TY - JOUR
T1 - Statistical consideration for clinical biomarker research in bladder cancer
AU - Shariat, Shahrokh F.
AU - Lotan, Yair
AU - Vickers, Andrew
AU - Karakiewicz, Pierre I.
AU - Schmitz-Dräger, Bernd J.
AU - Goebell, Peter J.
AU - Malats, Nuria
PY - 2010
Y1 - 2010
N2 - Objective: To critically review and illustrate current methodological and statistical considerations for bladder cancer biomarker discovery and evaluation. Methods: Original, review, and methodological articles, and editorials were reviewed and summarized. Results: Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are many and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation-a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, need to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid the use of cutpoints. Biomarkers providing a continuous score provide potentially more useful information than cutpoints since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biological potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers. Conclusions: Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.
AB - Objective: To critically review and illustrate current methodological and statistical considerations for bladder cancer biomarker discovery and evaluation. Methods: Original, review, and methodological articles, and editorials were reviewed and summarized. Results: Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are many and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation-a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, need to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid the use of cutpoints. Biomarkers providing a continuous score provide potentially more useful information than cutpoints since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biological potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers. Conclusions: Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.
KW - Biomarker
KW - Bladder cancer
KW - Decision-analysis
KW - Diagnosis
KW - Nomogram
KW - Prognosis
KW - Statistical analysis
KW - Treatment
KW - statistics
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UR - http://www.scopus.com/inward/citedby.url?scp=77953817519&partnerID=8YFLogxK
U2 - 10.1016/j.urolonc.2010.02.011
DO - 10.1016/j.urolonc.2010.02.011
M3 - Review article
C2 - 20610277
AN - SCOPUS:77953817519
SN - 1078-1439
VL - 28
SP - 389
EP - 400
JO - Urologic Oncology: Seminars and Original Investigations
JF - Urologic Oncology: Seminars and Original Investigations
IS - 4
ER -