|
[1] A. Alonso, G. Molenberghs, T. Burzykowski, D. Renard, H. Geys, Z. Shkedy, F. Tibaldi, J. C. Abrahantes and M. Buyse, Prentice’s approach and the meta-analytic paradigm: a reflection on the role of statistics in the evaluation of surrogate endpoints, Biometrics 60(3) (2004), 724-728.
[2] M. Buyse, G. Molenberghs, T. Burzykowski, D. Renard and H. Geys, The validation of surrogate endpoints in meta-analyses of randomized experiments, Biostatistics 1(1) (2000), 49-67.
[3] M. Buyse and G. Molenberghs, Criteria for the validation of surrogate endpoints in randomized experiments, Biometrics 54(3) (1998), 1014-1029.
[4] A. Chakravarty, Regulatory aspects in using surrogate markers in clinical trials, The Evaluation of Surrogate Endpoints, T. Burzykowski, G. Molenberghs and M. Buyse, eds., Springer, 2005, pp. 13-51.
[5] L. S. Freedman, B. I. Graubard and A. Schatzkin, Statistical validation of intermediate endpoints for chronic diseases, Stat. Med. 11(2) (1992), 167-178.
[6] SAS Institute Inc., SAS/IML User’s Guide, Version 8, SAS Institute Inc., Cary, NC, 1999.
[7] C. J. Weir and R. J. Walley, Statistical evaluation of biomarkers as surrogate endpoints: a literature review, Stat. Med. 25(2) (2006), 183-203. |