Testing the missing at random assumption in generalized linear models in the presence of instrumental variables.

Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present a new hypothesis testing approach for deciding between the conventional notions of missing at random and missing not at random in generalized linear models in the presence of instrumental variables. The foundational idea is to develop appropriate discrepancy measures between estimators whose properties significantly differ only when missing at random does not hold. We show that our testing approach achieves an objective data-oriented choice between missing at random or not. We demonstrate the feasibility, validity, and efficacy of the new test by theoretical analysis, simulation studies, and a real data analysis.

Information criteria for latent factor models: a study on factor pervasiveness and adaptivity.

We study the information criteria extensively under general conditions for high-dimensional latent factor models. Upon carefully analyzing the estimation errors of the principal component analysis method, we establish theoretical results on the estimation accuracy of the latent factor scores, incorporating the impact from possibly weak factor pervasiveness; our analysis does not require the same factor strength of all the leading factors. To estimate the number of the latent factors, we propose a new penalty specification with a two-fold consideration: i) being adaptive to the strength of the factor pervasiveness, and ii) favoring more parsimonious models. Our theory establishes the validity of the proposed approach under general conditions. Additionally, we construct examples to demonstrate that when the factor strength is too weak, scenarios exist such that no information criterion can consistently identify the latent factors. We illustrate the performance of the proposed adaptive information criteria with extensive numerical examples, including simulations and a real data analysis.

A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.

OBJECTIVES: Electronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach for designing manual chart review, where EHR-derived phenotypes can be used to guide the selection of charts to be reviewed in order to maximize statistical efficiency in the subsequent estimation of risk factor associations. MATERIALS AND METHODS: After applying outcome-dependent sampling, an augmented estimator can be constructed by optimally combining the chart-reviewed phenotypes from the selected patients with the error-prone EHR-derived phenotype. We conducted simulation studies to evaluate the proposed method and applied our method to data on colon cancer recurrence in a cohort of patients treated for a primary colon cancer in the Kaiser Permanente Washington (KPW) healthcare system. RESULTS: Simulations verify the coverage probability of the proposed method and show that, when disease prevalence is less than 30%, the proposed method has smaller variance than an existing method where the validation set for chart review is uniformly sampled. In addition, from design perspective, the proposed method is able to achieve the same statistical power with 50% fewer charts to be validated than the uniform sampling method, thus, leading to a substantial efficiency gain in chart review. These findings were also confirmed by the application of the competing methods to the KPW colon cancer data. DISCUSSION: Our simulation studies and analysis of data from KPW demonstrate that, compared to an existing uniform sampling method, the proposed outcome-dependent method can lead to a more efficient chart review sampling design and unbiased association estimates with higher statistical efficiency. CONCLUSION: The proposed method not only optimally combines phenotypes from chart review with EHR-derived phenotypes but also suggests an efficient design for conducting chart review, with the goal of improving the efficiency of estimated risk factor associations using EHR data.

Empirical Frequency Band Analysis of Nonstationary Time Series.

The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be optimal or justified for adequately summarizing information from a given time series process under current study. There is a dearth of methods for empirically constructing statistically optimal bands for a given signal. The goal of this article is to provide a standardized, unifying approach for deriving and analyzing customized frequency bands. A consistent, frequency-domain, iterative cumulative sum based scanning procedure is formulated to identify frequency bands that best preserve nonstationary information. A formal hypothesis testing procedure is also developed to test which, if any, frequency bands remain stationary. The proposed method is used to analyze heart rate variability of a patient during sleep and uncovers a refined partition of frequency bands that best summarize the time-varying power spectrum.

LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD.

We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions. Since our approach actually requires no estimation, it is advantageous in scenarios such as the single-index models where even specification and identification of a marginal model is an issue. By automatically incorporating the level of variation of the nonparametric regression and directly assessing the strength of data evidence supporting local contribution from each explanatory variable, our approach provides a unique perspective for solving feature screening problems. Theoretical analysis shows that our approach can handle data dimensionality growing exponentially with the sample size. With extensive theoretical illustrations and numerical examples, we show that the local independence screening approach performs promisingly.

Resveratrol preconditioning protects hepatocytes against hepatic ischemia reperfusion injury via Toll-like receptor 4/nuclear factor-κB signaling pathway in vitro and in vivo.

