calculate.meta.survival {SIMMS}

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
  );