Measuring the effect of inter-study variability on estimating prediction error.

ABSTRACT:

Background

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in "batch-effects") and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies.

Methods

Here we quantify the impact of these combined "study-effects" on a disease s

SUBMITTER: Ma S 

PROVIDER: S-EPMC4201588 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature