Supervised structural learning of semiparametric regression on high-dimensional correlated covariates with applications to eQTL studies.

Expression quantitative trait loci (eQTL) studies utilize regression models to explain the variance of gene expressions with genetic loci or single nucleotide polymorphisms (SNPs). However, regression models for eQTL are challenged by the presence of high dimensional non-sparse and correlated SNPs with small effects, and nonlinear relationships between responses and SNPs. Principal component analyses are commonly conducted for dimension reduction without considering responses. Because of that, this non-supervised learning method often does not work well when the focus is on discovery of the response-covariate relationship. We propose a new supervised structural dimensional reduction method for semiparametric regression models with high dimensional and correlated covariates; we extract low-dimensional latent features from a vast number of correlated SNPs while accounting for their relationships, possibly nonlinear, with gene expressions. Our model identifies important SNPs associated with gene expressions and estimates the association parameters via a likelihood-based algorithm. A GTEx data application on a cancer related gene is presented with 18 novel eQTLs detected by our method. In addition, extensive simulations show that our method outperforms the other competing methods in bias, efficiency, and computational cost.

Center-augmented ℓ2 -type regularization for subgroup learning.

The existing methods for subgroup analysis can be roughly divided into two categories: finite mixture models (FMM) and regularization methods with an ℓ1 -type penalty. In this paper, by introducing the group centers and ℓ2 -type penalty in the loss function, we propose a novel center-augmented regularization (CAR) method; this method can be regarded as a unification of the regularization method and FMM and hence exhibits higher efficiency and robustness and simpler computations than the existing methods. In particular, its computational complexity is reduced from the O ( n 2 ) $O(n^2)$ of the conventional pairwise-penalty method to only O ( n K ) $O(nK)$ , where n is the sample size and K is the number of subgroups. The asymptotic normality of CAR is established, and the convergence of the algorithm is proven. CAR is applied to a dataset from a multicenter clinical trial, Buprenorphine in the Treatment of Opiate Dependence; a larger R2 is produced and three additional significant variables are identified compared to those of the existing methods.

Functional data analysis with covariate-dependent mean and covariance structures.

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.

Green supply chain game model and contract design: risk neutrality vs. risk aversion.

This paper incorporates the players' risk attitudes into a green supply chain (GSC) consisting of a supplier and a retailer. The supplier conducts production and determines the green level and wholesale price as a game leader; the retailer sells green products to consumers and determines the retail price as a follower. Equilibrium solutions are derived, and the influence of risk aversion on the GSC is examined. Our results show that, for the centralized GSC, risk aversion lowers the green level and the retail price, while for the decentralized GSC, risk aversion lowers the wholesale price and the retail price, but it may induce the supplier to increase the green level given a high-risk tolerance of the supplier. Meanwhile, the risk-averse decentralized GSC may obtain more expected profit than the risk-neutral decentralized GSC. Furthermore, this paper designs a revenue-and-cost-sharing joint contract to coordinate the risk-neutral GSC, and such a contract can improve the risk-averse GSC under specific conditions.

Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data.

Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step. We therefore developed a computation method, Dimension-Reduction Spatial-Clustering (DR-SC), that can simultaneously perform dimension reduction and (spatial) clustering within a unified framework. Joint analysis by DR-SC produces accurate (spatial) clustering results and ensures the effective extraction of biologically informative low-dimensional features. DR-SC is applicable to spatial clustering in spatial transcriptomics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. Here, DR-SC relies on a latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. Underlying DR-SC is an efficient expectation-maximization algorithm based on an iterative conditional mode. As such, DR-SC is scalable to large sample sizes and can optimize the spatial smoothness parameter in a data-driven manner. With comprehensive simulations and real data applications, we show that DR-SC outperforms existing clustering and spatial clustering methods: it extracts more biologically relevant features than conventional dimension reduction methods, improves clustering performance, and offers improved trajectory inference and visualization for downstream trajectory inference analyses.

A Novel Pyroptosis-Related Prognostic Signature for Risk Stratification and Clinical Prognosis in Clear Cell Renal Cell Carcinoma.

