Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects.

The growing popularity of personalized medicine motivates people to explore individualized treatment regimes according to heterogeneous characteristics of the patients. For the large-scale data analysis, however, the data are collected at different times and different locations, i.e. subjects are usually from a heterogeneous population, which causes that the optimal treatment regimes also vary for patients across different subgroups. In this paper, we mainly focus on the estimation of optimal treatment regimes for subjects come from a heterogeneous population with high-dimensional data. We first remove the main effects of the covariates for each subgroup to eliminate non-ignorable residual confounding. Based on the centralized outcome, we propose a penalized robust learning that estimates the coefficient matrix of the interactions between covariates and treatment by penalizing pairwise differences of the coefficients of any two subgroups for the same covariate, which can automatically identify the latent complex structure of the coefficient matrix with heterogeneous and homogeneous columns. At the same time, the penalized robust learning can also select the important variables that truly contribute to the individualized treatment decisions with commonly used sparsity structure penalty. Extensive simulation studies show that our proposed method outperforms current popular methods, and it is further illustrated in the real analysis of the Tamoxifen breast cancer data.

Multiple phenotype association tests based on sliced inverse regression.

BACKGROUND: Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes. RESULTS: We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors. CONCLUSION: Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α .

A Bayesian approach for subgroup analysis.

Several penalization approaches have been developed to identify homogeneous subgroups based on a regression model with subject-specific intercepts in subgroup analysis. These methods often apply concave penalty functions to pairwise comparisons of the intercepts, such that the subjects with similar intercept values are assigned to the same group, which is very similar to the procedure of the penalization approaches for variable selection. Since the Bayesian methods are commonly used in variable selection, it is worth considering the corresponding approaches to subgroup analysis in the Bayesian framework. In this paper, a Bayesian hierarchical model with appropriate prior structures is developed for the pairwise differences of intercepts based on a regression model with subject-specific intercepts, which can automatically detect and identify homogeneous subgroups. A Gibbs sampling algorithm is also provided to select the hyperparameter and estimate the intercepts and coefficients of the covariates simultaneously, which is computationally efficient for pairwise comparisons compared to the time-consuming procedures for parameter estimation of the penalization methods (e.g., alternating direction method of multiplier) in the case of large sample sizes. The effectiveness and usefulness of the proposed Bayesian method are evaluated through simulation studies and analysis of a Cleveland Heart Disease Dataset.

The impact of nurse-led nonpharmacological multidisciplinary holistic nursing care on fatigue patients receiving hemodialysis: a randomized, parallel-group, controlled trial.

BACKGROUND: Fatigue is a symptom characterized by an elevated prevalence in patients undergoing hemodialysis, which may cause extreme mental and muscular debilitation, significantly influencing social interaction, life quality and well-being. However, the significance of fatigue to patients undergoing hemodialysis has not been recognized yet, and prevention and management of fatigue in this population have not been thoroughly investigated. Additionally, previous studies mainly focused on muscular fatigue, while mental fatigue has been seldom discussed. This study aims to investigate the interaction between nurses and multidisciplinary of nonpharmacological integrated care interventions (NICIs) and assess the impact of fatigue on patients undergoing hemodialysis. METHODS: The integrative nonpharmacological care interventions in this study included walking, motivational interviewing (MI) and health education regarding behavioral self-management. A single-center randomized controlled trial was conducted in the dialysis center of the nephrological department in a tertiary affiliated hospital of medical university from January to June 2019. A total of 118 patients were selected and randomly divided into the intervention group (IG) and the control group (CG). Four patients dropped out during the study, and 114 patients were enrolled for the eventual analysis. The 60 patients in the IG received routine nursing combined with integrated care interventions, while the 54 patients in the CG received routine nursing only. This study lasted for six months. RESULTS: The experimental group exhibited significant reductions of overall fatigue (2.26 vs. 0.48), mental fatigue (1.41 vs. 0.54), muscular fatigue (2.13 vs. 0.75), and some biochemical indicators (e.g., serum urea) (P<0.05), compared with the CG. CONCLUSIONS: Nurses and multidisciplinary teams have been demonstrated to play a key role and interplay function in chronic disease management. Hence, the nurse-led multidisciplinary NICIs significantly alleviated total fatigue (muscular fatigue and mental fatigue) and improved other parameters. TRIAL REGISTRATION: ChiCTR-IOR-16008621 (March 18, 2016).