The purpose of this study was to investigate the protective effect of resveratrol against hepatic ischemia reperfusion injury (HIRI) and explore the potential underlying mechanism. Resveratrol-pretreated BRL-3A (rat liver) cells and rats underwent hypoxia/reoxygenation and hepatic ischemia/reperfusion, respectively. BRL-3A cell damage was evaluated, and the mRNA and protein expression of related signal molecules was assessed in cell model. The protein expression of related signal molecules was also assessed in rat model. Inflammatory cytokines levels were determined in the cell supernatant and rat serum while rat liver function and hepatocyte apoptosis were assessed. The results revealed that resveratrol significantly enhanced cell viability, inhibited cell apoptosis, and decreased levels of lactate dehydrogenase (LDH) and production of tumor necrosis factor-α (TNF-α) and interleukin-(IL)-1ß in the cell supernatant. In addition, resveratrol ameliorated elevated Toll-like receptor 4 (TLR4) and nuclear factor (NF)-κB, and the depressed inhibitor of NF-κB (IκB)-α caused by hypoxia/reoxygenation stimulation in BRL-3A cells. Moreover, resveratrol inhibited the translocation of NF-κB p65 after the stimulation of hypoxia/reoxygenation in BRL-3A cells. In vivo assays revealed that resveratrol reduced levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and liver pathological changes, while it alleviated hepatocyte apoptosis, negatively mediated the production of TNF-α and IL-1ß in serum, and reversed TLR4/NF-κB signaling pathway caused by hepatic ischemia/reperfusion stimulation in liver tissues. The results indicate that resveratrol protected hepatocytes against HIRI, which may be mediated in part via the TLR4/NF-κB signaling pathway.

Everolimus in de novo liver transplant recipients: a systematic review.

BACKGROUND: Everolimus has no nephrotoxicity and is used to treat patients with post-liver transplant chronic renal insufficiency. The present systematic review was to evaluate the efficacy and safety of everolimus in de novo liver transplant patients. DATA SOURCES: Randomized controlled trials comparing everolimus for de novo liver transplant in PubMed, the Cochrane Library, and ScienceDirect published up to March 31, 2014 were searched by two independent reviewers. Mean differences and 95% confidence interval (95% CI) for renal function, relative risk (RR) and 95% CI for treated biopsy-proven acute rejection (tBPAR), graft loss, death, neoplasms/tumor recurrence, and adverse events were collected. Meta-analyses were performed with RevMan version 5.10. RESULTS: A total of four randomized controlled trials covering 1119 cases were included. The meta-analyses revealed that compared with standard exposure of calcineurin inhibitors (CNIs), everolimus combined with reduced CNIs improved creatinine clearance (calculated with the Cockcroft-Gault formula) by 5.13 mL/min at one year (95% CI: 0.42-9.84; P=0.03), and decreased tBPAR (RR: 0.56; 95% CI: 0.35-0.90; P=0.02). Everolimus initiation with CNIs elimination improved glomerular filtration rate (GFR, measured with the modification of diet in renal disease formula) of 10.42 mL/min/1.73 m2 (95% CI: 3.44-17.41; P<0.01) one year after treatment, but increased tBPAR (RR: 1.71; 95% CI: 1.15-2.53; P<0.01). Everolimus decreased the risk of neoplasms/tumor recurrence after liver transplant (RR: 0.60; 95% CI: 0.34-1.03; P=0.06), but was associated with greater risk of adverse events which resulted in drug discontinuation (RR: 1.98; 95% CI: 1.49-2.64; P<0.01). CONCLUSIONS: Early introduction of everolimus combined with low-dose or no CNI in de novo liver transplant significantly improves renal function one year post treatment. Everolimus combined with low-dose CNI decreases the risk of tBPAR one year after liver transplant, but everolimus administered without CNIs increases tBPAR.

MARGINAL EMPIRICAL LIKELIHOOD AND SURE INDEPENDENCE FEATURE SCREENING.

We study a marginal empirical likelihood approach in scenarios when the number of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the parameters of interest are systematically examined, and we find that the marginal empirical likelihood ratio evaluated at zero can be used to differentiate whether an explanatory variable is contributing to a response variable or not. Based on this finding, we propose a unified feature screening procedure for linear models and the generalized linear models. Different from most existing feature screening approaches that rely on the magnitudes of some marginal estimators to identify true signals, the proposed screening approach is capable of further incorporating the level of uncertainties of such estimators. Such a merit inherits the self-studentization property of the empirical likelihood approach, and extends the insights of existing feature screening methods. Moreover, we show that our screening approach is less restrictive to distributional assumptions, and can be conveniently adapted to be applied in a broad range of scenarios such as models specified using general moment conditions. Our theoretical results and extensive numerical examples by simulations and data analysis demonstrate the merits of the marginal empirical likelihood approach.