Constrained empirical-likelihood confidence regions in nonignorable covariate-missing data problems

Missing covariates in regression analysis are a pervasive problem in medical, social, and economic researches. We study empirical-likelihood confidence regions for unconstrained and constrained regression parameters in a nonignorable covariate-missing data problem. For an assumed conditional mean regression model, we assume that some covariates are fully observed but other covariates are missing for some subjects. By exploitation of a probability model of missingness and a working conditional score model from a semiparametric perspective, we build a system of unbiased estimating equations, where the number of equations exceeds the number of unknown parameters. Based on the proposed estimating equations, we introduce unconstrained and constrained empirical-likelihood ratio statistics to construct empirical-likelihood confidence regions for the underlying regression parameters without and with constraints. We establish the asymptotic distributions of the proposed empirical-likelihood ratio statistics. Simulation results show that the proposed empirical-likelihood methods have a better finite-sample performance than other competitors in terms of coverage probability and interval length. Finally, we apply the proposed empirical-likelihood methods to the analysis of a data set from the US National Health and Nutrition Examination Survey.     

Penalized variable selection for accelerated failure time models with random effects

Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Comparison of Cancer Detection Rates Between TRUS-Guided Biopsy and MRI-Targeted Biopsy According to PSA Level in Biopsy-Naive Patients: A Propensity Score Matching Analysis

Background

The purpose of the study was to compare cancer detection rates between 12-core transrectal ultrasound-guided prostate biopsy (TRUS-Bx) and multiparametric magnetic resonance imaging (mpMRI)-guided target prostate biopsy (MRI-TBx) according to prostate-specific antigen (PSA) level in biopsy-naive patients.

肾嫌色细胞癌与不典型透明细胞癌的CT纹理分析研究

目的研究影像组学方法在肾嫌色细胞癌和强化方式不典型的透明细胞癌二者中的应用。方法搜集行肾动脉CTA扫描的108例肾细胞癌患者的临床资料及影像学图像。应用影像组学中的Lasso回归统计方法和机器学习中的随机森林算法提取病例的CTA图像特征并使计算机学习,通过20次重复试验得到平均诊断准确率。患者的临床特征处理采用SPSS 20.0软件,计量资料用t检验,计数资料用χ^2检验,P<0.05为差异具有统计学意义。结果108例肾细胞癌中,透明细胞癌57例,嫌色细胞癌51例。两组病例临床特征中的性别和吸烟史差异具有统计学意义(P<0.05),透明细胞癌更多见于吸烟的男性患者。放射科医师对两组病例诊断的平均准确性为(45.42±3.32)%,低于Lasso回归(76.5±12.26)%和随机森林算法(78.5±6.3)%。在两组病例中,随机森林算法给出的总准确性、对嫌色细胞癌诊断的特异性要高于Lasso回归,Lasso回归对透明细胞癌诊断的敏感性高于随机森林算法。结论影像组学方法可以对肾嫌色细胞癌及透明细胞癌做出有效的鉴别诊断,且诊断能力高于放射科医师的能力。影像组学作为一种新兴的研究方法,有望为医学发展带来重要变革。     

Radiomics for classification of bone mineral loss: A machine learning study

PurposeThe purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.Materials and methodsA total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88 ± 10.6 (SD) years (range: 28–87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis + osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC).ResultsThe AUC values ranged from 0.50 to 0.78. The combination of methods RF + CLAE, RF + ORAE and RC + ORAE yielded highest performance (AUC = 0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF + PRCA and LB + PRCA had the highest performance (AUC = 0.76) in discriminating between osteoporosis and normal state in the neck region.ConclusionThe machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features.     

药品化妆品抽样及检验的有关问题探讨

目的:对药品、药包材、化妆品抽样及检验工作中发现的问题进行分析研究,旨在为监管部门和检验机构提供参考,规范抽样和检验,提高监管效能和服务水平。方法:通过抽样、样品寄送、收样、检验等环节,对药品、药包材、化妆品抽样及检验工作中发现的有关问题进行分析研究,提出相应的解决措施和意见建议。结果与结论:部分药品/药包材/化妆品的抽查检验存在抽样不规范、抽样信息不完整、抽样与检查未能兼顾、样品寄送不规范、检验登记单和检验报告信息不完整或填写不规范、检验不规范、部分产品的批号效期标识不规范,以及部分品种因抽样量大导致抽样难以覆盖基层等问题,影响了监管效能。应根据药品、药包材、化妆品的产品特性和监管要求,分类设置并完善抽样及检验检测信息,并从强化人员培训、监督和检验有机结合、针对基层用药特点完善抽检工作机制、加强学习交流提高检验报告质量等方面加以解决。     

Caution! Overestimation of treatment effects of corticosteroid therapy for community-acquired pneumonia in a meta-analysis of randomized controlled trials

It is well known that a meta-analysis of randomized controlled trials aims to increase the power and precision of the estimated intervention effects. However, when a meta-analysis includes a limited number of patients and a small number of events, overestimation of intervention effect estimates may occur and could cause spurious results. Although many biases can cause the overestimation, random error may be the most common cause. Trial sequential analysis (TSA) can explore the independent effect of random error on intervention effect estimates in meta-analyses and protect meta-analyses against overestimation due to random error.