Literature DB >> 6362863

Importance of prognostic factors in cancer clinical trials.

R Simon.   

Abstract

The importance of prognostic factors in the design, analysis, and reporting of clinical trials is discussed. For many kinds of cancer, the variability in prognosis among patients is greater than the size of treatment differences usually seen. Consequently, failure to understand and adequately account for patient heterogeneity easily leads to unreliable claims and inefficient trials. Identified prognostic factors are generally of sufficient importance to demand attention in design and analysis, but rarely are sufficiently explanatory to render randomization unnecessary. Improvement in knowledge of prognostic factors is important for sharpening the focus and for improving the reliability, efficiency, and interpretability of clinical trials. Problems in the conduct of prognostic factor studies are also discussed, and the calculation of the proportion of variability explained by logistic regression models is illustrated for two examples.

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Year:  1984        PMID: 6362863

Source DB:  PubMed          Journal:  Cancer Treat Rep        ISSN: 0361-5960


  10 in total

1.  Shortcomings in the clinical evaluation of new drugs: acute myeloid leukemia as paradigm.

Authors:  Roland B Walter; Frederick R Appelbaum; Martin S Tallman; Noel S Weiss; Richard A Larson; Elihu H Estey
Journal:  Blood       Date:  2010-06-10       Impact factor: 22.113

2.  Does palliative chemotherapy palliate?

Authors:  Maurie Markman
Journal:  Curr Oncol Rep       Date:  2002-09       Impact factor: 5.075

3.  Flexible covariate effects in the proportional hazards model.

Authors:  T Hastie; L Sleeper; R Tibshirani
Journal:  Breast Cancer Res Treat       Date:  1992       Impact factor: 4.872

Review 4.  Evaluating prognostic factors: implications for measurement of health care outcome.

Authors:  M C Gulliford
Journal:  J Epidemiol Community Health       Date:  1992-08       Impact factor: 3.710

5.  Prognostic factors in non-small cell lung cancer: multiregression analysis in the National Cancer Center Hospital (Japan).

Authors:  M Sakurai; T Shinkai; K Eguchi; Y Sasaki; T Tamura; K Miura; Y Fujiwara; A Otsu; N Horiuchi; H Nakano
Journal:  J Cancer Res Clin Oncol       Date:  1987       Impact factor: 4.553

6.  Measures of explained variation for a regression model used in survival analysis.

Authors:  K Akazawa
Journal:  J Med Syst       Date:  1997-08       Impact factor: 4.460

7.  The importance of distinguishing "clinical judgement" in cancer management from "selection bias" in clinical trials.

Authors:  M Markman
Journal:  J Cancer Res Clin Oncol       Date:  1996       Impact factor: 4.553

8.  Pretreatment clinical prognostic factors in patients with stage IV non-small cell lung cancer (NSCLC) treated with chemotherapy.

Authors:  Branislav Jeremic; Biljana Milicic; Aleksandar Dagovic; Jasna Aleksandrovic; Nebojsa Nikolic
Journal:  J Cancer Res Clin Oncol       Date:  2003-03-07       Impact factor: 4.553

9.  Multivariate analysis of clinical prognostic factors in patients with glioblastoma multiforme treated with a combined modality approach.

Authors:  Branislav Jeremic; Biljana Milicic; Danica Grujicic; Aleksandar Dagovic; Jasna Aleksandrovic
Journal:  J Cancer Res Clin Oncol       Date:  2003-07-15       Impact factor: 4.553

10.  How many strata in an RCT? A flexible approach.

Authors:  P Silcocks
Journal:  Br J Cancer       Date:  2012-03-13       Impact factor: 7.640

  10 in total

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