Articles |
The prognostic analogue of the propensity score
Statistics Department, University of Michigan 439 West Hall, Ann Arbor, Michigan 48109, U.S.A. ben.b.hansen{at}umich.edu
Received for publication 1 June 2006. Revision received 1 November 2007.
The propensity score collapses the covariates of an observational study into a single measure summarizing their joint association with treatment conditions; prognostic scores summarize covariates' association with potential responses. As with propensity scores, stratification on prognostic scores brings to uncontrolled studies a concrete and desirable form of balance, a balance that is more familiar as an objective of experimental control. Like propensity scores, prognostic scores can reduce the dimension of the covariate, yet causal inferences conditional on them are as valid as are inferences conditional only on the unreduced covariate. As a method of adjustment unto itself, prognostic scoring has limitations not shared with propensity scoring, but it holds promise as a complement to the propensity score, particularly in certain designs for which unassisted propensity adjustment is difficult or infeasible.
Key Words: Covariate balance Matched sampling Matching Observational study Quasi-experiment Regression discontinuity Subclassification
References
-
Barsky R., Bound J., Charles K. K., Lupton J. P. Accounting for the black-white wealth gap: a nonparametric approach. J. Am. Statist. Assoc. (2002) 97:663–74.[CrossRef][Web of Science]
Belson W. A. A technique for studying the effects of a television broadcast. Appl. Statist. (1956) 5:195–202.[CrossRef]
Berk R. A., de Leeuw J. An evaluation of California's inmate classification system using a generalized regression discontinuity design. J. Am. Statist. Assoc. (1999) 94:1045–52.[CrossRef][Web of Science]
Campbell D., Stanley J. Experimental and Quasi-Experimental Designs for Research (1966) Boston: Houghton Mifflin.
Cochran W. G. The use of covariance in observational studies. Appl. Statist. (1969) 18:270–5.[CrossRef]
Cox D. R., Hinkley D. V. Theoretical Statistics (1974) London: Chapman & Hall.
Dehejia R., Wahba S. Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. J. Am. Statist. Assoc. (1999) 94:1053–62.[CrossRef][Web of Science]
Drake C. Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics (1993) 49:1231–6.[CrossRef][Web of Science]
Gastwirth J., Greenhouse S. Biostatistical concepts and methods in the legal setting. Statist. Med. (1995) 14:1641–53.[CrossRef]
Hahn J., Todd P., Van der Klaauw W. Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica (2001) 69:201–9.[CrossRef][Web of Science]
Heckman J. Instrumental variables: a study of implicit behavioral assumptions in one widely used estimator. J. Hum. Resour. (1997) 32:441–62.[CrossRef]
Heckman J. J., Ichimura H., Todd P. E. Matching as an econometric evaluation estimator. Rev. Econ. Studies (1998) 65:261–94.[CrossRef][Web of Science]
Hodges J. L., Lehmann E. L. Estimates of location based on rank tests. Ann. Math. Statist. (1963) 34:598–611.[CrossRef]
Holland P. W. Statistics and causal inference (with Discussion). J. Am. Statist. Assoc. (1986) 81:945–70.[CrossRef][Web of Science]
Imbens G. W. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev. Econ. Statist. (2004) 86:4–29.[CrossRef][Web of Science]
Kurth T., Walker A., Glynn R., Chan K., Gaziano J., Berger K., Robins J. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am. J. Epidemiol. (2006) 163:262–70.
McCullagh P., Nelder J. A. Generalized Linear Models (1989) 2nd ed. London: Chapman and Hall.
Miettinen O. S. Stratification by a multivariate confounder score. Am. J. Epidemiol. (1976) 104:609–20.
Neyman J. On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statist. Sci. Translated by Dabrowska D. M., Speed T. P. (1990) 5:463–80. Translated by.
Pace L., Salvan A. Principles of Statistical Inference: From a Neo-Fisherian Perspective (1997) Singapore: World Scientific.
Peters C. C. A method of matching groups for experiment with no loss of population. J. Educ. Res. (1941) 34:606–12.
Rosenbaum P. R., Rubin D. B. The central role of the propensity score in observational studies for causal effects. Biometrika (1983) 70:41–55.
Rubin D. B. Assignment to treatment group on the basis of a covariate. J. Educ. Statist. (1977) 2:1–26. (Correction (1978), 3, 384).[CrossRef]
Rubin D. B. William G. Cochran's contributions to the design, analysis, and evaluation of observational studies. In: W. G. Cochran's Impact on Statistics—Rao P. S., Sedransk J., eds. (1984) New York: Wiley. 37–69.
Rubin D. B., Stuart E. A. Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions. Ann. Statist. (2006) 34:1814–26.[CrossRef]
Rubin D. B., Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. J. Am. Statist. Assoc. (2000) 95:573–85.[CrossRef][Web of Science]
Silber J., Rosenbaum P., Trudeau M., Even-Shoshan O., Chen W., Zhang X., Mosher R. Multivariate matching and bias reduction in the surgical outcomes study. Med. Care (2001) 39:1048–64.[CrossRef][Web of Science][Medline]
Zhao Z. Using matching to estimate treatment effects: data requirements, matching metrics, and Monte Carlo evidence. Rev. Econ. Statist. (2004) 86:91–107.[CrossRef][Web of Science]
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