Paradoxes and resolutions for semiparametric fusion of individual and summary data

Suppose we have available individual data from an internal study and various types of summary statistics from relevant external studies. External summary statistics have been used as constraints on the internal data distribution, which promised to improve the statistical inference in the internal data; however, the additional use of external summary data may lead to paradoxical results: efficiency loss may occur if the uncertainty of summary statistics is not negligible and large estimation bias can emerge even if the bias of external summary statistics is small. We investigate these paradoxical results in a semiparametric framework. We establish the semiparametric efficiency bound for estimating a general functional of the internal data distribution, which is shown to be no larger than that using only internal data. We propose a data-fused efficient estimator that achieves this bound so that the efficiency paradox is resolved. Besides, a debiased estimator is further proposed which has selection consistency property by employing adaptive lasso penalty so that the resultant estimator can achieve the same asymptotic distribution as the oracle one that uses only unbiased summary statistics, which resolves the bias paradox. Simulations and application to a Helicobacter pylori infection dataset are used to illustrate the proposed methods.Comment: 16 pages, 3 figure

Identifying effects of multiple treatments in the presence of unmeasured confounding

Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions. In this paper, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding. The auxiliary variables approach leverages auxiliary variables that are not causally associated with the outcome; in the case of a univariate confounder, our method only requires one auxiliary variable, unlike existing instrumental variable methods that would require as many instruments as there are treatments. An alternative null treatments approach relies on the assumption that at least half of the confounded treatments have no causal effect on the outcome, but does not require a priori knowledge of which treatments are null. Our identification strategies do not impose parametric assumptions on the outcome model and do not rest on estimation of the confounder. This work extends and generalizes existing work on unmeasured confounding with a single treatment, and provides a nonparametric extension of models commonly used in bioinformatics

Novel Mechanism of Nonalcoholic Lipid Accumulation Promoting Malignant Transformation of Hepatocytes

The incidence of hepatocellular carcinoma (HCC) is steadily increasing in worldwide, which has been a public concern significantly associated with diabetes and non-alcoholic fatty liver disease (NAFLD) is an emerging risk factor with increasing prevalence nowadays, with gradually instead of HBV and HCV, aflatoxin, or alcohol liver disease as major etiological factors. The deeply worrisome aspects of these high risk factors are their large spread in population. Systemic and genetic mechanisms involved in malignant transformation of liver cells as well as useful biomarkers at early stage of HCC are being investigated. However, the exact mechanisms from NAFLD to HCC still remain to be explored. In this paper, some advances of liver lipid accumulation were summarized on the relationship between NAFLD and hepatocytes malignant transformation

Oncogenic Secretory Clusterin: A Promising Therapeutic Target for Hepatocellular Carcinoma

Oncogenic secretory clusterin (sCLU) is a stress-induced molecular chaperone that confers proliferative and survival advantages to hepatocellular carcinoma (HCC), plays a crucial role in cell proliferation, multiple drug resistance, metastasis, and tumor progression. However, the targeted effects and molecular mechanisms of sCLU for malignant tumor are still unknown. This chapter aims to review some progression of oncogenic sCLU as a promising therapeutic target for HCC. An English-language literature search was conducted using bibliographic databases on some valuable articles in focused review questions to analyze the interventions and findings of included studies using a conceptual framework. The positive rate of hepatic sCLU expression in cancerous tissues was significantly higher more than that in their surrounding non-cancerous ones at gene transcription level or at protein level, with increasing according to tumor-node-metastasis (TNM) staging. Abnormal expression of oncogenic sCLU associated with poor differentiation degree and TNM stage of HCC also has been considered as a valuable diagnostic or independent prognostic biomarker for HCC. Furthermore, silencing sCLU at mRNA level by specific shRNA or inhibition by OGX-011 suppressed the colony formation and proliferation of tumor cells with apoptosis increasing, cell cycle arrested, alterations of cell migration and invasion behaviors, decreasing phosphorylation level of Akt and GSK-3β in vitro, and significantly suppressing the xenograft tumor growth with decreasing expression of β-catenin, p-GSK3β, and cyclinD1 in vivo. The oncogenic sCLU expression was closely associated with tumor progression, and it should be a novel potential molecular-targeted therapy for HCC

A Monte Carlo Method for Rayleigh Scattering in Liquid Detectors

A new Monte Carlo method has been implemented to describe the angular and polarization distributions of anisotropic liquids, like water and linear alkylbenzene, by considering orientational fluctuations of polarizability tensors. The scattered light of anisotropic liquids is depolarized with an angular distribution of $1+(1-\rho_v)/(1+3\rho_v)\cos^2\theta$, which is modified by the depolarization ratio $\rho_v$. A standalone experiment has validated the simulation results of LAB. The new method can provide more accurate knowledge on light propagation in large liquid detectors, which is beneficial to the developments of reconstruction for detectors.Comment: 13 pages, 5 figure