Using Propensity Score Subclassification for Multiple Treatment Doses to Evaluate a National Antidrug Media Campaign

In 1998, the U.S. Office of National Drug Control Policy launched a national media campaign in an effort to reduce and prevent drug use among young Americans. Because the campaign was implemented nationwide, there is no control group available for use in evaluating the effects of the campaign. Nevertheless, it is possible to use propensity score methods to evaluate the effects of the campaign. However, because teens receive varying degrees of exposure to the media campaign, it is necessary to apply propensity score methods that accommodate multiple treatment doses. This work extends that of previous authors to subclassification on the propensity score for observational studies with multiple treatment doses, rather than matching on the propensity score, and proposes modifications to accommodate complex survey data. This methodology is illustrated using data from a pilot study for the media campaign evaluation.

[1]  W. G. Cochran The effectiveness of adjustment by subclassification in removing bias in observational studies. , 1968, Biometrics.

[2]  D. Rubin Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies , 1973 .

[3]  D. Rubin,et al.  Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies , 1978 .

[4]  P. McCullagh Regression Models for Ordinal Data , 1980 .

[5]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[6]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[7]  P. Rosenbaum The Consequences of Adjustment for a Concomitant Variable that Has Been Affected by the Treatment , 1984 .

[8]  D. Rubin,et al.  The bias due to incomplete matching. , 1983, Biometrics.

[9]  P. Rosenbaum Dropping out of High School in the United States: An Observational Study , 1986 .

[10]  R. Little Survey Nonresponse Adjustments for Estimates of Means , 1986 .

[11]  P. Rosenbaum Model-Based Direct Adjustment , 1987 .

[12]  Ulrich Derigs,et al.  Solving non-bipartite matching problems via shortest path techniques , 1988 .

[13]  Roderick J. A. Little,et al.  Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics , 1992 .

[14]  C. Drake Effects of misspecification of the propensity score on estimators of treatment effect , 1993 .

[15]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .

[16]  D B Rubin,et al.  Matching using estimated propensity scores: relating theory to practice. , 1996, Biometrics.

[17]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[18]  R. D'Agostino Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[19]  P. Rosenbaum,et al.  Invited commentary: propensity scores. , 1999, American journal of epidemiology.

[20]  On estimating the causal effects of DNR orders. , 1999, Medical care.

[21]  G. Imbens The Role of the Propensity Score in Estimating Dose-Response Functions , 1999 .

[22]  D. Rubin,et al.  Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates , 2000 .

[23]  D. Rubin,et al.  Estimating and Using Propensity Scores with Partially Missing Data , 2000 .

[24]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .

[25]  Xiao-Hua Zhou,et al.  The use of propensity scores in pharmacoepidemiologic research , 2000, Pharmacoepidemiology and drug safety.

[26]  Elaine L. Zanutto,et al.  Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse , 2001, Journal of the American Statistical Association.

[27]  A M Zaslavsky,et al.  Racial disparity in influenza vaccination: does managed care narrow the gap between African Americans and whites? , 2001, JAMA.

[28]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2002 .

[29]  Elaine L. Zanutto,et al.  Evaluation of the National Youth Anti-Drug Media Campaign: Fifth Semi-Annual Report of Findings , 2003 .

[30]  D. Benjamin Does 401(k) Eligibility Increase Saving? Evidence from Propensity Score Subclassification , 2003 .

[31]  Kosuke Imai,et al.  Causal Inference With General Treatment Regimes , 2004 .

[32]  Roberto Agodini,et al.  Are Experiments the Only Option? A Look at Dropout Prevention Programs , 2004, Review of Economics and Statistics.

[33]  William R Shadish,et al.  Propensity Scores , 2005, Evaluation review.