By Jean-Louis Auget; et al
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Extra info for Advances in statistical methods for the health sciences : applications to cancer and AIDS studies, genome sequence analysis, and survival analysis
2002). Pooled analysis of prognostic impact of urokinasetype plasminogen activator and its inhibitor PAI-1 in 8377 breast cancer patients, Journal of the National Cancer Institute, 94, 116–128. 13. McKibbon, K. , and Wilczynski, N. (1995). Beyond ACP Journal Club: How to harness MEDLINE for prognosis problems, ACP Journal Club, 123. A12–A14. 14. McShane, L. , Altman, D. , Taube, S. , Clark, G. M. for the Statistics Subcommittee of the NCI/EORTC Working Group on Cancer Diagnostics (2005). Reporting recommendations for tumor marker prognostic studies (REMARK), Journal of the National Cancer Institute, 97, 1180–1184.
29. Trivella, M. , Altman, D. , and Harris, A. L. (2006). Angiogenesis as a prognostic factor in non-small cell lung carcinoma. A meta-analysis using individual patient data, Submitted for publication. 30. Williamson, P. , Smith, C. , Hutton, J. , and Marson, A. G. (2002). Aggregate data meta-analysis with time-to-event outcomes, Statistics in Medicine, 21, 3337–3351. 2 On Statistical Approaches for the Multivariable Analysis of Prognostic Marker Studies N. Holl¨ ander and W. Sauerbrei Institute of Medical Biometry and Medical Informatics, Freiburg, Germany Abstract: Various statistical methods to analyse prognostic marker studies are available and are used in practice.
Finally, random-eﬀects meta-analysis was employed to synthesize the available data. Although just a single case study, many of the lessons learnt from PILC will apply broadly to IPD studies of prognosis in cancer and indeed in other medical areas. Our experiences accord with what has been written by others about IPD studies, and may be summarised as follows 1. Obtaining and combining raw data from prognostic studies is a long, expensive, and rather laborious process, although it oﬀers clear compensation in avoiding methodological pitfalls.
Advances in statistical methods for the health sciences : applications to cancer and AIDS studies, genome sequence analysis, and survival analysis by Jean-Louis Auget; et al