Emerging research has substantiated that pyroptosis-related biomarkers were mightily related to the clinical outcome of patients with clear cell renal cell carcinoma (ccRCC). However, a single-gene biomarker's moderate predictive power is insufficient, and more accurate prognostic models are urgently needed. We conducted this investigation in order to develop a robust pyroptosis-related gene signature for use in risk stratification and survival prognosis in colorectal cancer. We downloaded transcriptomic data and survival information of ccRCC patients from TCGA. Bioinformatic methods were used to generate a pyroptosis-related gene signature based on data from TCGA training cohort. ROC curve, uni- and multivariate regression analyses were used for the prognostic assays. What is more, we explored the relationship between model-based risk score and the tumor microenvironment. Herein, 11 pyroptosis-related hub genes (CASP9, TUBB6, NFKB1, BNIP3, CAPN1, CD14, PRDM1, BST2, SDHB, TFAM, and GSDMB) were determined as risk signature for risk stratification and prognosis prediction for ccRCC. Kaplan-Meier curves, ROC curves, and risk plots were employed to analyze and verify its performance in all groups. Multivariate assays exhibited that risk score could be an independent prognostic factor for patients' OS. ESTIMATE algorithm showed a higher immune score in the group of high-risk. Overall, a novel pyroptosis-related gene signature generated can be employed for prognosis prediction of ccRCC patients. This may assist in individual treatment of clinical decision-making.

Cluster non-Gaussian functional data.

Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This paper focuses on clustering non-Gaussian functional data without the prior information of the number of clusters. We introduce a semiparametric mixed normal transformation model to accommodate non-Gaussian functional data, and propose a penalized approach to simultaneously estimate the parameters, transformation function, and the number of clusters. The estimators are shown to be consistent and asymptotically normal. The practical utility of the methods is confirmed via simulations as well as an application of the analysis of Alzheimer's disease study. The proposed method yields much less classification error than the existing methods. Data used in preparation of this paper were obtained from the Alzheimer's Disease Neuroimaging Initiative database.

Covariance-Insured Screening.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss weak signals. By incorporating the inter-feature dependence, a covariance-insured screening approach is proposed to identify predictors that are jointly informative but marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and a real data study for selecting potential genetic factors related to the onset of multiple myeloma.

A single-index threshold Cox proportional hazard model for identifying a treatment-sensitive subset based on multiple biomarkers.

In this paper, we introduce a single-index threshold Cox proportional hazard model to select and combine biomarkers to identify patients who may be sensitive to a specific treatment. A penalized smoothed partial likelihood is proposed to estimate the parameters in the model. A simple, efficient, and unified algorithm is presented to maximize this likelihood function. The estimators based on this likelihood function are shown to be consistent and asymptotically normal. Under mild conditions, the proposed estimators also achieve the oracle property. The proposed approach is evaluated through simulation analyses and application to the analysis of data from two clinical trials, one involving patients with locally advanced or metastatic pancreatic cancer and one involving patients with resectable lung cancer.

Light deprivation produces distinct morphological orchestrations on RGCs and cortical cells in a depressive-like YFP-H mouse model.

Neural physiological functions and synaptic changes underlying the pathogenesis of depression have obtained great achievements. However, neuronal morphological changes under a depressive state have not been well understood yet. Here a depressive-like YFP-H transgenic mouse model was produced by light deprivation (LD), and morphological changes of retinal ganglion cells (RGCs) and primary visual and auditory cortical layer 5 pyramidal cells (L5PCs) were investigated. Three distinct RGC subtypes were identified based on soma- and dendritic field (DF) size. RGA cells were highlighted by large soma and medium-sized to large DF. RGB cells were characterized by small- to medium-sized soma and small- to medium-sized DF. RGC cells were typical of small- to medium-sized soma and large DF. LD showed cell-type-specific morphological orchestrations on RGCs and predominantly promoted the dendritic growth of RGA cells, leaving no significant effect on RGB and RGC cells. LD produced a consistently suppressed effect on the morphology of primary visual and auditory cortical L5PCs. LD enhanced the dendritic spine density of primary visual cortical L5PCs, implying a compensation mechanism underlying morphological changes in individual cortical L5PCs. The increased morphological complexity of RGA cells and the simplified morphology of cortical L5PCs suggest a broad range of neuronal morphological "cross-modal plasticity" among different brain areas. Our observations in morphological changes of RGCs and cortical L5PCs under a depressive-like state will provide some insights into the pathogenesis of depression at a single neuronal morphological level.

