Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.     

Principled leveraging of external data in the evaluation of diagnostic devices via the propensity score-integrated composite likelihood approach

In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost needed for the performance evaluation of an investigational diagnostic device. However, the statistical methods currently being used for such leveraging may not clearly separate study design and outcome data analysis, and they may not adequately address possible bias due to differences in clinically relevant characteristics between the subjects constituting the traditional study and those constituting the external data. This paper is intended to draw attention in the field of diagnostics to the recently developed propensity score-integrated composite likelihood approach, which originally focused on therapeutic medical products. This approach applies the outcome-free principle to separate study design and outcome data analysis and can mitigate bias due to imbalance in covariates, thereby increasing the interpretability of study results. While this approach was conceived as a statistical tool for the design and analysis of clinical studies for therapeutic medical products, here, we will show how it can also be applied to the evaluation of sensitivity and specificity of an investigational diagnostic device leveraging external data. We consider two common scenarios for the design of a traditional diagnostic device study consisting of prospectively enrolled subjects, which is to be augmented by external data. The reader will be taken through the process of implementing this approach step-by-step following the outcome-free principle that preserves study integrity.     

从目的论看软新闻的英译

功能翻译理论与传播学具有相通性,功能学派的目的论能够为软新闻的汉译英提供有力的理论支持,其中翻译主体的意图性、译文的可接受性和文化的特殊性是确定翻译策略和评价译文质量的关键性因素。     

资产互补性与专用性对农业垂直一体化的影响——基于5省378份企业与农户调查数据

从企业与农户双边资产互补性与专用性的视角,在GHM模型框架内分析了企业与农户在农业生产过程中垂直一体化的产生条件,并基于5个省份378份企业与农户的交易数据进行实证分析,结果表明：企业和农户的垂直一体化不由单方面资产专用性决定,而是由企业和农户双边资产专用性和资产互补性决定。企业或农户任何一方的资产专用性较低或企业和农户的资产互补程度较低,都会导致企业和农户最终以现货市场完成交易,这解释了中国“订单农业”履约率低的现象。当企业和农户双边资产专用性程度很高或资产互补性较高时,企业和农户会以反映双边关系的垂直一体化模式完成交易。     

供应链集中度与银行信贷

供应链集中对企业而言既有整合效应也有风险效应,供应链集中度究竟如何影响企业获取银行信贷是值得探讨的重要问题。基于2007—2020年中国A股上市公司数据,研究了供应链集中度对企业银行信贷的影响。研究发现：供应链集中度与银行信贷之间呈倒“U”型关系,即随着供应链集中度的提高,企业获取的银行信贷呈现出先增加后减少的变化趋势。中介效应检验表明,经营风险和资产专用性在供应链集中度影响银行信贷关系中发挥了中介作用。异质性分析发现,在议价能力较弱、非国有和中小型的企业中,供应链集中度与银行信贷之间的倒“U”型关系更为显著。因此,企业应将供应链集中度保持在合理范围内,维持适度的经营风险和资产专用性水平以获取更多银行信贷;同时银行应关注企业的供应链集中度水平,充分评估授信风险并合理配置信贷资源,为企业发展提供资金支持。     

Cumulative/dynamic ROC curve estimation

ABSTRACT

Receiver operating-characteristic (ROC) curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, the direct generalization is known as cumulative/dynamic ROC curve. For a fixed point of time, t, one subject is allocated into the positive group if the event happens before t and into the negative group if the event is not happened at t. The presence of censored subject, which can not be directly assigned into a group, is the main handicap of this approach. The proposed cumulative/dynamic ROC curve estimator assigns a probability to belong to the negative (positive) group to the subjects censored previously to t. The performance of the resulting estimator is studied from Monte Carlo simulations. Some real-world applications are reported. Results suggest that the new estimators provide a good approximation to the real cumulative/dynamic ROC curve.     

Information borrowing methods for covariate‐adjusted ROC curve

In medical diagnostic testing problems, the covariate adjusted receiver operating characteristic (ROC) curves have been discussed recently for achieving the best separation between disease and control. Due to various restrictions such as cost, the availability of patients, and ethical issues quite frequently only limited information is available. As a result, we are unlikely to have a large enough overall sample size to support reliable direct estimations of ROCs for all the underlying covariates of interest. For example, some genetic factors are less commonly observable compared with others. To get an accurate covariate adjusted ROC estimation, novel statistical methods are needed to effectively utilize the limited information. Therefore, it is desirable to use indirect estimates that borrow strength by employing values of the variables of interest from neighbouring covariates. In this paper we discuss two semiparametric exponential tilting models, where the density functions from different covariate levels share a common baseline density, and the parameters in the exponential tilting component reflect the difference among the covariates. With the proposed models, the estimated covariate adjusted ROC is much smoother and more efficient than the nonparametric counterpart without borrowing information from neighbouring covariates. A simulation study and a real data application are reported. The Canadian Journal of Statistics 40: 569–587; 2012 © 2012 Statistical Society of Canada     

Screening for prostate cancer using multivariate mixed-effects models

Using several variables known to be related to prostate cancer, a multivariate classification method is developed to predict the onset of clinical prostate cancer. A multivariate mixed-effects model is used to describe longitudinal changes in prostate specific antigen (PSA), a free testosterone index (FTI), and body mass index (BMI) before any clinical evidence of prostate cancer. The patterns of change in these three variables are allowed to vary depending on whether the subject develops prostate cancer or not and the severity of the prostate cancer at diagnosis. An application of Bayes' theorem provides posterior probabilities that we use to predict whether an individual will develop prostate cancer and, if so, whether it is a high-risk or a low-risk cancer. The classification rule is applied sequentially one multivariate observation at a time until the subject is classified as a cancer case or until the last observation has been used. We perform the analyses using each of the three variables individually, combined together in pairs, and all three variables together in one analysis. We compare the classification results among the various analyses and a simulation study demonstrates how the sensitivity of prediction changes with respect to the number and type of variables used in the prediction process.     

湖南战略性新兴产业发展的投融资模式选择--基于一个三维立体空间分析范式的构建

基于威廉姆森的交易成本经济学和钱德勒的企业理论,针对湖南战略性新兴产业的投融资模式选择,以资产专用性程度、研究开发密集度和有形资本密集度构成的三维立体空间分析范式,为解释湖南战略性新兴产业的投融资模式选择提供了崭新的思路。充分考虑了产业的动态成长问题,并进一步对模型进行扩展。研究的政策含义是：应结合不同产业的特点,把握各类产业的动态成长过程,才能有效地、有针对性地创新湖南战略性新兴产业的投融资模式。同时,选择匹配战略性新兴产业的最优成长方式,建立多渠道、多层次的投融资模式,有效破解湖南战略性新兴产业的投融资难题。     

Robust effects of stress on early lexical representation

This study aims to elucidate the factors that affect the robustness of word form representations by exploring the relative influence of lexical stress and segmental identity (consonant vs. vowel) on infant word recognition. Our main question was which changes to the words may go unnoticed and which may lead the words to be unrecognizable. One‐hundred 11‐month‐old Hebrew‐learning infants were tested in two experiments using the Central Fixation Procedure. In Experiment 1, 20 infants were presented with iambic Familiar and Unfamiliar words. The infants listened longer to Familiar than to Unfamiliar words, indicating their recognition of frequently heard word forms. In Experiment 2, four groups of 20 infants each were tested in each of four conditions involving altered iambic Familiar words contrasted with iambic Unfamiliar nonwords. In each condition, one segment in the Familiar word was changed—either a consonant or a vowel, in either the first (unstressed) or the second (stressed) syllable. In each condition, recognition of the Familiar words despite the change indicates a less accurate or less well‐specified representation. Infants recognized Familiar words despite changes to the weak (first) syllable, regardless of whether the change involved a consonant or a vowel (conditions 2a, 2c). However, a change of either consonant or vowel in the stressed (second) syllable blocked word recognition (conditions 2b, 2d). These findings support the proposal that stress pattern plays a key role in early word representation, regardless of segmental identity.