calculate.meta.survival: Fit a meta-analytic Cox proportional hazards model to a...

In SIMMS: Subnetwork Integration for Multi-Modal Signatures

Fit a meta-analytic Cox proportional hazards model to a single feature

Description

Takes a meta-analysis data object and fits a Cox proportional hazards model (possibly with adjustment for some specific covariates) by median-dichotomizing patients within each individual dataset.

Usage

calculate.meta.survival(
  feature.name,
  expression.data,
  survival.data,
  rounding = 3,
  other.data = NULL,
  data.type.ordinal = FALSE,
  centre.data = "median"
)

Arguments

feature.name	Character indicate what feature (gene/probe/etc.) should be extracted for analysis
expression.data	A list where each component is an expression matrix (patients = columns, genes = rows) for a different dataset
survival.data	A list where each component is an object of class Surv
rounding	How many digits after the decimal place to include
other.data	A list of other covariates to be passed to the Cox model (all elements in this list are used
data.type.ordinal	Logical indicating whether to treat this datatype as ordinal. Defaults to FALSE
centre.data	A character string specifying the centre value to be used for scaling data. Valid values are: 'median', 'mean', or a user defined numeric threshold e.g. '0.3' when modelling methylation beta values. This value is used for both scaling as well as for dichotomising data for estimating univariate betas from Cox model. Defaults to 'median'

Value

Returns a vector containing the HR, p-value, n, and 95% confidence limits of the HR (see fit.coxmodel() for details)

Author(s)

Paul C. Boutros

Examples

data.directory <- get.program.defaults()[["test.data.dir"]];
data.types <- c("mRNA");
x1 <- load.cancer.datasets(
  datasets.to.load = c('Breastdata1'),
  data.types = data.types,
  data.directory = data.directory
  );
x2 <- calculate.meta.survival(
  feature.name = "1000_at",
  expression.data = x1$all.data[[data.types[1]]],
  survival.data = x1$all.survobj
  );

SIMMS documentation built on April 24, 2022, 5:06 p.m.