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Observational Studies

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Abstract

William G. Cochran first presented “observational studies” as a topic defined by principles and methods of statistics. Cochran had been an author of the 1964 United States Surgeon General’s Advisory Committee Report, Smoking and Health, which reviewed a vast literature and concluded: “Cigarette smoking is causally related to lung cancer in men; the magnitude of the effect of cigarette smoking far outweighs all other factors. The data for women, though less extensive, point in the same direction (p. 37) .” Though there had been some experiments confined to laboratory animals, the direct evidence linking smoking with human health came from observational or nonexperimental studies.

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References

  • Most scientific fields that study human populations conduct observational studies. Many fields have developed a literature on the design, conduct, and interpretation of observational studies, often with little reference to related work in other fields. It is not possible to do justice to these several literatures in a short bibliographic note. There follows a short and incomplete list of fine books that contain substantial general discussions of the methodology used for observational studies in epidemiology, public program evaluation, or the social sciences. A shared goal in these diverse works is evaluation of treatments, exposures, programs, or policies from nonexperimental data. The list is followed by references cited in Chapter 1.

    Google Scholar 

Some Books and a Few Papers

  • Angrist, J. D. and Krueger, A. B. (1999) Empirical strategies in labor economics. In: Handbook of Labor Economics, O. Ashenfelter and D. Card, eds., Volume 3A, Chapter 23, New York: Elsevier.

    Google Scholar 

  • Ashenfelter, O., ed. (2000) Labor Economics. New York: Worth.

    Google Scholar 

  • Becker, H. S. (1997) Tricks of the Trade. Chicago: University of Chicago Press.

    Google Scholar 

  • Blaug, M. (1980) The Methodology of Economics. New York: Cambridge University Press.

    Google Scholar 

  • Breslow, N. and Day, N. (1980, 1987) Statistical Methods in Cancer Research, Volumes 1 and 2. Lyon, France: International Agency for Research on Cancer.

    Google Scholar 

  • Campbell, D. T. (1988) Methodology and Epistemology for Social Science: Selected Papers. Chicago: University of Chicago Press, pp. 315–333.

    Google Scholar 

  • Campbell, D. and Stanley, J. (1963) Experimental and Quasi-Experimental Design for Research. Chicago: Rand McNally.

    Google Scholar 

  • Chamberlain, G. (1984) Panel data. In: Handbook of Econometrics, Chapter 22, Volume 2, Z. Griliches and M. D. Intriligator, eds., New York: Elsevier.

    Google Scholar 

  • Cochran, W. G. (1965) The planning of observational studies of human populations (with discussion) . Journal of the Royal Statistical Society, Series A, 128, 134–155.

    Google Scholar 

  • Cochran, W. (1983) Planning and Analysis of Observational Studies. New York Wiley

    Book  Google Scholar 

  • Cook, T. D. and Campbell, D. C. (1979) Quasi-Experimentation. Chicago: Rand McNally.

    Google Scholar 

  • Cook, T. D., Campbell, D. T., and Peracchio, L. (1990) Quasi-experimentation. In: Handbook of Industrial and Organizational Psychology, M. Dunnette and L. Hough, eds., Palo Alto, CA: Consulting Psychologists Press, Chapter 9, pp. 491–576.

    Google Scholar 

  • Cook, T. D. and Shadish, W. R. (1994) Social experiments: Some developments over the past fifteen years. Annual Review of Psychology, 45, 545–580.

    Article  Google Scholar 

  • Cornfield, J., Haenszel, W., Hammond, E., Lilienfeld, A., Shimkin, M., and Wynder, E. (1959) Smoking and lung cancer: Recent evidence and a discussion of some questions. Journal of the National Cancer Institute, 22, 173–203.

    Google Scholar 

  • Cox, D. R. (1992) Causality: Some statistical aspects. Journal of the Royal Statistical Society, Series A, 155, 291–301.

    Article  MATH  Google Scholar 

  • Elwood, J. M. (1988) Causal Relationships in Medicine. New York: Oxford University Press.

    Google Scholar 

  • Emerson, R. M. (1981) Observational field work. Annual Review of Sociology, 7, 351 378.

    Google Scholar 

  • Freedman, D. (1997) From association to causation via regression. Advances in Applied Mathematics, 18, 59–110.

    Article  MathSciNet  MATH  Google Scholar 

  • Friedman, M. (1953) Essays in Positive Economics. Chicago: University of Chicago Press.

    Google Scholar 

  • Gastwirth, J. (1988) Statistical Reasoning in Law and Public Policy. New York: Academic Press.

    MATH  Google Scholar 

  • Gordis, L. (2000) Epidemiology (Second Edition) Philadelphia: Saunders.

    Google Scholar 

  • Greenhouse, S. (1982) Jerome Cornfield’s contributions to epidemiology. Biometrics, 28, Supplement, 33–46.

