Estimation in optimal treatment regimes based on mean residual lifetimes with right-censored data.

An optimal individualized treatment regime (ITR) is a decision rule in allocating the best treatment to each patient and, hence, maximizing overall benefits. In this paper, we propose a novel framework based on nonparametric inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) estimators of the value function when the data are subject to right censoring. In contrast to most existing approaches that are designed to maximize the expected survival time under a binary treatment framework, the proposed method targets maximizing the mean residual lifetime of patients. Specifically, the proposed IPW method searches the optimal ITR by maximizing an estimator for the overall population outcome directly, without specifying the regression model for the conditional mean residual lifetime, whereas the AIPW method integrates the model information of the mean residual lifetime to improve the robustness. Furthermore, to overcome the computational difficulty in a nonsmooth value estimator, smoothed IPW and AIPW estimators are constructed. In theory, we establish the asymptotic properties of the proposed method under suitable regularity conditions. The empirical performances of the proposed IPW and AIPW estimators are evaluated using simulation studies and are further illustrated with an application to the real-world data set from the Acquired Immunodeficiency Syndrome Clinical Trial Group Protocol 175 (ACTG175).

Default risk prediction and feature extraction using a penalized deep neural network.

Online peer-to-peer lending platforms provide loans directly from lenders to borrowers without passing through traditional financial institutions. For lenders on these platforms to avoid loss, it is crucial that they accurately assess default risk so that they can make appropriate decisions. In this study, we develop a penalized deep learning model to predict default risk based on survival data. As opposed to simply predicting whether default will occur, we focus on predicting the probability of default over time. Moreover, by adding an additional one-to-one layer in the neural network, we achieve feature selection and estimation simultaneously by incorporating an L 1 -penalty into the objective function. The minibatch gradient descent algorithm makes it possible to handle massive data. An analysis of a real-world loan data and simulations demonstrate the model's competitive practical performance, which suggests favorable potential applications in peer-to-peer lending platforms.

Information-incorporated Gaussian graphical model for gene expression data.

In the analysis of gene expression data, network approaches take a system perspective and have played an irreplaceably important role. Gaussian graphical models (GGMs) have been popular in the network analysis of gene expression data. They investigate the conditional dependence between genes and "transform" the problem of estimating network structures into a sparse estimation of precision matrices. When there is a moderate to large number of genes, the number of parameters to be estimated may overwhelm the limited sample size, leading to unreliable estimation and selection. In this article, we propose incorporating information from previous studies (for example, those deposited at PubMed) to assist estimating the network structure in the present data. It is recognized that such information can be partial, biased, or even wrong. A penalization-based estimation approach is developed, shown to have consistency properties, and realized using an effective computational algorithm. Simulation demonstrates its competitive performance under various information accuracy scenarios. The analysis of TCGA lung cancer prognostic genes leads to network structures different from the alternatives.

Robust group variable screening based on maximum Lq-likelihood estimation.

Variable screening plays an important role in ultra-high-dimensional data analysis. Most of the previous analyses have focused on individual predictor screening using marginal correlation or other rank-based techniques. When predictors can be naturally grouped, the structure information should be incorporated while applying variable screening. This study presents a group screening procedure that is based on maximum Lq-likelihood estimation, which is being increasingly used for robust estimation. The proposed method is robust against data contamination, including a heavy-tailed distribution of the response and a mixture of observations from different distributions. The sure screening property is rigorously established. Simulations demonstrate the competitive performance of the proposed method, especially in terms of its robustness against data contamination. Two real data analyses are presented to further illustrate its performance.

Marginal false discovery rate for a penalized transformation survival model.