A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.

The timing of a time-dependent treatment-e.g., when to perform a kidney transplantation-is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favor the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modeling treatment history and accounting for the possible time-varying nature of the treatment effect. We propose a varying-coefficient Cox model that investigates the efficacy of a time-dependent treatment by utilizing a global partial likelihood, which renders appealing statistical properties, including consistency, asymptotic normality and semiparametric efficiency. Extensive simulations verify the finite sample performance, and we apply the proposed method to study the efficacy of kidney transplantation for end-stage renal disease patients in the U.S. Scientific Registry of Transplant Recipients (SRTR).

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Levodopa alone compared with levodopa-sparing therapy as initial treatment for Parkinson's disease: a meta-analysis.

To assess the long-term use of L-dopa alone vs L-dopa-sparing therapy, as initial treatment, provides the most efficient long-term control of symptoms and best quality of life for people with early Parkinson's disease (PD). PubMed; Google scholar; Cochrane Central Register of Controlled Trials and the Web of Science were searched for randomised, placebo-controlled trials (RCTs) on L-dopa alone and L-dopa sparing as initial treatment in early PD patients. We used a random effects model rather than a fixed effects model because of this takes into account heterogeneity between multi-studies. Eleven RCTs were included. The results showed that L-dopa alone could evidently improve the UPDRS part I (p = 0.005), part II (p < 0.0001), part III (p < 0.0001) and UPDRS total score (p = 0.004) compared with L-dopa-sparing therapy in PD patients. Meanwhile, a reduced risk of dyskinesia (p < 0.0001, RR = 1.88, 95 % CI 1. 37-2.59) and wearing-off phenomenon (p < 0.00001, RR = 1.36, 95 % CI 1. 20-1.55) in patients treated initially with L-dopa-sparing therapy compared to L-dopa has been consistently reported. What is more, we found more patients on aL-dopa-sparing therapy were more than triple as likely to discontinue treatment prematurely due to adverse events than L-dopa treatment patients (43.7 vs 15.8 %). L-Dopa alone is the most effective medication available for treating the motor symptoms of PD patients, despite the greater incidence of involuntary movements. Meanwhile, more patients on dopamine agonists or MAOBI were more likely to discontinue treatment prematurely than L-dopa alone treatment patients within the long follow-up period.

A systematic review and meta-analysis of cognitive behavioral and psychodynamic therapy for depression in Parkinson's disease patients.

Numerous practice guidelines have recommended cognitive behavioral therapy (CBT) and psychodynamic therapy as a treatment of choice for depression in Parkinson's disease (PD). However, no recent meta-analysis has examined the effects of brief psychotherapy (which includes both CBT and psychodynamic therapy) for adult depression in PD. We decided to conduct such a systematic review and meta-analysis. We included randomized controlled trials (RCTs) examining the effects of brief psychotherapy compared with control groups, other support nursing, or pharmacotherapy. The quality of included studies was strictly evaluated. Twelve studies including 766 patients met all inclusion criteria. The result showed that brief psychotherapy could evidently improve the HAMD (p < 0.00001) and Moca scale (p = 0.006). There was no statistical significance in PDQ-39 scale (p = 0.31). In the subgroup analysis by types of brief psychotherapy, the efficacy of psychodynamic psychotherapy was better than CBT (SMD = -2.02 vs SMD = -0.90) for the outcome measure according to HAMD scale. Meanwhile, we found brief psychotherapy in China was more effective than in US (SMD = -1.54 vs SMD = -1.23), and in low quality studies was more efficacious than in high quality studies (SMD = -1.50 vs SMD = -1.33). Time of brief psychotherapy treatment above 6 weeks was superior to studies with less than 6 weeks treatment. We found brief psychotherapy is probable effective in the management of depression in PD patients. But one reason to undermine the validity of findings is high clinical heterogeneity and low methodological quality of the included trials.

