Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data

Abstract

Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features [Formula: see text] is extremely larger than sample size [Formula: see text]. While gene expression patterns have been shown to be related to patients’ survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice.

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

“…In our data example of gastric cancer patients (Section 5.1), bivariate correlated endpoints are of great interest [55]. Correlations between responses should be employed to improve the efficiency and reduce the bias of univariate analyses [56][57][58][59][60][61][62][63][64][65] and to predict a primary outcome by the secondary outcome [66][67][68][69]. Multivariate shrinkage estimators of multivariate normal means, such as [70,71], can be considered for this extension.…”

meta.shrinkage: An R Package for Meta-Analyses for Simultaneously Estimating Individual Means

“…A similar instance involves math and stat tests [44,46]. There is much room for meta-analyzing bivariate and multivariate responses [47][48][49][50][51][52][53][54][55][56]. Multivariate shrinkage estimators of multivariate restricted and unrestricted normal means, such as those in [57][58][59][60], can be considered for this extension.…”

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

“…Usually, a multi-gene predictor is more accurate than a single-gene predictor as the prognostic ability of a single gene is limited. A predictor optimally constructed from the selected genes becomes a useful biomarker for predicting survival in breast cancer [ 10 , 11 , 12 , 13 , 14 , 15 ], lung cancer [ 6 , 7 , 8 , 9 , 16 , 17 ], gastric cancer [ 18 , 19 ], ovarian cancer [ 20 , 21 , 22 , 23 , 24 ], skin cancer [ 25 ], liver cancer [ 26 , 27 ], bladder cancer [ 28 ], head and neck cancer [ 29 , 30 ], glioma [ 31 ], myeloproliferative neoplasms [ 32 ], kidney cancer [ 33 ], and cancers of mixed types [ 34 , 35 ]. These analyses were performed mostly based on univariate Cox regression with the significance scaling of p -values, such as 0.05, 0.01, and 0.001, followed by cross-validation and/or external validation.…”