Gene selection in microarray survival studies under possibly non-proportional hazards.
BACKGROUND
Univariate Cox regression (COX) is often used to select genes possibly linked to survival. With non-proportional hazards (NPH), COX could lead to under- or over-estimation of effects. The effect size measure c=P(T(1)<T(0)), i.e. the probability that a person randomly chosen from group G(1) dies earlier than a person from G(0), is independent of the proportional hazards (PH) assumption. Here we consider its generalization to continuous data c' and investigate the suitability of c' for gene selection.


RESULTS
Under PH, c' is most efficiently estimated by COX. Under NPH, c' can be obtained by weighted Cox regression (WHE) or a novel method, concordance regression (CON). The least biased and most stable estimates were obtained by CON. We propose to use c' as summary measure of effect size to rank genes irrespective of different types of NPH and censoring patterns.


BACKGROUND
WHE and CON are available as R packages.


BACKGROUND
georg.heinze@meduniwien.ac.at


BACKGROUND
Supplementary Data are available at Bioinformatics online.
Dunkler
Daniela
D
Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.
Schemper
Michael
M
Heinze
Georg
G
D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D014157:Transcription Factors / Q000235:genetics
D014157:Transcription Factors