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Special issue dedicated to David Oakes. 大卫·奥克斯的特刊。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09572-6
Jong H Jeong, Amita K Manatunga
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引用次数: 0
Semiparametric single-index models for optimal treatment regimens with censored outcomes. 具有审查结果的最佳治疗方案的半参数单指标模型。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09566-4
Jin Wang, Donglin Zeng, D Y Lin

There is a growing interest in precision medicine, where a potentially censored survival time is often the most important outcome of interest. To discover optimal treatment regimens for such an outcome, we propose a semiparametric proportional hazards model by incorporating the interaction between treatment and a single index of covariates through an unknown monotone link function. This model is flexible enough to allow non-linear treatment-covariate interactions and yet provides a clinically interpretable linear rule for treatment decision. We propose a sieve maximum likelihood estimation approach, under which the baseline hazard function is estimated nonparametrically and the unknown link function is estimated via monotone quadratic B-splines. We show that the resulting estimators are consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. The optimal treatment rule follows naturally as a linear combination of the maximum likelihood estimators of the model parameters. Through extensive simulation studies and an application to an AIDS clinical trial, we demonstrate that the treatment rule derived from the single-index model outperforms the treatment rule under the standard Cox proportional hazards model.

人们对精准医疗的兴趣越来越大,在精准医疗中,可能被删减的生存时间往往是最重要的兴趣结果。为了发现这种结果的最佳治疗方案,我们提出了一个半参数比例风险模型,通过未知单调联系函数将治疗与单个协变量指数之间的相互作用结合起来。该模型足够灵活,允许非线性治疗-协变量相互作用,并为治疗决策提供临床可解释的线性规则。提出了一种筛极大似然估计方法,该方法对基线危险函数进行非参数估计,并通过单调二次b样条估计未知连接函数。我们证明了所得到的估计量是一致的和渐近正态的,并且有一个达到半参数效率界的协方差矩阵。最优处理规则自然是模型参数的最大似然估计量的线性组合。通过广泛的模拟研究和对艾滋病临床试验的应用,我们证明了单指标模型得出的治疗规则优于标准Cox比例风险模型下的治疗规则。
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引用次数: 0
Assessing dynamic covariate effects with survival data. 用生存数据评估动态协变量效应。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09571-7
Ying Cui, Limin Peng

Dynamic (or varying) covariate effects often manifest meaningful physiological mechanisms underlying chronic diseases. However, a static view of covariate effects is typically adopted by standard approaches to evaluating disease prognostic factors, which can result in depreciation of some important disease markers. To address this issue, in this work, we take the perspective of globally concerned quantile regression, and propose a flexible testing framework suited to assess either constant or dynamic covariate effects. We study the powerful Kolmogorov-Smirnov (K-S) and Cramér-Von Mises (C-V) type test statistics and develop a simple resampling procedure to tackle their complicated limit distributions. We provide rigorous theoretical results, including the limit null distributions and consistency under a general class of alternative hypotheses of the proposed tests, as well as the justifications for the presented resampling procedure. Extensive simulation studies and a real data example demonstrate the utility of the new testing procedures and their advantages over existing approaches in assessing dynamic covariate effects.

动态(或变化的)协变量效应往往表现出慢性疾病潜在的有意义的生理机制。然而,评估疾病预后因素的标准方法通常采用协变量效应的静态观点,这可能导致一些重要疾病标志物的贬值。为了解决这个问题,在这项工作中,我们采取了全球关注的分位数回归的角度,并提出了一个灵活的测试框架,适合于评估恒定或动态协变量效应。我们研究了强大的Kolmogorov-Smirnov (K-S)和cram - von Mises (C-V)型检验统计量,并开发了一个简单的重采样程序来处理它们复杂的极限分布。我们提供了严格的理论结果,包括极限零分布和在所提出的检验的一般类别的可选假设下的一致性,以及所提出的重采样程序的理由。广泛的模拟研究和一个真实的数据例子证明了新的测试程序的效用,以及它们在评估动态协变量效应方面比现有方法的优势。
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引用次数: 1
Mixture survival trees for cancer risk classification. 用于癌症风险分类的混合生存树。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-07-01 DOI: 10.1007/s10985-022-09552-w
Beilin Jia, Donglin Zeng, Jason J Z Liao, Guanghan F Liu, Xianming Tan, Guoqing Diao, Joseph G Ibrahim

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

在肿瘤学研究中,了解和表征患者之间的疾病异质性是非常重要的,这样可以将患者划分为不同的风险组,并在适当的时候识别出高危患者。然后,这些信息可以用于确定更均匀的患者群体,以开发精准医疗。本文提出了一种用于直接风险分类的混合生存树方法。我们假设患者可以被分为预先指定的风险组,其中每组有不同的生存概况。我们提出的基于树的方法是设计来估计潜在的群体成员使用EM算法。将观测数据的对数似然函数作为递归划分的分割准则。有限样本的性能通过广泛的模拟研究进行了评估,并提出了一种方法,说明了一个案例研究在乳腺癌。
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引用次数: 0
Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. 最佳个体化治疗规则的隐私保护估计:最大限度延长严重抑郁症相关结果的案例研究。
IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-07-01 Epub Date: 2022-05-02 DOI: 10.1007/s10985-022-09554-8
Erica E M Moodie, Janie Coulombe, Coraline Danieli, Christel Renoux, Susan M Shortreed

Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.

