Confidence intervals for the effect of a prognostic factor after selection of an 'optimal' cutpoint

Statistics in Medicine

Volumn 23, Issue 11, 2004, Pages 1701-1713

  Holländer, Norbert ^a,b   Sauerbrei, Willi ^a   Schumacher, Martin ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Bootstrap resampling; Confidence intervals; Maximally selected test statistics; Prognostic or risk factors; Shrinkage methods

Indexed keywords

ANALYTICAL ERROR; ARTICLE; CONFIDENCE INTERVAL; PROGNOSIS; QUANTITATIVE ANALYSIS; RISK ASSESSMENT; SAMPLING; STATISTICAL MODEL; VARIANCE;

BREAST NEOPLASMS; COMPUTER SIMULATION; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; DISEASE-FREE SURVIVAL; DNA, NEOPLASM; FEMALE; FLOW CYTOMETRY; HUMANS; PREDICTIVE VALUE OF TESTS; PROGNOSIS; S PHASE;

EID: 2942689368     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1611     Document Type: Article

References (33)

1. Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using 'optimal' cutpoints in the evaluation of prognostic factors. Journal of the National Cancer Institute 1994; 86:829-835.
2. Miller R, Siegmund D. Maximally selected chi-square statistics. Biometrics 1982; 38:1011-1016.
3. Koziol JA. On maximally selected chi-square statistics. Biometrics 1991; 47:1557-1561.
4. Lausen B, Schumacher M. Maximally selected rank statistics. Biometrics 1992; 48:73-85.
5. Betensky P, Rabinowitz D. Maximally selected chi-square statistics for k × 2 tables. Biometrics 1999; 55: 317-320.
6. Mazumdar M, Glassman JR. Tutorial in biostatistics categorizing a prognostic variable: review of methods, code for easy implementation and applications to decision-making about cancer treatments. Statistics in Medicine 2000; 19:113-132.
7. Rabinowitz D, Betensky RA. Approximating the distribution of maximally selected McNemar's statistics. Biometrics 2000; 56:897-902.
8. Linderholm B, Grankvist K, Wiling N, Johansson M, Tavelin B, Henriksson R. Correlation of vascular endothelial growth factor content with recurrence, survival, and first relapse site in primary node-positive breast carcinoma after adjuvant treatment. Journal of Clinical Oncology 2000; 18:1423-1431.
9. Schulgen G, Lausen B, Olsen J, Schumacher M. Outcome-oriented cutpoints in quantitative exposure. American Journal of Epidemiology 1994; 120:172-184.
10. Schumacher M, Hollinder N, Sauerbrei W. Resampling and cross-validation techniques: A tool to reduce bias caused by model building? Statistics in Medicine 1997; 16:2813-2827.
11. Pfisterer J, Kommoss F, Sauerbrei W, Menzel D, Kiechle M, Giese E, Hilgarth M, Pfleiderer A. DNA flow cytometry in node positive breast cancer: prognostic value and correlation to morphological and clinical factors. Analytical and Quantitative Cytology and Histology 1995; 17:406-412.
12. Lausen B, Schumacher M. Evaluating the effect of optimized cutoff values in the assessment of prognostic factors. Computational Statistics & Data Analysis 1996; 21:307-326.
13. Cox DR. Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 1972; 74:187-200.
14. Marubini E, Valsecchi MG. Analysing Survival Data from Clinical Trials and Observational Studies. Wiley: New York, 1995.
15. Hilsenbeck SG, Clark GM. Practical P-value adjustment for optimally selected cutpoints. Statistics in Medicine 1996; 15:103-112.
16. Verweij P, Houwelingen van HC. Cross-validation in survival analysis. Statistics in Medicine 1993; 12: 2305-2314.
17. Houwelingen van HC, Le Cessie S. Predictive value of statistical models. Statistics in Medicine 1990; 9: 1303-1325.
18. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. Wiley: New York, 1980.
19. Pons O. Estimation in a Cox regression model with a change-point according to a threshold in a covariate. The Annals of Statistics 2003; 31:442-463.
20. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman & Hall: New York, 1993.
21. Harrell FE Jr. Regression Modelling Strategies. Springer: New York, 2001.
22. Ferrandina G, Scambia G, Bardelli F, Benedetti Panici P, Mancuso S, Messori A. Relationship between cathepsin-D content and disease-free survival in node-negative breast cancer patients: a meta-analysis. British Journal of Cancer 1997; 76:661-666.
23. Altman DG. Systematic reviews of studies evaluation of prognostic variables. In Systematic Reviews in Health Care: Meta-Analysis in Context. Egger M, Smith GD, Altman DG (eds). BMJ Publishing Group: London: 2001; 228-247.
24. Bast RC, Ravdin P, Hayes DF, Bates S, Fritsche H, Jessup JM, Kemeny N, Locker GY, Mennel RG, Somerfield MR, for the American Society on Clinical Oncology Tumour Markers Expert Panel. 2000 update of recommendations for the use of tumour markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. Journal of Clinical Oncology 2001; 19:1865-1878.
25. Altman DG. Suboptimal analysis using 'optimal' cutpoints. British Journal of Cancer 1998; 78:556-557.
26. Breiman L. Bagging predictors. Machine Learning 1996; 26:123-140.
27. Schumacher M, Holländer N, Sauerbrei W. Reduction of bias caused by model building. In Proceedings of the Statistical Computing Section. American Statistical Association, Alexandria, 1996; 1-7.
28. Mazumdar M, Smith A, Bacik J. Methods for categorizing a prognostic variable in a multivariable setting. Statistics in Medicine 2003; 22:559-571.
29. Breiman L, Friedman JH, Olsen RA, Stone CJ. Classification and Regression Trees. Wadsworth: Monterey, 1984.
30. Miller AJ. Subset Selection in Regression. Chapman & Hall: London, 1990.
31. Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Applied Statistics 1994; 43:429-467.
32. Hjorth JSU. Computer Intensive Statistical Methods. Validation, Model Selection and Bootstrap. Chapman & Hall: London, 1994.
33. Sauerbrei W. The use of resampling methods to simplify regression models in medical statistics. Applied Statistics 1999; 48:313-329.