Proteomics research to discover markers: what can we learn from Netflix?
BACKGROUND
Research in the field of proteomics to discover markers for detection of cancer has produced disappointing results, with few markers gaining US Food and Drug Administration approval, and few claims borne out when subsequently tested in rigorous studies. What is the role of better mathematical or statistical analysis in improving the situation?


BACKGROUND
This article examines whether a recent successful Netflix-sponsored competition using mathematical analysis to develop a prediction model for movie ratings of individual subscribers can serve to improve studies of markers in the field of proteomics. Netflix developed a database of movie preferences of individual subscribers using a longitudinal cohort research design. Groups of researchers then competed to develop better ways to analyze the data. Against this background, the strengths and weaknesses of research design are reviewed, contrasting the Netflix design with that of studies of biomarkers to detect cancer. Such biomarker studies generally have less-strong design, lower numbers of outcomes, and greater difficulty in even just measuring predictors and outcomes, so the fundamental data that will be used in mathematical analysis tend to be much weaker than in other kinds of research.


CONCLUSIONS
If the fundamental data that will be analyzed are not strong, then better analytic methods have limited use in improving the situation. Recognition of this situation is an important first step toward improving the quality of clinical research about markers to detect cancer.
Ransohoff
David F
DF
University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7080, USA. ransohof@med.unc.edu
D014408:Biomarkers, Tumor / Q000097:blood; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods*
D014408:Biomarkers, Tumor