Risk-Set Matching

Part of the Springer Series in Statistics book series (SSS)


When a treatment may be given at various times, it is important to form matched pairs or sets in which subjects are similar prior to treatment but avoid matching on events that were subsequent to treatment. This is done using risk-set matching, in which a newly treated subject at time t is matched to one or more controls who are not yet treated at time t based on covariate information describing subjects prior to time t.


Propensity Score Neonatal Intensive Care Unit Propensity Score Match Interstitial Cystitis Gang Membership 
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. American Academy of Pediatrics.: Hospital discharge of the high-risk neonate – proposed guidelines. Pediatrics 102, 411–17 (1998)Google Scholar
  2. Cox, D.R.: Prediction by exponentially weighted moving averages and related methods. J R Statist Soc B 23, 414–422 (1961)MATHGoogle Scholar
  3. Cox, D.R.: Regression models and life-tables. J Roy Statist Soc B 34, 187–220.Google Scholar
  4. Derigs, U.: Solving nonbipartite matching problems by shortest path techniques. Ann Operat Res 13, 225–261 (1988)CrossRefMathSciNetGoogle Scholar
  5. Gail, M.H. : Does cardiac transplantation prolong life? A reassessment. Ann Intern Med 76, 815–817 (1972)Google Scholar
  6. Haviland, A.M., Nagin, D.S. : Causal inferences with group based trajectory models, Psychometrika 70, 557–578 (2005)CrossRefMathSciNetGoogle Scholar
  7. Haviland, A.M., Nagin, D.S., Rosenbaum, P.R.: Combining propensity score matching and group-based trajectory analysis in an observational study. Psychol Methods 12, 247–267 (2007)CrossRefGoogle Scholar
  8. Haviland, A.M., Nagin, D.S., Rosenbaum, P.R., Tremblay, R.E. : Combining group-based trajectory modeling and propensity score matching for causal inferences in nonexperimental longitudinal data. Dev Psychol 44, 422–436 (2008)CrossRefGoogle Scholar
  9. Li, Y.F.P., Propert, K.J., Rosenbaum, P.R.: Balanced risk set matching. J Am Statist Assoc 96, 870–882 (2001)MATHCrossRefMathSciNetGoogle Scholar
  10. Lu, B. : Propensity score matching with time-dependent covariates. Biometrics 61, 721–728 (2005)MATHCrossRefMathSciNetGoogle Scholar
  11. Marcus, S.M., Siddique, J., Ten Have, T.R., Gibbons, R.D., Stuart, E., and Normand, S-L.: Balancing treatment comparisons in longitudinal studies. Psychiatric Ann 38, 805–812 (2008)CrossRefGoogle Scholar
  12. Messmer, B.J., Nora, J.J., Leachman, R.D., et al: Survival-times after cardiac allografts. Lancet 1, 954–956 (1969)CrossRefGoogle Scholar
  13. Nagin, D.S.: Group-Based Modeling of Development. Cambridge, MA: Harvard University Press (2005)Google Scholar
  14. Nieuwbeerta, P., Nagin, D.S., Blokland, A.A.J.: The relationship between first imprisonment and criminal career development: A matched samples comparison. J Quant Criminol, to appear.Google Scholar
  15. 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
  16. Robins, J.M., Blevins, D., Ritter, G., Wulfsohn, M. : G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 3, 319–336.Google Scholar
  17. 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
  18. 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
  19. 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
  20. Suissa, S.: Immortal time bias in pharmacoepidemiology. Am J Epidemiol 167, 492–499 (2008)CrossRefGoogle Scholar
  21. Tremblay, R.E., Desmarais-Gervais, L., Gagnon, C., Charlebois, P.: The preschool behavior questionnaire: Stability of its factor structure between culture, sexes, ages, and socioeconomic classes. Int J Behav Devl 10, 467–484 (1987)Google Scholar
  22. van der Laan, M., Robins, J.: Unified Methods for Censored Longitudinal Data and Causality. New York: Springer (2003)MATHGoogle Scholar
  23. Wermink, H., Blokland, A., Nieubeerta, P., Nagin, D., Tollenaar, N.: Comparing the effects of community service and imprisonment on recidivism: A matched samples approach. Manuscript.Google Scholar
  24. Wu, W., West, S.G., Hughes, J.N. : Effect of retention in first grade on children’s achievement trajectories over 4 years: A piecewise growth analysis using propensity score matching. J Educ Psychol 100, 727–740 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Statistics Department Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations