GMM nonparametric correction methods for logistic regression with error‐contaminated covariates and partially observed instrumental variables

We consider logistic regression with covariate measurement error. Most existing approaches require certain replicates of the error‐contaminated covariates, which may not be available in the data. We propose generalized method of moments (GMM) nonparametric correction approaches that use instrumental variables observed in a calibration subsample. The instrumental variable is related to the underlying true covariates through a general nonparametric model, and the probability of being in the calibration subsample may depend on the observed variables. We first take a simple approach adopting the inverse selection probability weighting technique using the calibration subsample. We then improve the approach based on the GMM using the whole sample. The asymptotic properties are derived, and the finite sample performance is evaluated through simulation studies and an application to a real data set.     

Dynamic ordering design for dose finding in drug-combination trials

Drug-combination studies have become increasingly popular in oncology. One of the critical concerns in phase I drug-combination trials is the uncertainty in toxicity evaluation. Most of the existing phase I designs aim to identify the maximum tolerated dose (MTD) by reducing the two-dimensional searching space to one dimension via a prespecified model or splitting the two-dimensional space into multiple one-dimensional subspaces based on the partially known toxicity order. Nevertheless, both strategies often lead to complicated trials which may either be sensitive to model assumptions or induce longer trial durations due to subtrial split. We develop two versions of dynamic ordering design (DOD) for dose finding in drug-combination trials, where the dose-finding problem is cast in the Bayesian model selection framework. The toxicity order of dose combinations is continuously updated via a two-dimensional pool-adjacent-violators algorithm, and then the dose assignment for each incoming cohort is selected based on the optimal model under the dynamic toxicity order. We conduct extensive simulation studies to evaluate the performance of DOD in comparison with four other commonly used designs under various scenarios. Simulation results show that the two versions of DOD possess competitive performances in terms of correct MTD selection as well as safety, and we apply both versions of DOD to two real oncology trials for illustration.     

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug‐drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.     

Accelerated failure time models for the analysis of competing risks

Competing risks are common in clinical cancer research, as patients are subject to multiple potential failure outcomes, such as death from the cancer itself or from complications arising from the disease. In the analysis of competing risks, several regression methods are available for the evaluation of the relationship between covariates and cause-specific failures, many of which are based on Cox’s proportional hazards model. Although a great deal of research has been conducted on estimating competing risks, less attention has been devoted to linear regression modeling, which is often referred to as the accelerated failure time (AFT) model in survival literature. In this article, we address the use and interpretation of linear regression analysis with regard to the competing risks problem. We introduce two types of AFT modeling framework, where the influence of a covariate can be evaluated in relation to either a cause-specific hazard function, referred to as cause-specific AFT (CS-AFT) modeling in this study, or the cumulative incidence function of a particular failure type, referred to as crude-risk AFT (CR-AFT) modeling. Simulation studies illustrate that, as in hazard-based competing risks analysis, these two models can produce substantially different effects, depending on the relationship between the covariates and both the failure type of principal interest and competing failure types. We apply the AFT methods to data from non-Hodgkin lymphoma patients, where the dataset is characterized by two competing events, disease relapse and death without relapse, and non-proportionality. We demonstrate how the data can be analyzed and interpreted, using linear competing risks regression models.     

长联多跨低墩刚构-连续梁组合梁桥合龙顺序研究

近年来, 在国企红利征缴比例倍增,且财政部提出继续加大投入用于保障和改善民生支出力度的公共财政预算这一时代背景下，国企利润分配回归惠民具有一定的可行性，其中一条重要路径就是国有资本经营预算支出向民生导向，但与此同时,实施过程中也存在着诸多的制约因素。正确认识制约民生化的症结障碍，有利于从根本上突破现有困境，找出优化预算支出民生化的正确路径，实现作为国企出资人的全国人民共享投资收益的目标。     

Weighted quantile regression with missing covariates using empirical likelihood

This paper proposes an empirical likelihood-based weighted (ELW) quantile regression approach for estimating the conditional quantiles when some covariates are missing at random. The proposed ELW estimator is computationally simple and achieves semiparametric efficiency if the probability of missingness is correctly specified. The limiting covariance matrix of the ELW estimator can be estimated by a resampling technique, which does not involve nonparametric density estimation or numerical derivatives. Simulation results show that the ELW method works remarkably well in finite samples. A real data example is used to illustrate the proposed ELW method.     

Bayesian adaptive linearization method for phase I drug combination trials with dimension reduction

Many phase I drug combination designs have been proposed to find the maximum tolerated combination (MTC). Due to the two‐dimension nature of drug combination trials, these designs typically require complicated statistical modeling and estimation, which limit their use in practice. In this article, we propose an easy‐to‐implement Bayesian phase I combination design, called Bayesian adaptive linearization method (BALM), to simplify the dose finding for drug combination trials. BALM takes the dimension reduction approach. It selects a subset of combinations, through a procedure called linearization, to convert the two‐dimensional dose matrix into a string of combinations that are fully ordered in toxicity. As a result, existing single‐agent dose‐finding methods can be directly used to find the MTC. In case that the selected linear path does not contain the MTC, a dose‐insertion procedure is performed to add new doses whose expected toxicity rate is equal to the target toxicity rate. Our simulation studies show that the proposed BALM design performs better than competing, more complicated combination designs.     

经济时间序列的非线性组合建模与预测方法研究

基于模糊系统在紧立集中能够任意逼近非线性连续函数的特性,本文提出了一种基于Takagi-Sugeno模糊规则基的非线性组合预测新方法,以克服线性组合预测方法在解决非平稳时间序列组合建模问题所遇到的困难和存在的不足,并采用相应的遗传算法确定模糊系统的参数及模糊子集的划分。理论分析和大量的应用实例表明:该方法具有很强的学习与泛化能力,在处理诸如经济时间序列这种具有一定程度不确性的非线性系统的组合建模与预测方面有很好的应用价值。     

中国放弃日本战争赔偿的反思

首次分析了中国放弃日本战争赔偿的历史背景和复杂影响 ,并对中国放弃日本战争赔偿进行了深刻反思。作者认为 ,中国放弃对日本的战争赔偿要求 ,固然有着眼中日友好的长远考虑 ,但更多的是一种无奈的选择 ,其影响也是复杂的