Annals of Applied Statistics最新文献

In this work we study the lifetime Medicare spending patterns of patients with end-stage renal disease (ESRD). We extract the information of patients who started their ESRD services in 2007-2011 from the United States Renal Data System (USRDS). Patients are partitioned into three groups based on their kidney transplant status: 1-unwaitlisted and never transplanted, 2-waitlisted but never transplanted, and 3-waitlisted and then transplanted. To study their Medicare cost trajectories, we use a semiparametric regression model with both fixed and bivariate time-varying coefficients to compare groups 1 and 2, and a bivariate time-varying coefficient model with different starting times (time since the first ESRD service and time since the kidney transplant) to compare groups 2 and 3. In addition to demographics and other medical conditions, these regression models are conditional on the survival time, which ideally depict the lifetime Medicare spending patterns. For estimation, we extend the profile weighted least squares (PWLS) estimator to longitudinal data for the first comparison and propose a two-stage estimating method for the second comparison. We use sandwich variance estimators to construct confidence intervals and validate inference procedures through simulations. Our analysis of the Medicare claims data reveals that waitlisting is associated with a lower daily medical cost at the beginning of ESRD service among waitlisted patients which gradually increases over time. Averaging over lifespan, however, there is no difference between waitlisted and unwaitlisted groups. A kidney transplant, on the other hand, reduces the medical cost significantly after an initial spike.

Mendelian randomization (MR) assesses the total effect of exposure on outcome. With the rapidly increasing availability of summary statistics from genome-wide association studies (GWASs), MR leverages existing summary statistics and is widely used to study the causal effects among complex traits and diseases. The total effect in the population is a sum of indirect and direct effects. For complex disease outcomes with complicated etiologies, and/or for modifiable exposure traits, there may exist more than one pathway between exposure and outcome. The direct effect and the indirect effect via a mediator of interest could be of opposite directions, and the total effect estimates may not be informative for treatment and prevention decision-making or may be even misleading for different subgroups of patients. Causal mediation analysis delineates the indirect effect of exposure on outcome operating through the mediator and the direct effect transmitted through other mechanisms. However, causal mediation analysis often requires individual-level data measured on exposure, outcome, mediator and confounding variables, and the power of the mediation analysis is restricted by sample size. In this work, motivated by a study of the effects of atrial fibrillation (AF) on Alzheimer's dementia, we propose a framework for Integrative Mendelian randomization and Mediation Analysis (IMMA). The proposed method integrates the total effect estimates from MR analyses based on large-scale GWASs with the direct and indirect effect estimates from mediation analysis based on individual-level data of a limited sample size. We introduce a series of IMMA models, under the scenarios with or without exposure-mediator interaction and/or study heterogeneity. The proposed IMMA models improve the estimation and the power of inference on the direct and indirect effects in the population, as well as the characterization of the variation of effects. Our analyses showed a significant positive direct effect of AF on Alzheimer's dementia risk not through the use of the oral anticoagulant treatment and a significant indirect effect of AF-induced anticoagulant treatment in reducing Alzheimer's dementia risk. The results suggested potential Alzheimer's dementia risk prediction and prevention strategies for AF patients, and paved the way for future re-evaluation of anticoagulant treatment guidelines for AF patients. A sensitivity analysis was conducted to assess the sensitivity of the conclusions to a key assumption of the IMMA approach.

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

Functional connectivity of the brain, characterized by interconnected neural circuits across functional networks, is a cutting-edge feature in neuroimaging. It has the potential to mediate the effect of genetic variants on behavioral outcomes or diseases. Existing mediation analysis methods can evaluate the impact of genetics and brain structurefunction on cognitive behavior or disorders, but they tend to be limited to single genetic variants or univariate mediators, without considering cumulative genetic effects and the complex matrix and group and network structures of functional connectivity. To address this gap, the paper presents an integrative network-based mediation model (NMM) that estimates the effect of multiple genetic variants on behavioral outcomes or diseases mediated by functional connectivity. The model incorporates group information of inter-regions at broad network level and imposes low-rank and sparse assumptions to reflect the complex structures of functional connectivity and selecting network mediators simultaneously. We adopt block coordinate descent algorithm to implement a fast and efficient solution to our model. Simulation results indicate the efficacy of the model in selecting active mediators and reducing bias in effect estimation. With application to the Human Connectome Project Youth Adult (HCP-YA) study of 493 young adults, two genetic variants (rs769448 and rs769449) on the APOE4 gene are identified that lead to deficits in functional connectivity within visual networks and fluid intelligence.

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.

With advances in high-throughput technology, molecular disease subtyping by high-dimensional omics data has been recognized as an effective approach for identifying subtypes of complex diseases with distinct disease mechanisms and prognoses. Conventional cluster analysis takes omics data as input and generates patient clusters with similar gene expression pattern. The omics data, however, usually contain multi-faceted cluster structures that can be defined by different sets of gene. If the gene set associated with irrelevant clinical variables (e.g., sex or age) dominates the clustering process, the resulting clusters may not capture clinically meaningful disease subtypes. This motivates the development of a clustering framework with guidance from a pre-specified disease outcome, such as lung function measurement or survival, in this paper. We propose two disease subtyping methods by omics data with outcome guidance using a generative model or a weighted joint likelihood. Both methods connect an outcome association model and a disease subtyping model by a latent variable of cluster labels. Compared to the generative model, weighted joint likelihood contains a data-driven weight parameter to balance the likelihood contributions from outcome association and gene cluster separation, which improves generalizability in independent validation but requires heavier computing. Extensive simulations and two real applications in lung disease and triple-negative breast cancer demonstrate superior disease subtyping performance of the outcome-guided clustering methods in terms of disease subtyping accuracy, gene selection and outcome association. Unlike existing clustering methods, the outcome-guided disease subtyping framework creates a new precision medicine paradigm to directly identify patient subgroups with clinical association.

We develop a quantile regression decomposition (QRD) method for analyzing observed disparities (OD) between population groups in socioeconomic and health-related outcomes for complex survey data. The conventional decomposition approaches use the conditional mean regression to decompose the disparity into two parts, the part explained by the difference arising from the different distributions in the explanatory covariates and the remaining part, which is unexplained by the covariates. Many socioeconomic and health outcomes exhibit heteroscedastic distributions, where the magnitude of observed disparities varies across different quantiles of these outcomes. Thus, differences in the explanatory covariates may account for varying differences in the OD across the quantiles of the outcome. The QRD can identify where there are greater differences in the outcome distribution, for example, 90th quantile, and how important the covariates are in explaining those differences. Much socioeconomic and health research relies on complex surveys, such as the National Health and Nutrition Examination Survey (NHANES), that oversample individuals from disadvantaged/minority population groups in order to provide improved precision. QRD has not been extended to the complex survey setting. We improve the QRD approach proposed in Machado and Mata (2005) to yield more reliable estimates at the quantiles, where the data are sparse, and extend it to the complex survey setting. We also propose a perturbation-based variance estimation method. Simulation studies indicate that the estimates of the unexplained portions of the OD across quantiles are unbiased and the coverage of the confidence intervals are close to nominal value. This methodology is used to study disparities in body mass index (BMI) and telomere length between race/ethnic groups estimated from the NHANES data.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose hierarchical deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell-type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon outperforms existing methods and accurately estimates cellular fractions. Finally, we show the utility of HiDecon estimates in identifying the associations between cellular fractions and Alzheimer's disease.