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Matching estimators for the effect of a treatment on survival times

Author

Listed:
  • de Luna, Xavier

    (Department of Statistics, Umeå University)

  • Johansson, Per

    (Institute for Labour Market Policy Evaluation)

Abstract

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.

Suggested Citation

  • de Luna, Xavier & Johansson, Per, 2007. "Matching estimators for the effect of a treatment on survival times," Working Paper Series 2007:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2007_001
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    References listed on IDEAS

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    1. 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.
    2. Glenn Heller & E. S. Venkatraman, 2004. "A nonparametric test to compare survival distributions with covariate adjustment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 719-733, August.
    3. 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.
    4. Fredriksson, Peter & Johansson, Per, 2004. "Dynamic Treatment Assignment – The Consequences for Evaluations Using Observational Data," IZA Discussion Papers 1062, Institute of Labor Economics (IZA).
    5. 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, September.
    6. 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.
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    Citations

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    Cited by:

    1. Bruno Crépon & Marc Ferracci & Grégory Jolivet & Gerard J. van den Berg, 2009. "Active Labor Market Policy Effects in a Dynamic Setting," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 595-605, 04-05.
    2. Anders Stenberg & Xavier Luna & Olle Westerlund, 2012. "Can adult education delay retirement from the labour market?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 25(2), pages 677-696, January.
    3. Forslund, Anders & Liljeberg, Linus & von Trott zu Solz, Leah, 2013. "Job practice: an evaluation and a comparison with vocational labour market training programmes," Working Paper Series 2013:6, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    4. Anders Stenberg & Olle Westerlund, 2013. "Education and retirement: does University education at mid-age extend working life?," IZA Journal of European Labor Studies, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-22, December.
    5. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.

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    More about this item

    Keywords

    Effect of a treatment; treatment;

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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