Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences

Journal of Statistical Planning and Inference

Volumn 140, Issue 7, 2010, Pages 2122-2137

  de Luna, Xavier ^a   Johansson, Per ^b,c  

^a UMEÅ UNIVERSITY   (Sweden)

^b UPPSALA UNIVERSITY   (Sweden)

^c Institute for Labour Market Policy Evaluation (IFAU)   (Sweden)

Author keywords

Employment subsidy; Heart transplant; Matching estimator; Observational study; Potential outcome; Survival function

Indexed keywords

EID: 77949261538     PISSN: 03783758     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jspi.2010.02.012     Document Type: Article

References (28)

1. Abbring J.H., and van der Berg G.J. The non-parametric identification of treatment effects in duration models. Econometrica 71 (2003) 1491-1518
2. Breslow N., and Crowley J. A large sample study of the life table and product limit estimates under random censorship. The Annals of Statistics 2 (1974) 437-453
3. Cochran W.G., and Rubin D.B. Controlling bias in observational studies: a review. Sankhya Series A 35 (1973) 417-446
4. Crowley J., and Hu M. Covariance analysis of heart transplant data. Journal of the American Statistical Association 72 (1977) 27-36
5. Dawid A.P. Conditional independence in statistical theory. Journal of the Royal Statistical Society Series B 41 (1979) 1-31
6. Forslund, A., Johansson, P., Lindqvist, L., 2004. Employment subsidies: a fast lane from unemployment to work?. Working Paper 2004:18, Institute for Labour Policy Evaluation, Uppsala.
7. Fredriksson, P., Johansson, P., 2007. Dynamic treatment assignment - The consequences for evaluations using observational data. Journal of Business and Economic and Statistics 26, 435-445.
8. Greenwood M. The natural duration of cancer. Reports on Public Health and Medical Subjects 33 (1926) 1-26 (His Majesty's Stationery Office: London)
9. Heller G., and Venkatraman E.S. A nonparametric test to compare survival distributions with covariance adjustment. Journal of the Royal Statistical Society Series B 66 (2004) 719-733
10. Hernán M., Brumback B., and Robins J.M. Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association 96 (2001) 440-448
11. Holland P. Statistics and causal inference, with discussion. Journal of the American Statistical Association 81 (1986) 945-970
12. Imbens G.W. Nonparametric estimation of average treatment effects under exogeneity: a review. The Review of Economics and Statistics 86 (2004) 4-29
13. Kalbfleisch J.D., and Prentice R.L. The Statistical Analysis of Failure Time Data (1980), New York, Wiley
14. Kaplan E.L., and Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53 (1958) 457-481
15. Keiding N. Historical controls and modern survival analysis. Lifetime Data Analysis 1 (1995) 19-25
16. Lechner M. Earnings and employment effects of continuous off-the-job training in East Germany after unification. Journal of Business and Economic Statistics 17 (1999) 74-90
17. Neyman J. On the application of probability theory to agricultural experiments. Essay on principles Translated by D.M. Dabrowska and edited by T.P. Speed. Statistical Science 5 (1990) 465-472
18. Robins J.M. Marginal structural models versus structural nested models as tools for causal inference. In: Halloran M.E., and Berry D. (Eds). Statistical Models in Epidemiology: The Environment and Clinical Trials, IMA vol. 116 (1999), Springer-Verlag, New York 95-135
19. Rosenbaum P.R., and Rubin D.B. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1983) 41-55
20. Rosenbaum P.R., and Rubin D.B. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79 (1984) 516-524
21. Rosenbaum P.R., and Rubin D.B. Constructing a control group using multivariate matched sampling incorporating the propensity score. American Statistician 39 (1985) 33-38
22. Rubin D.B. Matching to remove bias in observational studies. Biometrics 29 (1973) 159-183
23. Rubin D.B. The use of matched sampling and regression adjustments to remove bias in observational studies. Biometrics 29 (1973) 185-203
24. Rubin D.B. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66 (1974) 688-701
25. Rubin D.B. Neyman (1923) and causal inference in experiments and observational studies. Statistical Science 5 (1990) 472-480
26. Rubin D.B. Formal modes of statistical inference for causal effects. Journal of Statistical Planning and Inference 25 (1990) 279-292
27. Rubin D.B. Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. Biometrics 47 (1991) 1213-1234
28. Sianesi B. An evaluation of the Swedish system of active labour market programmes in the 1990s. Review of Economics and Statistics 86 (2004) 133-155