This article describes how and why I became involved in consulting for the tobacco industry. I briefly discuss the four relatively distinct statistical topics that were the primary focus of my work, all of which have been central to my published academic research for over three decades: missing data; causal inference; adjustment for covariates in observational studies; and meta-analysis. To me, it is entirely appropriate to present the application of this academic work in a legal setting.
Get full access to this article
View all access options for this article.
References
1.
Rybak DC , Phelps D.Smoked: the inside story of the Minnesota tobacco trial. Minneapolis: MSP Books , 1998, pp 394-395.
2.
Frangakis C , Rubin DB.What does it mean to estimate the causal effects of smoking?Proceedings of the Section on Statistics in Epidemiology of the American Statistical Association, 1998, pp 18-27 .
3.
Rubin DB.Statistical issues in the estimation of the causal effects of smoking due to the conduct of the tobacco industry. In: Gastwirth J , ed. Statistical science in the courtroom. New York: Springer-Verlag , 2000, pp 321-351.
4.
Rubin DB.Statistical assumptions in the estimation of the causal effects of smoking due to the conduct of the tobacco industry . In: Blasius J , Hox J , de Leeuw E , Schmidt P , eds. Proceedings of the Fifth International Conference on Logic and Methodology, 6 October 2000, Cologne, Germany, 1-22 .
5.
Rubin DB.Estimating the causal effects of smoking . Statistics in Medicine2001; 20: 1395-1414 .
6.
Rubin DB.Multiple imputation of NMES . Proceedings of the International Conference on Quality in Official Statistics. Stockholm, Sweden (May 14-15, 2001), 2002, CD 24.3.
7.
Rubin DB.Using propensity scores to help design observational studies: application to the tobacco litigation . Health Services Outcome Research Methodology, 2001; 2: 169-188 .
8.
Clogg CC , Schenker N , Schultz B , Weidman L , Rubin DB.Multiple imputation of industry and occupation codes in census public-use samples using Bayesian logistic regression . Journal of the American Statistical Association1991; 86: 68-78 .
9.
Czajka JC , Hirabayashi SM , Little RJA , Rubin DB.Projecting from advance data using propensity modeling . Journal of Business and Economic Statistics1992; 10: 117-131 .
10.
Ezzati-Rice T , Johnson W , Khare M , Little R , Rubin D , Schafer J.A simulation study to evaluate the performance of model-based multiple-imputations in NCHS health examination surveys . Proceedings of Bureau of the Census Eleventh Annual Research Conference1995; 257-266 .
11.
US Department of Transportation, NHTSA . Multiple imputation of missing blood alcohol content (BAC) in FARS. Research note. National Highway Traffic Safety Administration , 1998.
12.
Madow WG , Olkin I , Rubin DB.Incomplete data in sample surveys (volume 2): theory and bibliographies. New York: Academic Press , 1983.
13.
Dempster AP , Laird N , Rubin DB.Maximum likelihood from incomplete data via the EM algorithm . Journal of the Royal Statistical Society, Series B1977; 39: 1-38 .
14.
Rubin DB.Inference and missing data . Biometrika1976; 63: 581-592 .
15.
Rubin DB.The design of a general and flexible system for handling non-response in sample surveys. Unpublished manuscript, 1977.
16.
Also In: Imputation and editing of faulty or missing survey data. Washington DC: US Department of Commerce , 1978: 1-23.
17.
Harrison GW.Communication at public presentation. Harvard School of Public Health , December 14, 2001.
18.
Shen Z. Nested Multiple Imputation. Cambridge, MA: Harvard University Department of Statistics, PhD thesis, 2000.
19.
20.
Proctor RN.Bitter pill . The Sciences1999; 39: 14-19 .
21.
Rubin DB.Statistical inference for causal effects in epidemiological studies via potential outcomes . Proceedings of the XL Scientific Meeting of the Italian Statistical Society2000, 419-430 .
22.
Harrison GW. Expert report, April 27, 1998: Health care expenditures attributable to smoking in Oklahoma. The State of Oklahoma, ex.rel., et al., plaintiffs, versus Reynolds Tobacco Co., et al., defendants 1998. Case no. CJ-96-1499-L, District Court of Cleveland County, Oklahoma.
23.
Cochran WG , Rubin DB.Controlling bias in observational studies: a review . Sankhya-A1973; 35: 417-446 .
24.
Rubin DB.Matching to remove bias in observational studies . Biometrics1973; 29: 159-183 .
25.
Rubin DB.The use of matched sampling and regression adjustment to remove bias in observational studies . Biometrics1973; 29: 184-203 .
26.
Rosenbaum P , Rubin DB.The central role of the propensity score in observational studies for causal effects . Biometrika1983; 70: 41-55 .
27.
Rubin DB , Thomas N.Affinely invariant matching methods with ellipsoidal distributions . Annals of Statistics1992; 20: 1079-1093 .
28.
Rubin DB , Thomas N.Characterizing the effect of matching using linear propensity score methods with normal covariates . Biometrika1992; 79: 797-809 .
29.
US Department of Health and Human Services, Public Health Service, Office of the Surgeon General, Publication No. 1103. Smoking and Health: Report of the Advisory Committee to the Surgeon General of the Public Health Service 1964. Washington DC: US Government Printing Office.
30.
US Department of Health, Education, and Welfare, Public Health Service, Office of the Surgeon General, Publication No. 79-50066. Smoking and Health: A Report of the Surgeon General 1979. Washington DC: US Government Printing Office.
31.
US Department of Health and Human Services, Public Health Service, Office of the Surgeon General. Preventing Tobacco Use Among Young People: A Report of the Surgeon General, Centers for Disease Control and Prevention 1994. Washington DC: US Government Printing Office.
32.
US Department of Health and Human Services. Reducing tobacco use: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2000.
33.
Fiore et al. Smoking cessation. Clinical Practice Guidelines, No. 18, USDHHS, Public Health Service, Agency for Health Care Policy and Research, Centers for Disease Control and Prevention, AHCPR Publication No. 96-0692, April 1996.
34.
Strecher VJ.Computer-tailored smoking cessation materials: a review and discussion . Patient Education and Counseling1999; 36: 107-117 .
35.
Velicer WFet al.An expert system intervention for smoking cessation . Addictive Behaviors1993; 18: 269-290 .
36.
Peterson AV , Kealey KA , Mann SL , Marek PM , Sarason IG.Hutchinson smoking prevention project: long-term randomized trial in school-based tobacco use prevention—results on smoking . Journal of the National Cancer Institute2000; 92: 1979-1991 .
37.
COMMIT Research Group . Community Intervention Trial for Smoking Cessation (COMMIT): I. cohort results from a four-year community intervention 1995 . American Journal of Public Health1995; 85: 183-192 .
38.
COMMIT Research Group . Community Intervention Trial for Smoking Cessation (COMMIT): II. changes in adult cigarette smoking prevalence . American Journal of Public Health1995; 85: 193-200 .
39.
Rubin DB.A new perspective on meta analysis. In: Watcher KW , Straf ML , eds. The future of meta analysis. Washington DC: Russell Sage=NAS , 1990, pp 155-165.