# Continuous Biomarker Assessment by Exhaustive Survival Analysis

In routine practice, biomarker performance is calculated by
splitting a patient cohort at some arbitrary level, often by median gene
expression. The logic behind this is to divide patients into “high” or “low”
expression groups that in turn correlate with either good or poor prognosis.
However, this median-split approach assumes that the data set composition
adheres to a strict 1:1 proportion of high vs. low expression, that for
every one “low” there is an equivalent “high”. In reality, data sets are
often heterogeneous in their composition (Perou, CM et al., 2000
)- i.e. this 1:1 relationship is unlikely to exist and
the true relationship unknown. Given this limitation, it remains difficult
to determine where the most significant separation should be made. For
example, estrogen receptor (ER) status determined by immunohistochemistry is
standard practice in predicting hormone therapy response, where ER is found
in an ~1:3 ratio (-:+) in the populationi (Selli, C et al., 2016
). We would expect therefore, upon dividing
patients by ER expression, 25% to be classified “low” and 75% “high”, and
an otherwise 50-50 split to incorrectly classify 25% of our patient cohort,
rendering our survival estimate under powered. 'survivALL' is a data-driven
approach to calculate the relative survival estimates for all possible
points of separation - i.e. at all possible ratios of “high” vs. “low” -
allowing a measure’s relationship with survival to be more reliably
determined and quantified. We see this as a solution to a flaw in common
research practice, namely the failure of a true biomarker as part of a
meta-analysis.