    Article  Google Scholar 

  • Heckman, J. J. (2001) Micro data, heterogeneity, and the evaluation of public policy: The Nobel lecture. Journal of Political Economy, 109, 673–748.

    Article  Google Scholar 

  • Hill, A. B. (1965) The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295–300.

    Google Scholar 

  • Holland, P. (1986) Statistics and causal inference (with discussion) . Journal of the American Statistical Association, 81, 945–970.

    Article  MathSciNet  MATH  Google Scholar 

  • Kelsey, J., Whittemore, A., Evans, A., and Thompson, W. (1996). Methods in Observational Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Khoury, M. J., Cohen, B. H., and Beaty, T. H. (1993) Fundamentals of Genetic Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Kish, L. (1987) Statistical Design for Research. New York: Wiley.

    Book  MATH  Google Scholar 

  • Lilienfeld, A. and Lilienfeld, D. E. (1980) Foundations of Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Lilienfeld, D. E. and Stolley, P. D. (1994) Foundations of Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Lipsey, M. W. and Cordray, D. S. (2000) Evaluation methods for social intervention. Annual Review of Psychology, 51, 345–375.

    Article  Google Scholar 

  • Little, R. J. and Rubin, D. B. (2000) Causal effects in clinical and epidemiological studies via potential outcomes. Annual Review of Public Health, 21, 121 145.

    Google Scholar 

  • Maclure, M. and Mittleman, M. A. (2000) Should we use a case-crossover design? Annual Review of Public Health, 21, 193–221.

    Article  Google Scholar 

  • MacMahon, B. and Pugh, T. (1970) Epidemiology. Boston: Little, Brown.

    Google Scholar 

  • MacMahon, B. and Trichopoulos, D. (1996) Epidemiology. Boston: Little, Brown.

    Google Scholar 

  • Manski, C. (1995) Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Mantel, N. and Haenszel, W. (1959) Statistical aspects of retrospective studies of disease. Journal of the National Cancer Institute, 22, 719– 748.

    Google Scholar 

  • Meyer, B. D. (1995) Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13, 151–161.

    Google Scholar 

  • Meyer, M. and Fienberg, S., eds. (1992) Assessing Evaluation Studies: The Case of Bilingual Education Strategies. Washington, DC: National Academy Press.

    Google Scholar 

  • Miettinen, O. (1985) Theoretical Epidemiology. New York: Wiley.

    Google Scholar 

  • Pearl, J. (2000) Causality: Models, Reasoning, Inference. New York: Cambridge University Press.

    Google Scholar 

  • Reichardt, C. S. (2000) A typology of strategies for ruling out threats to validity. In: Research Design: Donald Campbell’s Legacy, L. Brickman, ed., Thousand Oaks, CA: Sage, Volume 2, pp., 89–115.

    Google Scholar 

  • Reiter, J. (2000) Using statistics to determine causal relationships. American Mathematical Monthly, 107, 2432.

    Article  MathSciNet  Google Scholar 

  • Robins, J. M. (1999) Association, causation, and marginal structural models. Synthese, 121, 151 179.

    MathSciNet  Google Scholar 

  • Robins, J., Blevins, D., Ritter, G., and Wulfsohn, M. (1992) G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology, 3, 319–336.

    Article  Google Scholar 

  • Rosenthal, R. and Rosnow, R., eds. (1969) Artifact in Behavioral Research. New York: Academic.

    Google Scholar 

  • Rosenzweig, M. R. and Wolpin, K. I. (2000) Natural “natural experiments” in economics. Journal of Economic Literature, 38, 827–874.

    Article  Google Scholar 

  • Rosnow, R. L. and Rosenthal, R. (1997) People Studying People: Artifacts and Ethics in Behavioral Research. New York: W. H. Freeman.

    Google Scholar 

  • Rossi, P., Freeman, H., and Lipsey, M. W. (1999) Evaluation. Beverly Hills, CA: Sage.

    Google Scholar 

  • Rothman, K. and Greenland, S. (1998) Modern Epidemiology. Philadelphia: Lippincott-Raven.

    Google Scholar 

  • Rubin, D. (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  • Schlesselman, J. (1982) Case-Control Studies. New York: Oxford University Press.

    Google Scholar 

  • Schulte, P. A. and Perera, F. (1993) Molecular Epidemiology: Principles and Practices. New York: Academic.

    Google Scholar 

  • Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002) Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin.

    Google Scholar 

  • Shafer, G. (1996) The Art of Causal Conjecture. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Sobel, M. (1995) Causal inference in the social and behavioral sciences. In: Handbook of Statistical Modelling for the Social and Behavioral Sciences, G. Arminger, C. Clogg, and M. Sobel, eds., New York: Plenum, 1–38.

    Google Scholar 

  • Steenland, K., ed. (1993) Case Studies in Occupational Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Strom, B. (2000) Pharmacoepidemiology. New York: Wiley.

    Book  Google Scholar 

  • Suchman, E. (1967) Evaluation Research. New York: Sage.

