Package: survivALL
Title: Continuous Biomarker Assessment by Exhaustive Survival Analysis
Version: 0.9.3
Authors@R: person("Dominic", "Pearce", email = "dominic.pearce@ed.ac.uk", 
    role = c("aut", "cre"))
Description: 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 
    <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 “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.
Depends: R (>= 3.3.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
biocViews:
Imports: survival, survcomp, desiR, ggplot2, cowplot, ggthemes, viridis
Suggests: testthat, survsim, broom, readr, pander, magrittr, Biobase,
        GGally, knitr, rmarkdown
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-25 09:47:26 UTC; dom
Author: Dominic Pearce [aut, cre]
Maintainer: Dominic Pearce <dominic.pearce@ed.ac.uk>
Repository: CRAN
Date/Publication: 2018-04-25 10:02:14 UTC
