Matching estimators for the effect of a treatment on survival times
We perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is time between the entrance in the study and treatment. This can be done only when sufficient data is available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of the waiting times. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D. B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study.
|Date of creation:||16 Jan 2007|
|Publication status:||Published as de Luna, Xavier and Per Johansson, 'Matching estimators for the effect of a treatment on survival times' in Journal of Statistical Planning and Inference, 2010, pages 2122-2137.|
|Contact details of provider:|| Postal: IFAU, P O Box 513, SE-751 20 Uppsala, Sweden|
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References listed on IDEAS
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- Fredriksson, Peter & Johansson, Per, 2004. "Dynamic Treatment Assignment – The Consequences for Evaluations Using Observational Data," IZA Discussion Papers 1062, Institute for the Study of Labor (IZA).
- Forslund, Anders & Johansson, Per & Lindqvist, Linus, 2004. "Employment subsidies - A fast lane from unemployment to work?," Working Paper Series 2004:18, IFAU - Institute for Evaluation of Labour Market and Education Policy.
- Guido W. Imbens, 2004.
"Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review,"
The Review of Economics and Statistics,
MIT Press, vol. 86(1), pages 4-29, February.
- Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
- Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, 09.
- Hernan M. A & Brumback B. & Robins J. M, 2001. "Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 440-448, June. Full references (including those not matched with items on IDEAS)