Effect of SRB and Applied Potential on Stress Corrosion Behavior of X80 Steel in High-pH Soil Simulated Solution.

The effect of SRB and applied potential on the stress corrosion sensitivity of X80 pipeline steel was analyzed in high-pH soil simulated solution under different conditions using a slow strain rate tensile test, electrochemical test, and electronic microanalysis. The experimental results showed that X80 pipeline steel has a certain degree of SCC sensitivity in high-pH simulated solution, and the crack growth mode was trans-granular stress corrosion cracking. In a sterile environment, the SCC mechanism of X80 steel was a mixture mechanism of anode dissolution and hydrogen embrittlement at -850 mV potential, while X80 steel had the lowest SCC sensitivity due to the weak effect of AD and HE; after Sulfate Reducing Bacteria (SRB) were inoculated, the SCC mechanism of X80 steel was an AD-membrane rupture mechanism at -850 mV potential. The synergistic effect of Cl- and SRB formed an oxygen concentration cell and an acidification microenvironment in the pitting corrosion pit, and this promoted the formation of pitting corrosion which induced crack nucleation, thus significantly improving the SCC sensitivity of X80 steel. The strong cathodic polarization promoted the local corrosion caused by SRB metabolism in the presence of bacteria, whereby the SCC sensitivity in the presence of bacteria was higher than that in sterile conditions under strong cathodic potential.

Poisson noisy image restoration via overlapping group sparse and nonconvex second-order total variation priors.

The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.

Thermosensitive Liposomes Encapsulating Anti-Cancer Agent Lomustine, and Contrast Medium Iohexol, for Thermochemotherapy: Preparation, Characterization, and In Vivo Evaluation.

Thermosensitive liposome-based drug delivery systems (DDS) are powerful tools for site-specific delivery of chemotherapeutics, especially when combined with regional hyperthermia. The objective of this work was to develop a novel thermosensitive liposomal DDS loaded with lomustine, a chemotherapeutic compound, and iohexol, a contrast medium for visualization by CT. Thermosensitive compound liposomes (TSCLs) composed of DPPC were prepared by reverse-phase evaporation and investigated for encapsulation efficiency, temperature-sensitivity, release kinetics, and In Vivo pharmacokinetics. The size and zeta-potential of TSCLs ranged from 250 to 300 nm and -15 to -30 mV, respectively. At 41 °C, TSCLs were shown to release over 90% of iohexol and lomustine within 4 h. The in vitro release profiles of iohexol and lomustine at 41 °C conformed to first-order kinetics and Weibullmodel, respectively. Phase-transition did not occur after incorporation of cholesterol and soybean phospholipids. In Vivo evaluation performed with C6 glioma model rats proved the prolonged half-lives and improved bioavailability by liposomal encapsulation for both compounds under mild local hyperthermia. The TSCLs used in this study may offer a clinically promising mean of increasing efficacy and controlling toxicity.

A novel association test for rare variants based on algebraic statistics.

With the rapid growth of next-generation sequencing technology, more and more rare variants are available in the human genome. In recent years, the point of study has already changed direction to rare variants in genome-wide association studies (GWAS). Although a variety of approaches have been proposed to test associations between rare variants and phenotypes of interest, it is far from the end of this problem, and it is worth exploring new statistical methods based on special features of rare variants. As we all know, the most direct way is to evaluate the association in a two-way contingency table if the phenotype is a discrete variable. The numbers of observations are very close or equal to 0s for most of cells in the contingency table due to the extremely low mutation rates of rare variants. In this paper, we propose a novel association test for rare variants based on a generalization of Fisher's exact test, and the p-value of this exact test can be computed under the multivariate hypergeometric distribution in the framework of algebraic statistics. Simulation results show that our proposed method outperforms the existing methods, despite there is heterogeneity among causal variants. We also successfully apply our method into the genetic association study of coronary artery disease and hypertension from the Wellcome Trust Case Control Consortium.

