Criteria for surrogate end points

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 69, Issue 5, 2007, Pages 919-932

  Chen, Hua ^a   Geng, Zhi ^a   Jia, Jinzhu ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

Causal inference; Causal network; Surrogate end point

Indexed keywords

EID: 35648955054     PISSN: 13697412     EISSN: 14679868     Source Type: Journal    
DOI: 10.1111/j.1467-9868.2007.00617.x     Document Type: Article

References (18)

1. Baker, S. G. Kramer, B. S. (2003) A perfect correlate does not a surrogate make. BMC Med. Res. Methodol., 3, 16.
2. Cox, D. R. Wermuth, N. (2003) A general condition for avoiding effect reversal after marginalization. J. R. Statist. Soc. B, 65, 937 941.
3. Dawid, A. P. (2002) Influence diagrams for causal modelling and inference. Int. Statist. Rev., 70, 161 189.
4. Durbin, J. (1954) Errors in variables. Rev. Int. Statist. Inst., 22, 23 32.
5. Durrett, R. (1996) Probability Theory and Examples, 2nd edn. New York : Duxbury.
6. Fleming, T. R. DeMets, D. L. (1996) Surrogate end points in clinical trials: are we being misled? Ann. Intern. Med., 125, 605 613.
7. Frangakis, C. E. Rubin, D. B. (2002) Principal stratification in causal inference. Biometrics, 58, 21 29.
8. Imbens, G. W. Angrist, J. (1994) Identification and estimation of local average treatment effects. Econometrica, 62, 467 475.
9. Lauritzen, S. L. (1996) Graphical Models. Oxford : Clarendon.
10. Lauritzen, S. L. (2001) Causal inference from graphical models. In Complex Stochastic Systems (eds O. E. Barndorff-Nielsen, D. R. Cox C. Kluppelberg pp. 63 107. London : Chapman and Hall-CRC.
11. Lauritzen, S. L. (2004) Discussion on causality. Scand. J. Statist., 31, 189 192.
12. Moore, T. (1995) Deadly Medicine: Why Tens of Thousands of Patients Died in America's Worst Drug Disaster. New York : Simon and Schuster.
13. Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge : Cambridge University Press.
14. Prentice, R. L. (1989) Surrogate endpoints in clinical trials: definition and operational criteria. Statist. Med., 8, 431 440.
15. Rubin, D. B. (1974) Estimating causal effects of a treatments in randomized and nonrandomized studies. J. Educ. Psychol., 66, 688 701.
16. Simpson, E. H. (1951) The interpretation of interaction in contingency tables. J. R. Statist. Soc. B, 13, 238 241.
17. Spirtes, P., Glymour, C. Scheines, R. (1993) Causation, Prediction and Search. New York : Springer.
18. Yule, G. U. (1903) Notes on the theory of association of attributes in statistics. Biometrika, 2, 121 134.