    Google Scholar 

  • Susser, M. (1973) Causal Thinking in the Health Sciences: Concepts and Strategies in Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Susser, M. (1987) Epidemiology, Health and Society: Selected Papers. New York: Oxford University Press.

    Google Scholar 

  • Tufte, E., ed. (1970) The Quantitative Analysis of Social Problems. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Weiss, C. (1997) Evaluation. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Weiss, N. S. (1996) Clinical Epidemiology. New York: Oxford University Press.

    Google Scholar 

  • Willett, W. (1998) Nutritional Epidemiology. New York: Oxford University Press.

    Book  Google Scholar 

  • Winship, C. and Morgan, S. L. (1999) The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.

    Article  Google Scholar 

  • Zellner, A. (1968) Readings in Economic Statistics and Econometrics. Boston: Little, Brown.

    Google Scholar 

References

  • Bross, I. D. J. (1960) Statistical criticism. Cancer, 13, 394–400

    Article  Google Scholar 

  • Bross, I. D. J. (1960) Reprinted in: The Quantitative Analysis of Social Problems, E. Tufte, ed., Reading, MA: Addison-Wesley, pp. 97–108.

    Google Scholar 

  • Cameron, E. and Pauling, L. (1976) Supplemental ascorbate in the supportive treatment of cancer: Prolongation of survival times in terminal human cancer. Proceedings of the National Academy of Sciences (USA), 73, 3685–3689.

    Article  Google Scholar 

  • Chalmers, T., Block, J., and Lee, S. (1970) Controlled studies in clinical cancer research. New England Journal of Medicine, 287, 75–78.

    Article  Google Scholar 

  • Cochran, W.G. (1965) The planning of observational studies of human populations (with discussion). Journal of the Royal Statistical Society, Series A, 128, 134–155

    Google Scholar 

  • Cochran, W.G. (1965) Reprinted in Readings in Economic Statistics and Econometrics, A. Zellner, ed., 1968, Boston: Little Brown, pp. 11–36.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Doll, R. and Hill, A. (1966) Mortality of British doctors in relation to smoking: Observations on coronary thrombosis. In: Epidemiological Approaches to the Study of Cancer and Other Chronic Diseases, W. Haenszel, ed., U.S. National Cancer Institute Monograph 19, Washington, DC: US Department of Health, Education, and Welfare, pp. 205–268.

    Google Scholar 

  • Fisher, R.A. (1935, 1949) The Design of Experiments. Edinburgh: Oliver & Boyd.

    Google Scholar 

  • Fraker, T. and Maynard, R. (1987) The adequacy of comparison group designs for evaluations of employment-related programs. Journal of Human Resources, 22, 194–227.

    Article  Google Scholar 

  • Friedlander, D. and Robins, P. K. (1995) Evaluating program evaluations: New evidence on commonly used nonexperimental methods. American Economic Review, 85, 923–937.

    Google Scholar 

  • Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (1997) Hypotheticals and hypotheses. American Statistician, 51, 120–121.

    Google Scholar 

  • Herbst, A., Ulfelder, H., and Poskanzer, D. (1971) Adenocarcinoma of the vagina: Association of maternal stilbestrol therapy with tumor appearance in young women. New England Journal of Medicine, 284, 878–881.

    Article  Google Scholar 

  • Hoffer, T., Greeley, A., and Coleman, J. (1985 Achievement growth in public and Catholic schools. Sociology of Education, 58, 74–97.

    Article  Google Scholar 

  • LaLonde, R. (1986) Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604–620.

    Google Scholar 

  • Meier, P. (1972) The biggest public health experiment ever: The 1954 field trial of the Salk poliomyelitis vaccine. In: Statistics: A Guide to the Unknown, J. Tanur, ed., San Francisco: Holden-Day, pp. 2–13.

    Google Scholar 

  • Moertel, C., Fleming, T., Creagan, E., Rubin, J., O’Connell, M., and Ames, M. (1985) High-dose vitamin C versus placebo in the treatment of patients with advanced cancer who have had no prior chemotherapy: A randomized double-blind comparison. New England Journal of Medicine, 312, 137–141.

    Article  Google Scholar 

  • Popper, K. (1959) The Logic of Scientific Discovery. New York: Harper & Row.

    MATH  Google Scholar 

  • Popper, K. (1994) The Myth of the Framework. New York: Routledge.

    Google Scholar 

  • United States Surgeon General’s Advisory Committee Report (1964) Smoking and Health. Washington, DC: US Department of Health, Education and Welfare.

    Google Scholar 

  • Wittgenstein, L. (1969) On Certainty. New York: Harper & Row.

    Google Scholar 

  • Zwick, R. (1991) Effects of item order and context on estimation of NAEP reading proficiency. Educational Measurement: Issues and Practice,3, 10–16.

    Article  Google Scholar 

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Rosenbaum, P.R. (2002). Observational Studies. In: Observational Studies. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3692-2_1

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  • DOI: https://doi.org/10.1007/978-1-4757-3692-2_1

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