mandel, J. S. (1992a). Selection of controls in case-control studies, II: types of controls. American Journal of Epidemiology, 135, 1029-1041.
339. Wacholder, S., Silverman, D. T., McLaughlin, J. K., and Mandel, J. S. (1992b). Selection of controls in case-control studies, III: design options. American Journal of Epidemiology, 135, 1042-1050.
340. Wallace, A. E., Neily, J., Weeks, W. B., and Friedman, M. J. (2006). A cumulative meta -analysis of selective serotonin reuptake inhibitors in pediatric depression: did unpublished studies influence the efficacy/safety debate? Journal of Child and Adolescent Psychopharmacology, 16, 37-58.
341. Weinberg, C. R. (1993). Toward a clearer definition of confounding. American Journal of Epidemiology, 137, 1-8.
342. Weinberg, C. R., Umbach, D. M., and Greenland, S. (1994). When will misclassification of an exposure preserve the direction of a trend? American Journal of Epidemiology, 140, 565-571.
343. Weisberg, H. I. (1979). Statistical adjustments and uncontrolled studies. Psychological Bulletin, 86, 1149-1164.
344. Weisberg, H. I. (2004). Expert report on phenylpropanolamine and hemorrhagic stroke. Unpublished.
345. Weisberg, H. I., Hayden, V. C., and Pontes, V. P. (2009). Selection criteria and generalizability within the counterfactual framework: explaining the paradox of antidepressant-induced suicidality ? Clinical Trials, 6, 109-118.
346. Wheeler, B. W., Metcalfe, C., Martin, R. M., and Gunnell, D. (2009). International aspects of regulatory action to limit antidepressant prescribing on rates of suicide in young people. Pharmacoepidemiology and Drug Safety, 18, 579-588.
347. Whitehead, A. N. (1933). Adventures of Ideas. New York: The Free Press.
348. Whittington, C. J., Kendall, T., Fonagy, P., Cottrell, D., Cotgrove, A., and Boddington, E. (2004). Selective serotonin reuptake inhibitors in childhood depression: systematic review of published versus unpublished data. Lancet, 363, 1342-1345.
349. Wickramaratne, P. J., and Holford, T. R. (1987). Confounding in epidemiologic studies. Biometrics, 43, 751-765.
350. Winship, C., and Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18, 327-350.
351. Winship, C., and Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Reviews of Sociology, 25, 659-706.
352. Witteman, J. C., D'Agostino, R. B., Stijnen, C., Kannel, W. B., Cobb, J. C., de Ridder, M. A., Hofman, A., and Robins, J. M. (1998). G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham heart study. American Journal of Epidemiology, 148, 390-401.
353. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557-585.
354. Writing Group for the Women's Health Initiative Investigators. (2002). Risks and benefits of estrogen plus progestin in healthy postmenopausal women, principal results from the Women's Health Initiative randomized controlled trial. Journal of the American Medical Association, 288, 321-333.
355. Yule, G. U. (1903). Notes on the theory of association of attributes in statistics. Biometrika, 2, 121-134.
356. Yusuf, S., Held, P., and Teo, K. K. (1990). Selection of patients for randomized controlled trials: implications of wide or narrow eligibility criteria. Statistics in Medicine, 9, 73-86.
357. Zhang, J. L., Rubin, D. B., and Mealli, F. (2009). Likelihood-based analysis of causal effects of job-training programs using principal stratification. Journal of the American Statistical Association, 104, 166-176.