Planning the Analysis

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


“Make your theories elaborate” in observational studies, argued R.A. Fisher, so that the many predictions of such a theory may disambiguate the association between treatment and outcome. How should one plan the analysis of an observational study to check the predictions of an elaborate theory?


Interstitial Cystitis Statist Assoc Testing Plan Disjoint Interval False Rejection 
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  1. Bauer P.: Multiple testing in clinical trials. Statist Med 10, 871–890 (1991)CrossRefGoogle Scholar
  2. Bauer, P., Kieser, M.: A unifying approach for confidence intervals and testing of equivalence and difference. Biometrika 83, 934–937 (1996)MATHCrossRefMathSciNetGoogle Scholar
  3. Bauer, P.: A note on multiple testing procedures in dose finding. Biometrics 53, 1125–1128 (1997)MATHCrossRefGoogle Scholar
  4. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate. J Roy Statist Soc B 57, 289–300 (1995)MATHMathSciNetGoogle Scholar
  5. Berger, R.L.: Multiparameter hypothesis testing and acceptance sampling. Technometrics 24, 295–300 (1982)MATHCrossRefMathSciNetGoogle Scholar
  6. Berger, R.L., Hsu, J.C.: Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statist Sci 11, 283–319 (1996)MATHCrossRefMathSciNetGoogle Scholar
  7. Bilban, M., Jakopin, C.B. : Incidence of cytogenetic damage in lead-zinc mine workers exposed to radon. Mutagenesis 20, 187–191 (2005)CrossRefGoogle Scholar
  8. Bunker, J.P., Barnes, B.A., Mosteller, F.: Costs, Risks and Benefits of Surgery. Oxford: Oxford University Press (1977)Google Scholar
  9. Cochran, W.G.: The planning of observational studies of human populations (with Discussion). J Roy Statist Soc A 128, 234–265 (1965)Google Scholar
  10. Cox, D.R.: Causality: Some statistical aspects. J Roy Statist Soc A 155, 291–301 (1992)MATHCrossRefGoogle Scholar
  11. Gail, M.: Statistics in action. J Am Statist Assoc 91, 1–13 (1996)CrossRefGoogle Scholar
  12. Heller, R., Rosenbaum, P.R., Small, D. : Split samples and design sensitivity in observational studies. J Am Statist Assoc 104, to appear (2009)Google Scholar
  13. Holm, S.: A simple sequentially rejective multiple test procedure. Scand J Statist 6, 65–70 (1979)MATHMathSciNetGoogle Scholar
  14. Hommel, G., Kropf, S.: Tests for differentiation in gene expression using a data-driven order or weights for hypotheses. Biomet J 47, 554–562 (2005)CrossRefMathSciNetGoogle Scholar
  15. Hsu, J.C., Berger, R.L.: Stepwise confidence intervals without multiplicity adjustment for dose-response and toxicity studies. J Am Statist Assoc 94, 468–475 (1999)CrossRefGoogle Scholar
  16. Hsu, J.C., Hwang, J.T.G., Liu, H-K., Ruberg, S.J.: Confidence intervals associated with tests for bioequivalence. Biometrika 81, 103–114 (1994)MATHCrossRefMathSciNetGoogle Scholar
  17. Koch, G.G., Gansky, S.A.: Statistical considerations for multiplicity in confirmatory protocols. Drug Inform J 30, 523–533 (1996)Google Scholar
  18. Laska, E.M., Meisner, M.J.: Testing whether an identified treatment is best. Biometrics 45, 1139–1151 (1989)MATHCrossRefMathSciNetGoogle Scholar
  19. Lehmacher, W., Wassmer, G., Reitmeir, P.: Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate. Biometrics 47, 511–521 (1991)CrossRefGoogle Scholar
  20. Lehmann, E.L.: Testing multiparameter hypotheses. Ann Math Statist 23, 541–552 (1952)MATHCrossRefMathSciNetGoogle Scholar
  21. Li, Y.F.P., Propert, K.J., Rosenbaum, P.R.: Balanced risk set matching. J Am Statist Assoc 96, 870–882 (2001)MATHCrossRefMathSciNetGoogle Scholar
  22. Marcus, R., Peritz, E., Gabriel, K.R.: On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63, 655–60 (1976)MATHCrossRefMathSciNetGoogle Scholar
  23. Masjedi, M.R., Heidary, A., Mohammadi, F., Velayati, A.A., and Dokouhaki, P.: Chromosomal aberrations and micronuclei in lymphocytes of patients before and after exposure to anti-tuberculosis drugs. Mutagenesis 15, 489–494 (2000)CrossRefGoogle Scholar
  24. McPherson, K., Bunker, J.P.: Costs, Risks and Benefits of Surgery: A milestone in the development of health services research. J Roy Soc Med 100, 387–390 (2007)CrossRefGoogle Scholar
  25. Propert, K.J., Schaeffer, A.J., Brensinger, C.M., Kusek, J.W., Nyberg, L.M., Landis, J.R. : A prospective study of interstitial cystitis: Results of longitudinal followup of the interstitial cystitis data base cohort. J Urol 163, 1434–1439. (2000)CrossRefGoogle Scholar
  26. Rosenbaum, P.R.: From association to causation in observational studies. J Am Statist Assoc 79, 41–48 (1984)MATHCrossRefMathSciNetGoogle Scholar
  27. Rosenbaum, P.R.: Observational Studies (2nd ed). New York: Springer (2002)MATHGoogle Scholar
  28. Rosenbaum, P.R.: Comment on a paper by Donald B. Rubin: The place of death in the quality of life. Statist Sci 21, 313–316 (2006)CrossRefMathSciNetGoogle Scholar
  29. Rosenbaum, P.R.: Testing hypotheses in order. Biometrika 95, 248–252 (2008)MATHCrossRefMathSciNetGoogle Scholar
  30. Rosenbaum, P.R., Silber, J.H.: Sensitivity analysis for equivalence and difference in an observational study of neonatal intensive care units. J Am Statist Assoc 104, 501–511 (2009)CrossRefGoogle Scholar
  31. Schuirmann, D.L.: On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval. Biometrics 37, 617 (1981)Google Scholar
  32. Shaffer, J.P.: Modified sequentially rejective multiple test procedures. J Am Statist Assoc 81, 826–831 (1986)MATHCrossRefGoogle Scholar
  33. Silber, J.H., Lorch, S.L., Rosenbaum, P.R., Medoff-Cooper, B., Bakewell-Sachs, S., Millman, A., Mi, L., Even-Shoshan, O., Escobar, G.E. : Additional maturity at discharge and subsequent health care costs. Health Serv Res 44, 444–463 (2009)CrossRefGoogle Scholar
  34. Tamhane, A., Logan, B.: A superiority-equivalence approach to one-sided tests on multiple endpoints in clinical trials. Biometrika 91, 715–727 (2004)MATHCrossRefMathSciNetGoogle Scholar
  35. Tukey, J.W.: We need both exploratory and confirmatory. Am Statistician 34, 23–25 (1980)CrossRefGoogle Scholar
  36. Tukey, J.W.: Sunset salvo. Am Statistician 40, 72–76 (1986)CrossRefGoogle Scholar
  37. Westlake, W.J.: Response to Kirkwood. Biometrics 37, 591–593 (1981)Google Scholar
  38. Yoon, F.: New Methods for the Design and Analysis of Observational Studies. Doctoral Thesis, Department of Statistics, University of Pennsylvania.Google Scholar

Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Statistics Department Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA

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