Survival analysis that involves moderate/high dimensional covariates has become common. Most of the existing analyses have been focused on estimation and variable selection, using penalization and other regularization techniques. To draw more definitive conclusions, a handful of studies have also conducted inference. The recently developed mFDR (marginal false discovery rate) technique provides an alternative inference perspective and can be advantageous in multiple aspects. The existing inference studies for regularized estimation of survival data with moderate/high dimensional covariates assume the Cox and other specific models, which may not be sufficiently flexible. To tackle this problem, the analysis scope is expanded to the transformation model, which is robust and has been shown to be desirable for practical data analysis. Statistical validity is rigorously established. Two data analyses are conducted. Overall, an alternative inference approach has been developed for survival analysis with moderate/high dimensional data.

Penalized integrative semiparametric interaction analysis for multiple genetic datasets.

In this article, we consider a semiparametric additive partially linear interaction model for the integrative analysis of multiple genetic datasets. The goals are to identify important genetic predictors and gene-gene interactions and to estimate the nonparametric functions that describe the environmental effects at the same time. To find the similarities and differences of the genetic effects across different datasets, we impose a group structure on the regression coefficients matrix under the homogeneity assumption, ie, models for different datasets share the same sparsity structure, but the coefficients may differ across datasets. We develop an iterative approach to estimate the parameters of main effects, interactions and nonparametric functions, where a reparametrization of interaction parameters is implemented to meet the strong hierarchy assumption. We demonstrate the advantages of the proposed method in identification, estimation, and prediction in a series of numerical studies. We also apply the proposed method to the Skin Cutaneous Melanoma data and the lung cancer data from the Cancer Genome Atlas.

Association of echocardiographic parameters with mortality in hospitalized patients with lupus nephritis.

AIM: Increasing evidence shows that the cardiac involvement attributes to the mortality of patients with lupus nephritis (LN) and echocardiography provides a valid measurement for cardiac disease. However, the association between echocardiographic parameters and mortality in LN patients without cardiac disease history remains unclear. The aim of this study was to explore the relationship between echocardiographic parameters and the mortality in hospitalized LN patients without cardiac disease history. METHODS: A total of 436 LN patients without cardiac disease history who underwent echocardiography at Sun Yat-sen Memorial Hospital, between 1 January 2000 and 31 December 2014, were enrolled into this study. The association between echocardiographic parameters and all-cause and cardiac mortality of LN patients was examined by the Cox proportional hazards model. RESULTS: In this cohort study, the median duration of follow-up was 18 months. Among 436 hospitalized LN patients, 88 patients (20.2%) died. Of them, 38 patients (43.2%) died of cardiac disease. Cardiac symptoms, high systolic blood pressure, high serum levels of C-reactive protein, low serum albumin, low estimated glomerular filtration rate (eGFR), and decreased left ventricular ejection fraction (LVEF) were found to be independently associated with increased all-cause mortality. Furthermore, the cardiac symptoms, low eGFR, increased left ventricular mass index (LVMI), and decreased LVEF were independently correlated with an increased cardiac mortality risk. CONCLUSIONS: Decreased LVEF was associated with increased all-cause and cardiac mortality and increased LVMI was an independent risk factor for cardiac mortality in hospitalized LN patients without cardiac disease history.

Conditional quantile residual lifetime models for right censored data.

Quantile residual lifetime function is a more comprehensive quantitative measure for residual lifetimes than the mean residual lifetime function. It also incorporates the median residual life function, which is less restrictive than the model based on the mean residual lifetime. In this study, we propose a semiparametric estimator of the conditional quantile residual lifetime under different covariate effects at a specified time point by the reinforcement of the auxiliary models. Two kind of test statistics are proposed to compare two quantile residual lifetimes at fixed time points. Asymptotic properties are also established and a revised bootstrap method is proposed to estimate the asymptotic variance of the estimator. Simulation studies are reported to assess the finite sample properties of the proposed estimator and the performance of test statistics in terms of type I error probabilities and powers at fixed time points. We also compare the proposed method with the method of Jung et al. (Biometrics 65:1203-1212, 2009) through simulation studies. The proposed methods are applied to HIV data and some interesting results are presented.