Semiparametric transformation models for semicompeting survival data.

Semicompeting risk outcome data (e.g., time to disease progression and time to death) are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following two ways. First, it estimates regression coefficients and association parameters simultaneously. Second, the measure of surrogacy, for example, the proportion of the treatment effect that is mediated by the surrogate and the ratio of the overall treatment effect on the true endpoint over that on the surrogate endpoint, can be directly obtained. We propose an estimation procedure for inference and show that the proposed estimator is consistent and asymptotically normal. Extensive simulations demonstrate the valid usage of our method. We apply the method to a multiple myeloma trial to study the impact of several biomarkers on patients' semicompeting outcomes--namely, time to progression and time to death.

Selection of latent variables for multiple mixed-outcome models.

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples' values and beliefs and the social and personal characteristics that might influence them.

A semiparametric linear transformation model to estimate causal effects for survival data.

Semiparametric linear transformation models serve as useful alternatives to the Cox proportional hazard model. In this study, we use the semiparametric linear transformation model to analyze survival data with selective compliance. We estimate regression parameters and the transformation function based on pseudo-likelihood and a series of estimating equations. We show that the estimators for the regression parameters and transformation function are consistent and asymptotically normal, and both converge to their true values at the rate of n -1/2, the convergence rate expected for a parametric model. The practical utility of the methods is confirmed via simulations as well as an application of a clinical trial to evaluate the effectiveness of sentinel node biopsy in guiding the treatment of invasive melanoma.

Les modèles semi-paramétriques de transformation linéaire constituent des solutions de rechange utiles au modèle à risques proportionnels de Cox. Dans cet article, les auteurs utilisent un modèle semi-paramétrique de transformation linéaire pour analyser des données de survie affichant une observance sélective. Les auteurs estiment les paramètres de régression et la fonction de transformation de ce modèle au moyen de la pseudo-vraisemblance et d'une série d' équations d'estimation. Ils montrent que ces estimateurs convergent et sont asymptotiquement normaux. Leur vitesse de convergence de n −1/2 correspond au taux attendu dans un modèle paramétrique. L'utilité pratique des méthodes est confirmée à l'aide de simulations et d'une application à un essai clinique visant à évaluer l'efficacité d'une biopsie des ganglions sentinelles dans le choix d'un traitement pour un mélanome invasif.

A DIRECT SEMIPARAMETRIC RECEIVER OPERATING CHARACTERISTIC CURVE REGRESSION WITH UNKNOWN LINK AND BASELINE FUNCTIONS.

In this article, we study a direct receiver operating characteristic (ROC) curve regression model with completely unknown link and baseline functions. A semiparametric procedure is proposed to estimate both the parametric and non-parametric components of the model. The resulting parameter estimates and ROC curve estimates are shown to be consistent and asymptotically normal with a n -1/2 convergence rate. With arbitrary link and baseline functions, our model is more robust than existing direct ROC regression models that require either complete or partially complete specification of the link and baseline functions. Moreover, the robustness of our new method is gained at little cost to efficiency, as evidenced by the parametric convergence rate of our estimators and by the simulation study. An illustrative example is given using a hearing test data set.

A nonparametric estimation of the infection curve.

Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme (SARS) epidemic in Hong Kong.

A correlated random-effects model for normal longitudinal data with nonignorable missingness.

The missing data problem is common in longitudinal or hierarchical structure studies. In this paper, we propose a correlated random-effects model to fit normal longitudinal or cluster data when the missingness mechanism is nonignorable. Computational challenges arise in the model fitting due to intractable numerical integrations. We obtain the estimates of the parameters based on an accurate approximation of the log likelihood, which has higher-order accuracy but with less computational burden than the existing approximation. We apply the proposed method it to a real data set arising from an autism study.