Does prediction potential of candidate biomarker set identified and validated on gene expression data from multiple datasets?
Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.
It appears that neither spontaneous differences in incorporation of effects on income, nor explicit instructions will yield significantly different health-state valuations. This may suggest that QALY measures are insensitive to concerns regarding effects on income even when these are (explicitly) incorporated, and these effects may therefore be best placed on the cost side of the cost-effectiveness ratio.