After Matching, Before Analysis

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


Three design tasks may usefully follow matching and precede planning of the analysis. Splitting the sample of I pairs into a small planning sample and a large analysis sample may aid in planning the analysis in a manner that increases the design sensitivity. If there will be analytic adjustments for some unmatched variables, it is prudent to check that the matched samples exhibit sufficient overlap on unmatched variables to permit analytic adjustments. Quantitative analysis of matched samples may usefully be combined with qualitative examination and narrative description of a few closely matched pairs.


Treatment Assignment Statist Assoc Matched Sample Design Sensitivity Thick Description 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Athens, L. : Violent Criminal Acts and Actors Revisited. Urbana, Illinois: University of Illinois Press (1997)Google Scholar
  2. Becker, H.S.: The epistemology of qualitative research. In: R. Jessor, A. Colby, R. Shweder (eds.), Ethnography and Human Development, pp. 53–72. Chicago: University of Chicago Press (1996)Google Scholar
  3. Bosk, C.L.: Forgive and Remember: Managing Medical Failure. Chicago: University of Chicago Press (1981)Google Scholar
  4. Cochran, W.G.: Analysis of covariance: Its nature and uses. Biometrics 13, 261–281 (1957)CrossRefMathSciNetGoogle Scholar
  5. Coleman, J., Hoffer, T., Kilgore, S.: Cognitive outcomes in public and private schools. Soc Educ 55, 65–76 (1982)CrossRefGoogle Scholar
  6. Cox, D.R.: A note on data-splitting for the evaluation of significance levels. Biometrika 62, 441–444 (1975)MATHCrossRefMathSciNetGoogle Scholar
  7. Daniel, S., Armstrong, K., Silber, J.H., Rosenbaum, P.R.: An algorithm for optimal tapered matching, with application to disparities in survival. J Comput Graph Statist 17, 914–924 (2008)CrossRefGoogle Scholar
  8. Erasmus: The Collected Works of Erasmus, Volume 34, Adages IIvii1 to IIIiii100. Toronto: University of Toronto Press (1992)Google Scholar
  9. Estroff, S.E.: Making It Crazy: An Ethnography of Psychiatric Clients in an American Community. Berkeley: University of California Press (1985)Google Scholar
  10. Bittner, E., Garfinkel, H.: ‘Good’ organizational reasons for ‘bad’ organizational records. In: H. Garfinkel, Studies in Ethnomethodology, pp. 186–207. Englewood Cliffs, NJ: Prentice Hall (1967).Google Scholar
  11. Geertz, C.: Thick description: toward an interpretative theory of culture. In: C. Geertz, The Interpretation of Cultures, pp. 3–30. New York: Basic Books (1973)Google Scholar
  12. Geertz, C.: Local Knowledge. New York: Basic Books (1983)Google Scholar
  13. Goldberger, A.S., Cain, G.S.: The causal analysis of cognitive outcomes in the Coleman, Hoffer and Kilgore report. Soc Educ 55, 103–122 (1982)CrossRefGoogle Scholar
  14. 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
  15. Rosenbaum, P.R.: The consequences of adjustment for a concomitant variable that has been affected by the treatment. J Roy Statist Soc A 147, 656–666 (1984)CrossRefGoogle Scholar
  16. Rosenbaum, P.R.: Conditional permutation tests and the propensity score in observational studies. J Am Statist Assoc 79, 565–574 (1984)CrossRefMathSciNetGoogle Scholar
  17. Rosenbaum, P.R.: Permutation tests for matched pairs with adjustments for covariates. Appl Statist 37, 401–411 (1988) (Correction: [20, §3])Google Scholar
  18. Rosenbaum, P.R., Silber, J.H.: Matching and thick description in an observational study of mortality after surgery. Biostatistics 2, 217–232 (2001)MATHCrossRefGoogle Scholar
  19. Rosenbaum, P.R.: Covariance adjustment in randomized experiments and observational studies (with Discussion). Statist Sci 17, 286–327 (2002)MATHCrossRefMathSciNetGoogle Scholar
  20. Rosenbaum, P.R.: Observational Studies (2nd ed). New York: Springer (2002)MATHGoogle Scholar
  21. Rosenbaum, P.R.: Design sensitivity in observational studies. Biometrika 91, 153–164 (2004)MATHCrossRefMathSciNetGoogle Scholar
  22. Rosenbaum, P.R.: Heterogeneity and causality: Unit heterogeneity and design sensitivity in observational studies. Am Statistician 59, 147–152 (2005)CrossRefMathSciNetGoogle Scholar
  23. Rosenbaum, P.R.: What aspects of the design of an observational study affect its sensitivity to bias from covariates that were not observed? In: Festshrift for Paul W. Holland. Princeton, NJ: ETS (2009)Google Scholar
  24. 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
  25. Ryle, G.: Collected Papers, Volume 2. London: Hutchinson (1971)Google Scholar
  26. Silber, J.H., Rosenbaum, P.R., Trudeau, M.E., Even-Shoshan, O., Chen, W., Zhang, X., Mosher, R.E. : Multivariate matching and bias reduction in the surgical outcomes study. Med Care 39, 1048–1064 (2001)CrossRefGoogle Scholar
  27. Silber, J.H., Rosenbaum, P. R., Trudeau, M.E., Chen, W., Zhang, X., Lorch, S.L., Rapaport-Kelz, R., Mosher, R.E, Even-Shoshan, O.: Preoperative antibiotics and mortality in the elderly. Ann Surg 242, 107–114 (2005)CrossRefGoogle Scholar
  28. Silber, J.H., Rosenbaum, P.R., Zhang, X., Even-Shoshan, O.: Estimating anesthesia and surgical time from medicare anesthesia claims. Anesthesiology 106, 346–355 (2007)CrossRefGoogle Scholar
  29. 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
  30. Small, D.S., Rosenbaum, P.R.: War and wages: The strength of instrumental variables and their sensitivity to unobserved biases. J Am Statist Assoc 103, 924–933 (2008)CrossRefMathSciNetGoogle Scholar
  31. Stone, M.: Cross-validatory choice and assessment of statistical predictions. J Roy Statist Soc B 36, 111–147 (1974)MATHGoogle Scholar
  32. Tukey, J.W.: Exploratory Data Analysis. Reading, MA: Addison-Wesley (1977)MATHGoogle Scholar
  33. Vandenbroucke, J.: In defence of case reports and case series. Ann Intern Med 134, 330–334 (2001)Google Scholar

Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Statistics Department Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations