calPerformance.meta: Meta analysis of survival data

In survJamda: Survival Prediction by Joint Analysis of Microarray Gene Expression Data

Description

Analyze jointly the data set by the inverse normal method (Hedges and Olkin, 1985).

Usage

calPerformance.meta(common.gene, zstat, i, j, geno.files, surv.data, method)

Arguments

common.gene	A vector of character strings containing the names of the genes common to all data sets.
zstat	A list containing the combined Z-scores of the data sets composing the training set.
i	A vector of character strings containing the names of the data sets composing the training set.
j	A character string specifying the name the data set used as the testing set.
geno.files	A vector of character strings containing the names of gene expression files.
surv.data	A list of two vectors, survival time and censoring status.In the censoring status vector, 1 = event occurred, 0 = censored.
method	A character string specifying the feature selection method: "none" for top-100 ranking or one of the adjusting methods specified by the p.adjust function.

Details

In top-ranking, genes are selected based on univariate Cox P-value ranking using the coxph function in the R survival package. In this feature selection method, the genes are ranked based on their likelihood ratio P-value and the top-100 ranked genes with the smallest P-values are retained as the gene signature.

The p.adjust function in the R stats package is used and all adjusted p-value not greater than 0.05 are retained if method != "none".

Value

AUC, HR(CI) and p-value.

Warning

This function is not called by the user directly.

Author(s)

Haleh Yasrebi

References

L. V. Hedges and I. Olkin. Statistical Methods for Meta-Analysis. Academic Press,January 1985. ISBN 0123363802.

survJamda documentation built on May 1, 2019, 8:50 p.m.