A permutation method for detecting trend correlations in rare variant association studies.

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.

BACKGROUND: Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability analysis. In this paper, we propose a novel multiple testing procedure based on the Cartesian hidden Markov model (CHMM), called repLIS procedure, for replicability analysis across two studies, which can characterize the local dependence structure among adjacent SNPs via a four-state Markov chain. RESULTS: Theoretical results show that the repLIS procedure can control the false discovery rate (FDR) at the nominal level α and is shown to be optimal in the sense that it has the smallest false non-discovery rate (FNR) among all α-level multiple testing procedures. We carry out simulation studies to compare our repLIS procedure with the existing methods, including the Benjamini-Hochberg (BH) procedure and the empirical Bayes approach, called repfdr. Finally, we apply our repLIS procedure and repfdr procedure in the replicability analyses of psychiatric disorders data sets collected by Psychiatric Genomics Consortium (PGC) and Wellcome Trust Case Control Consortium (WTCCC). Both the simulation studies and real data analysis show that the repLIS procedure is valid and achieves a higher efficiency compared with its competitors. CONCLUSIONS: In replicability analysis, our repLIS procedure controls the FDR at the pre-specified level α and can achieve more efficiency by exploiting the dependency information among adjacent SNPs.

Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes.

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this paper, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the non-respondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes.

Joint parameter estimation in the QTL mapping of ordinal traits.

With the rapid development of statistical genetics, the deep researches of ordinal traits have been gradually emphasized. The data of these traits bear relatively less information than those of continuous phenotypes, therefore it is more complex to map the quantitative trait loci (QTL) of ordinal traits. In this paper, the multiple-interval mapping method is considered in the genetic mapping of ordinal traits. By combining threshold model and statistical model, we build a cumulative logistic regression model to express the relationship between the ordinal data and the QTL genotypes. In order to make the interval mapping more straightforward, we treat the recombination rates as unknown parameters, and then simultaneously obtain the estimates of QTL positions, QTL effects and threshold parameters via the EM algorithm. We perform simulation experiments to investigate and compare the proposed method. We also present a real example to test the reasonableness of the considered model and estimate both model parameters and QTL parameters. Both results of simulations and example show that the method we proposed is reasonable and effective.

Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study.

The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, the original sampling scheme of these imaging genetic studies is primarily the retrospective case-control design, whereas most existing statistical analyses of these studies ignore such sampling scheme by directly correlating imaging phenotypes (called the secondary traits) with genotype. Although it has been well documented in genetic epidemiology that ignoring the case-control sampling scheme can produce highly biased estimates, and subsequently lead to misleading results and suspicious associations, such findings are not well documented in imaging genetics. We use extensive simulations and a large-scale imaging genetic data analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to evaluate the effects of the case-control sampling scheme on GWAS results based on some standard statistical methods, such as linear regression methods, while comparing it with several advanced statistical methods that appropriately adjust for the case-control sampling scheme.

A nonparametric method to test for associations between rare variants and multiple traits.

More and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall's τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits,especially for multivariate ordinal traits.

Nonparametric tests of associations with disease based on U-statistics.

In case-control studies, association analysis was designed to test whether genetic variants were associated with human diseases. To evaluate the association, analysing one genetic marker at a time suffered from weak power, because of the correction for multiple testing and possibly small genetic effects. An alternative strategy was to test simultaneous effects of multiple markers, which was believed to be more powerful. However, when the number of markers under investigation was large, they would be subjected to weak power as well, because of the greater degrees of freedom. To conquer these limitations in case-control studies, we proposed a novel method that could test joint association of several loci (i.e. haplotype), with only a single degree of freedom. In this research, we developed a nonparametric approach, which was based on U-statistics. We also introduced a new kernel for U-statistic, which could combine the haplotype structure information, and was expected to enhance the power. Simulations indicated that our proposed approach offered merits in identifying the associations between diseases and haplotypes. Application of our method to a study of candidate genes for internalising disorder illustrated its virtue in utility and interpretation, and provided an excellent result in detecting the associations.

Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models.

BACKGROUND: Identifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) arising from genetic background are often dependent. The existing methods, i.e., local index of significance (LIS) and pooled local index of significance (PLIS), were both proposed for modeling SNP dependence and assumed that the whole chromosome follows a hidden Markov model (HMM). However, the fact that SNP data are often collected from separate heterogeneous regions of a single chromosome encourages different chromosomal regions to follow different HMMs. In this research, we developed a data-driven penalized criterion combined with a dynamic programming algorithm to find change points that divide the whole chromosome into more homogeneous regions. Furthermore, we extended PLIS to analyze the dependent tests obtained from multiple chromosomes with different regions for GWAS. RESULTS: The simulation results show that our new criterion can improve the performance of the model selection procedure and that our region-specific PLIS (RSPLIS) method is better than PLIS at detecting disease-associated SNPs when there are multiple change points along a chromosome. Our method has been used to analyze the Daly study, and compared with PLIS, RSPLIS yielded results that more accurately detected disease-associated SNPs. CONCLUSIONS: The genomic rankings based on our method differ from the rankings based on PLIS. Specifically, for the detection of genetic variants with weak effect sizes, the RSPLIS method was able to rank them more efficiently and with greater power.

Transcriptional profiling of mouse uterus at pre-implantation stage under VEGF repression.

Uterus development during pre-implantation stage affects implantation process and embryo growth. Aberrant uterus development is associated with many human reproductive diseases. Among the factors regulating uterus development, vascular remodeling promoters are critical for uterus function and fertility. Vascular endothelial growth factor (VEGF), as one of the major members, has been found to be important in endothelial cell growth and blood vessel development, as well as in non-endothelial cells. VEGF mediation in reproduction has been broadly studied, but VEGF-induced transcriptional machinery during implantation window has not been systematically studied. In this study, a genetically repressed VEGF mouse model was used to analyze uterus transcriptome at gestation 2.5 (G2.5) by Solexa/Illumina's digital gene expression (DGE) system. A number of 831 uterus-specific and 2398 VEGF-regulated genes were identified. Gene ontology (GO) analysis indicated that genes actively involved in uterus development were members of collagen biosynthesis, cell proliferation and cell apoptosis. Uterus-specific genes were enriched in activities of phosphatidyl inositol phosphate kinase, histone H3-K36 demethylation and protein acetylation. Among VEGF-regulated genes, up-regulated were associated with RNA polymerase III activity while down-regulated were strongly related with muscle development. Comparable numbers of antisense transcripts were identified. Expression levels of the antisense transcripts were found tightly correlated with their sense expression levels, an indication of possibly non-specific transcripts generated around the active promoters and enhancers. The antisense transcripts with exceptionally high or low expression levels and the antisense transcripts under VEGF regulation were also identified. These transcripts may be important candidates in regulation of uterus development. This study provides a global survey on genes and antisense transcripts regulated by VEGF in the pre-implantation stage. Results will contribute to further study the candidate genes and pathways in regulating implantation process and related diseases.

A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines.

In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data (MASAL), which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.

Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau().

Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.

Genome-wide association studies using haplotype clustering with a new haplotype similarity.

Association analysis, with the aim of investigating genetic variations, is designed to detect genetic associations with observable traits, which has played an increasing part in understanding the genetic basis of diseases. Among these methods, haplotype-based association studies are believed to possess prominent advantages, especially for the rare diseases in case-control studies. However, when modeling these haplotypes, they are subjected to statistical problems caused by rare haplotypes. Fortunately, haplotype clustering offers an appealing solution. In this research, we have developed a new befitting haplotype similarity for "affinity propagation" clustering algorithm, which can account for the rare haplotypes primely, so as to control for the issue on degrees of freedom. The new similarity can incorporate haplotype structure information, which is believed to enhance the power and provide high resolution for identifying associations between genetic variants and disease. Our simulation studies show that the proposed approach offers merits in detecting disease-marker associations in comparison with the cladistic haplotype clustering method CLADHC. We also illustrate an application of our method to cystic fibrosis, which shows quite accurate estimates during fine mapping.