Systematic review of statistical methods used in molecular marker studies in cancer.
BACKGROUND
There is wide interest in the use of molecular markers for the early detection of cancer, the prediction of disease outcome, and the selection of patients for chemotherapy. Despite significant and increasing research activity, to the authors' knowledge only a small number of molecular markers have been successfully integrated into clinical practice. In the current study, the experimental designs and statistical methods used in contemporary molecular marker studies are reviewed, particularly with respect to whether these evaluated a marker's clinical value.


METHODS
MEDLINE was searched for studies that analyzed an association between a cancer outcome and a marker involving chemical analysis of body fluid or tissue. For each article, data were extracted regarding patients, markers, type of statistical analysis, and principal results.


RESULTS
The 129 articles eligible for analysis included a very large variety of molecular markers; the total number of markers was larger than the number of articles. Only a minority of articles (47 articles; 36%) incorporated multivariate modeling in which the marker was added to standard clinical variables, and only a very small minority had any measure of predictive accuracy (14 articles; 11%). No article used decision analytic methods or experimentally evaluated the clinical value of a marker. Correction for overfit was also rare (3 articles).


CONCLUSIONS
Statistical methods in molecular marker research have not focused on the clinical value of a marker. Attention to sound statistical practice, in particular the use of statistical approaches that provide clinically relevant information, will help maximize the promise of molecular markers for care of the cancer patient.
Vickers
Andrew J
AJ
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York City, New York 10021, USA. vickersa@mskcc.org
Jang
Kwang
K
Sargent
Daniel
D
Lilja
Hans
H
Kattan
Michael W
MW
D014408:Biomarkers, Tumor / Q000145:classification*; D005544:Forecasting; D006801:Humans; D011237:Predictive Value of Tests; D011379:Prognosis; D012107:Research Design / Q000706:statistics & numerical data*
D014408:Biomarkers, Tumor