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Review

. 2003 Sep 1;89(5):781-6.

doi: 10.1038/sj.bjc.6601117.

Survival analysis part IV: further concepts and methods in survival analysis

T G Clark¹, M J Bradburn, S B Love, D G Altman

Affiliations

PMID: 12942105
PMCID: PMC2394469
DOI: 10.1038/sj.bjc.6601117

Review

Survival analysis part IV: further concepts and methods in survival analysis

T G Clark et al. Br J Cancer. 2003.

. 2003 Sep 1;89(5):781-6.

doi: 10.1038/sj.bjc.6601117.

Authors

T G Clark¹, M J Bradburn, S B Love, D G Altman

Affiliation

¹ Cancer Research UK/NHS Centre for Statistics in Medicine, Institute of Health Sciences, Old Road, Oxford OX3 7LF, UK. taane.clark@cancer.org.uk

PMID: 12942105
PMCID: PMC2394469
DOI: 10.1038/sj.bjc.6601117

No abstract available

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Figures

Figure 1
Modelling log CA125 using spline functions: ∣ corresponds to measurements.

Figure 2
An example of an ANN.

Figure 3
A CART for Dukes' B colonic cancer study.

See this image and copyright information in PMC

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References

1. Altman DG (1998) Suboptimal analysis using ‘optimal’ cutpoints. Br J Cancer 78: 556–557 - PMC - PubMed
1. Altman DG, De Stavola BL (1994) Practical problems in fitting a proportional hazards model to data with updated measurements of the covariates. Stat Med 13: 301–341 - PubMed
1. Altman DG, Lausen B, Sauerbrei W, Schumacher M (1994) Dangers of using ‘optimal’ cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst 86: 829–835 - PubMed
1. Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19: 453–473 - PubMed
1. Biganzoli E, Boracchi P, Mariani L, Marubini E (1998) Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med 17: 1169–1186 - PubMed

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