allHR

A continuous variable used to order survival data. Samples
must be ordered exactly as in srv

measure

A dataframe that contains at least two columns, detailing event 
and time to event information. 
Samples must be ordered exactly as in measure

Column name in srv containing time to event information. Must 
not contain NAs

time

Column name in srv containing event information coded as 0 (no
event) and 1 (event). Must not contain NAs

event

Hazard ratios are returned as log2 values by default

log2HR

Large hazard ratios result from statistical 
disproportion when considering edge cases (e.g. 1 vs 99) and can be 
automaticall removed

remove_outliers

For all possible separation points for a cohort ordered by a continuous
measurement, calculate hazard ratio

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 <e2><80><9c>high<e2><80><9d> or <e2><80><9c>low<e2><80><9d>
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 <e2><80><9c>low<e2><80><9d> there is an equivalent <e2><80><9c>high<e2><80><9d>. In reality, data sets are
often heterogeneous in their composition (Perou, CM et al., 2000
<doi:10.1038/35021093>)- 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 population (Selli, C et al., 2016
<doi:10.1186/s13058-016-0779-0>). We would expect therefore, upon dividing
patients by ER expression, 25% to be classified <e2><80><9c>low<e2><80><9d> and 75% <e2><80><9c>high<e2><80><9d>, 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 <e2><80><9c>high<e2><80><9d> vs. <e2><80><9c>low<e2><80><9d> -
allowing a measure<e2><80><99>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.

allHR: For all possible separation points for a cohort ordered by a continuous measurement, calculate hazard ratio

Description

Usage

Arguments

Value

Examples