Advertisement

Multiple Control Groups

  • Paul R. Rosenbaum
Part of the Springer Series in Statistics book series (SSS)

Abstract

An observational study has multiple control groups if it has several distinct groups of subjects who did not receive the treatment. In a randomized experiment, every control is denied the treatment for the same reason, namely, the toss of a coin. In an observational study, there may be several distinct ways that the treatment is denied to a subject. If these several control groups have outcomes that differ substantially and significantly, then this cannot reflect an effect of the treatment, since no control subject received the treatment. It must reflect, instead, some form of bias.

Keywords

Ectopic Pregnancy Intrauterine Device Tubal Pregnancy Hide Bias Compensatory Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bitterman, M. (1965). Phyletic differences in learning. American Psychologist, 20, 396–410.CrossRefGoogle Scholar
  2. Campbell, D. (1969). Prospective: Artifact and Control. In Artifact in Behavioral Research (eds., R. Rosenthal and R. Rosnow). New York: Academic Press, pp. 351–382.Google Scholar
  3. Campbell, D. and Boruch, R. (1975). Making the case for randomized assignment to treatments by considering the alternatives: Six ways in which quasi-experimental evaluations in compensatory education tend to underestimate effects. In: Evaluation and Experiment (eds., C. Bennett and A. Lumsdaine). New York: Academic Press, pp. 195–296.Google Scholar
  4. Campbell, D. and Stanley, J. (1963). Experimental and Quasi-Experimental Designs for Research. Chicago: Rand McNally.Google Scholar
  5. Cox, D.R. (1970). The Analysis of Binary Data. London: Methuen.MATHGoogle Scholar
  6. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.CrossRefGoogle Scholar
  7. Hollander, M. and Wolfe, D. (1973). Nonparametric Statistical Methods. New York: Wiley.MATHGoogle Scholar
  8. Kruskal, W. and Wallis, W. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583–621.MATHCrossRefGoogle Scholar
  9. Lehmann, E.L. (1975). Nonparametrics: Statistical Methods Based on Ranks. San Francisco: Holden-Day.MATHGoogle Scholar
  10. Lilienfeld, A., Chang, L., Thomas, D., and Levin, M. (1976). Rauwolfia derivatives and breast cancer. Johns Hopkins Medical Journal, 139, 41–50.Google Scholar
  11. Lilienfeld, A. and Lilienfeld, D. (1980). Foundations of Epidemiology (second edition). New York: Oxford University Press.Google Scholar
  12. Maclure, M. and Greenland, S. (1992). Tests for trend and dose-response: Misinterpretations and alternatives. American Journal of Epidemiology, 135, 96–104.Google Scholar
  13. Petersson, B., Trell, E., Kristenson, H. (1982). Alcohol abstention and premature mortality in middle aged men. British Medical Journal, 285, 1457–1459.CrossRefGoogle Scholar
  14. Rosenbaum, P.R. (1984). From association to causation in observational studies. Journal of the American Statistical Association, 79, 41–48.MathSciNetMATHCrossRefGoogle Scholar
  15. Rosenbaum, P.R. (1987). The role of a second control group in an observational study (with Discussion). Statistical Science, 2, 292–316.CrossRefGoogle Scholar
  16. Rosenbaum, P.R. (1989a). On permutation tests for hidden biases in observational studies: An application of Holley’s inequality to the Savage lattice. Annals of Statistics, 17, 643–653.MathSciNetMATHCrossRefGoogle Scholar
  17. Rosenbaum, P.R. (1989b). Sensitivity analysis for matched observational studies with many ordered treatments. Scandinavian Journal of Statistics, 16, 227–236.MathSciNetMATHGoogle Scholar
  18. Rossing, M., Daling, J., Voigt, L., Stergachis, A., and Weiss, N. (1993). Current use of an intrauterine device and risk of tubal pregnancy. Epidemiology, 4, 252–258.CrossRefGoogle Scholar
  19. Seltser, R. and Sartwell, P. (1965). The influence of occupational exposure to radiation on the mortality of American radiologists and other medical specialists. American Journal of Epidemiology, 81, 2–22.Google Scholar
  20. Solomon, R. (1949). An extension of control group design. Psychological Bulletin, 137–150.Google Scholar
  21. Weiss, N. (1981). Inferring causal relationships: Elaboration of the criterion of “doseresponse.” American Journal of Epidemiology, 113, 487–490.Google Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Paul R. Rosenbaum
    • 1
  1. 1.Department of StatisticsUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations