Left truncation in linked data: A practical guide to understanding left truncation and applying it using SAS and R

Time-to-event data such as time to death are broadly used in medical research and drug development to understand the efficacy of a therapeutic. For time-to-event data, right censoring (data only observed up to a certain point of time) is common and easy to recognize. Methods that use right censored data, such as the Kaplan–Meier estimator and the Cox proportional hazard model, are well established. Time-to-event data can also be left truncated, which arises when patients are excluded from the sample because their events occur before a specific milestone, potentially resulting in an immortal time bias. For example, in a study evaluating the association between biomarker status and overall survival, patients who did not live long enough to receive a genomic test were not observed in the study. Left truncation causes selection bias and often leads to an overestimate of survival time. In this tutorial, we used a nationwide electronic health record-derived de-identified database to demonstrate how to analyze left truncated and right censored data without bias using example code from SAS and R.     

Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non-inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non-inferiority for two primary outcomes. While non-inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one-sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non-inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment-related adverse outcomes.     

A comparison of statistical methods for animal oncology studies

In pre-clinical oncology studies, tumor-bearing animals are treated and observed over a period of time in order to measure and compare the efficacy of one or more cancer-intervention therapies along with a placebo/standard of care group. A data analysis is typically carried out by modeling and comparing tumor volumes, functions of tumor volumes, or survival. Data analysis on tumor volumes is complicated because animals under observation may be euthanized prior to the end of the study for one or more reasons, such as when an animal's tumor volume exceeds an upper threshold. In such a case, the tumor volume is missing not-at-random for the time remaining in the study. To work around the non-random missingness issue, several statistical methods have been proposed in the literature, including the rate of change in log tumor volume and partial area under the curve. In this work, an examination and comparison of the test size and statistical power of these and other popular methods for the analysis of tumor volume data is performed through realistic Monte Carlo computer simulations. The performance, advantages, and drawbacks of popular statistical methods for animal oncology studies are reported. The recommended methods are applied to a real data set.     

"一带一盟"对接合作视阈下贸易便利化对出口持续时间的影响研究

巩固发展同欧亚经济联盟经贸合作关系对于促进"一带一路"建设具有重要意义。文章建立了比较全面的贸易便利化三级指标体系,贴近欧亚经济联盟成员国的实际情况;采用CEPII-BACI数据库2007—2016年HS6分位产品层面数据,首先运用生存分析方法估计中国与欧亚经济联盟及成员国出口贸易关系持续时间,其次运用Cloglog模型着重考察贸易便利化对双边出口贸易持续时间的影响;进一步引入贸易便利化与区域经济一体化的交互项,考察欧亚经济联盟启动后贸易便利化对出口持续时间的影响,研究发现:中国与欧亚经济联盟出口持续时间普遍较短,中位数只有3年,贸易关系持续时间段超过1年占比为63.7%,超过2年占比为50.5%,下降幅度为13.2%,第9年时仅剩29.6%,不足三分之一,中国与欧亚经济联盟成员国的贸易关系持续时间较短且失败概率具有负时间依赖性;贸易便利化会显著降低中国与欧亚经济联盟出口贸易关系失败的风险,提高双边贸易关系的生存率,在微观层面上说明贸易便利化水平提高,跨国企业在国际贸易活动中所支付的交易成本降低,提升利润空间从而延长企业出口持续时间;贸易便利化对不同类型产品出口持续时间的影响程度具有异质性,从回归系数大小来看,从重到轻依次为劳动密集型产品、资本密集型产品和初级产品。当贸易便利化水平变化,价值链较长、国内附加值较高的劳动密集型产品和资本密集型产品更易受到贸易便利化引致的"技术外溢"效应影响,而对于初级产品来说,农业政策的影响效果大于贸易便利化水平,因此相对于资本密集型产品和劳动密集型产品,贸易便利化水平对初级产品的影响相对较低;进一步通过交互项模型发现,欧亚经济联盟启动后产生关税同盟效应,成员国间形成零关税五国区域大市场,促进内部企业生产要素和资源配置的优化,内部要素流动更加自由便捷高效,成员国可以获取更多的规模经济效益,同时促使中国出口商品更加快捷高效地向欧亚经济联盟内其他成员国转移,增加了中国企业出口意愿进而提升了贸易便利化对出口持续时间的促进作用。文章的研究凸显了贸易便利化提升出口持续时间的关键作用,对于"一带一路"背景下提升中国与欧亚经济联盟经贸合作水平具有一定的实践参考价值。     

A model selection criterion for clustered survival analysis with informative cluster size

We propose a model selection criterion for correlated survival data when the cluster size is informative to the outcome. This approach, called Resampling Cluster Survival Information Criterion (RCSIC), uses the Cox proportional hazards model that is weighted with the inverse of the cluster size. The RCSIC based on the within-cluster resampling idea takes into account the possible variability of the within-cluster subsampling and the possible informativeness of cluster sizes. The RCSIC allows for easy execution for the within-cluster resampling idea without a large number of resamples of the data. In contrast with the traditional model selection method in survival analysis, the RCSIC has an additional penalization for the within-cluster subsampling variability. Our simulations show the satisfactory results where the RCSIC provides a more robust power for variable selection in terms of clustered survival analysis, regardless of whether informative cluster size exists or not. Applying the RCSIC method to a periodontal disease studies, we identify the tooth loss in patients associated with the risk factors, Age, Filled Tooth, Molar, Crown, Decayed Tooth, and Smoking Status, respectively.     

A new reproducing kernel-based nonlinear dimension reduction method for survival data

Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction for survival data is proposed. An isometric isomorphism is constructed based on the RKHS property, then the nonlinear function in the RKHS can be represented by the inner product of two elements that reside in the isomorphic feature space. Due to the censorship of survival data, double slicing is used to estimate the weight function to adjust for the censoring bias. The nonlinear sufficient dimension reduction (SDR) subspace is estimated by a generalized eigen-decomposition problem. The asymptotic property of the estimator is established based on the perturbation theory. Finally, the performance of RDSIR is illustrated on simulated and real data. The numerical results show that RDSIR is comparable with the linear SDR method. Most importantly, RDSIR can also effectively extract nonlinearity from survival data.     

《管家》中的“融合”主题探徽

玛丽莲·罗宾逊是美国最重要的当代作家之一,她1980年出版的小说处女作《管家》是一部文笔优美、思想深刻的作品.物质和精神两个层面的“融合”是该小说最为核心的主题.物质层面的“融合”展现了自然的宽容与和谐,同时使家的概念得以延伸;精神层面的“融合”展现了两种不同的女性身份和生存方式.《管家》虽展示了两种截然不同的女性身份,但作者并没有简单、狭隘地肯定一种女性身份而去否定另一种,“融合”这一主题揭示了作家对女性生存、人与自然相处等人类所面临的重要问题的关注和思考.     

论对象化及人之存在

人的存在是一种以否定对象、肯定自身为主要形式的矛盾存在，人在与对象的相互作用过程中塑造和设定自我，对象化 是人之存在的根本形式，其中对象性思维是对象化的静态表现，而马克思所提出的人类现实的劳动实践活动则是对象化的动 态内涵。     

Sensitivity analyses for the principal ignorability assumption using multiple imputation

In the context of clinical trials, there is interest in the treatment effect for subpopulations of patients defined by intercurrent events, namely disease-related events occurring after treatment initiation that affect either the interpretation or the existence of endpoints. With the principal stratum strategy, the ICH E9(R1) guideline introduces a formal framework in drug development for defining treatment effects in such subpopulations. Statistical estimation of the treatment effect can be performed based on the principal ignorability assumption using multiple imputation approaches. Principal ignorability is a conditional independence assumption that cannot be directly verified; therefore, it is crucial to evaluate the robustness of results to deviations from this assumption. As a sensitivity analysis, we propose a joint model that multiply imputes the principal stratum membership and the outcome variable while allowing different levels of violation of the principal ignorability assumption. We illustrate with a simulation study that the joint imputation model-based approaches are superior to naive subpopulation analyses. Motivated by an oncology clinical trial, we implement the sensitivity analysis on a time-to-event outcome to assess the treatment effect in the subpopulation of patients who discontinued due to adverse events using a synthetic dataset. Finally, we explore the potential usage and provide interpretation of such analyses in clinical settings, as well as possible extension of such models in more general cases.     

现代性视域下新媒体时代人生存面临的返魅现象

信息的重要作用之一在于对世界进行祛魅,而新媒体时代的“过度信息”却使世界重返迷魅.生存于新媒体时代的人,会时常感知到充裕信息带来的迷惑和虚拟社区带来的困境.看似凸显新媒体时代“直白”精神的“象征性”“隐喻”,却神奇地被人们征用,符号的非体系化已经成为新的社会机制的密码.过度信息和符号的非体系化成为新媒体时代人类生存再陷迷魅的原因.赛博空间的话语权威掌握在少数精英手上,令草根阶层喜不自禁的民主技术并没有必然促进技术的民主化,并非“等量信息”就能引起“等量关注”.景观的诱惑、真相的谋杀和个人生存痕迹的监控,构成了“新媒体恐怖主义”的基调.媒介化生存是一种自我呈现的过程,它既是目的,又是手段.作为目的,人成为了媒介;作为手段,人被媒介操控.人类理性如果不能驾驭这些行为,不能为这些行为的行使设定范围,那么,生活在新媒体时代就很难说是一件幸运的事情.