评估个体化治疗规则,特别是在权利审查结果的情况下,是一项挑战,因为感兴趣的治疗效果异质性通常很小,因此很难检测。虽然这促使人们使用非常大的数据集,例如来自多个卫生系统或中心的数据,但参与的数据中心可能会担心数据隐私,因为它们不愿意共享个人层面的数据。在这项关于抑郁症治疗的案例研究中,我们展示了分布式回归在隐私保护方面的应用,该应用与动态加权生存模型(DWSurv)相结合,以估计最佳的个性化治疗规则,同时掩盖个体水平的数据。在模拟中,我们证明了这种方法的灵活性,以解决可能影响混杂的局部治疗实践,并表明DWSurv即使通过(加权)分布式回归方法进行,也保持其双重稳健性。这项工作的动机是,使用英国临床实践研究数据链接对单极性抑郁症的治疗进行分析,并加以说明。
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引用次数: 0
Semi-supervised approach to event time annotation using longitudinal electronic health records. 使用纵向电子健康记录的事件时间注释的半监督方法。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-07-01 DOI: 10.1007/s10985-022-09557-5
Liang Liang, Jue Hou, Hajime Uno, Kelly Cho, Yanyuan Ma, Tianxi Cai

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-n consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.

来自保险索赔和电子健康记录(EHR)系统的大型临床数据集是精准医学研究的宝贵资源。这些数据集可用于开发个性化预测风险或治疗反应的模型。然而,利用真实世界的数据有效地推导预测模型面临着实践和方法上的挑战。关于重要临床结果的精确信息,如癌症进展的时间,在这些数据库中并不容易获得。真实的临床事件时间通常不能根据简单的账单或程序代码的摘录很好地近似。然而,手动标注事件时间既费时又浪费资源。在本文中,我们提出了一种利用多维纵向电子病历记录的两步半监督多模态自动时间注释(MATA)方法。在第一步中,我们采用功能主成分分析方法来估计基于未标记患者的观察点过程的潜在强度函数。在步骤II中,我们将一个惩罚比例赔率模型拟合到事件时间结果中,该模型使用步骤I在标记数据中导出的特征,其中使用b样条近似非参数基线函数。在正则性条件下,得到的特征效应向量估计量为根n一致。我们通过模拟和退伍军人健康管理局肺癌患者EHR队列中肺癌复发注释的真实数据示例,证明了我们的方法相对于现有方法的优越性。
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引用次数: 4
Optimum test planning for heterogeneous inverse Gaussian processes 异构逆高斯过程的最优测试规划
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-06-13 DOI: 10.1007/s10985-022-09556-6
Chien‐Yu Peng, H. Nagatsuka, Ya-Shan Cheng
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引用次数: 1
Estimation and inference of predictive discrimination for survival outcome risk prediction models. 生存结果风险预测模型预测判别的估算和推理。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-04-01 Epub Date: 2022-01-21 DOI: 10.1007/s10985-022-09545-9
Ruosha Li, Jing Ning, Ziding Feng

Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease.

准确的风险预测一直是许多生存结果研究的核心目标。在存在多种风险因素的情况下,可以采用删减回归模型来估计风险预测规则。在将预测工具推广到实际应用之前,对其预测性能进行严格评估至关重要。在我们的示例中,研究人员希望开发并验证一种风险预测工具,通过整合人口信息、疾病特征和吸烟相关数据来识别未来的肺癌病例。考虑到癌症的潜伏期较长,预测工具最好能达到不随时间而减弱的鉴别性能。我们提出了估算和推论程序,以全面评估整体预测辨别力和估算预测规则的时间模式。所提出的方法适用于常用的删减回归模型,包括 Cox 比例危险模型和加速失效时间模型。估计值具有一致性和渐近正态性,同时还开发了可靠的方差估计值。所提出的方法为推断随时间变化的预测判别以及比较候选模型之间的判别性能提供了信息工具。所提方法的应用证明了风险预测工具在 PLCO 研究中的持久性能,以及在肝病研究中检测到的衰减性能。
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引用次数: 0
Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design. 在广义病例队列设计下,采用协变量调整删减权重的竞争风险回归模型。
IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-04-01 Epub Date: 2022-01-15 DOI: 10.1007/s10985-022-09546-8
Yayun Xu, Soyoung Kim, Mei-Jie Zhang, David Couper, Kwang Woo Ahn

A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.

当测量暴露量的成本较高,而事件在整个队列中并不罕见时,就会采用广义的病例队列设计。这种设计从整个队列中随机抽取的一个(分层)子集(称为子队列)和子队列外的一部分病例中收集昂贵的暴露信息。对于具有竞争风险的全队列研究,He 等人(Scand J Stat 43:103-122,2016)研究了具有协变量依赖性删减的非分层比例次分布危险模型,以直接评估协变量对累积发病率函数的影响。本文针对广义病例队列设计下的竞争风险数据,提出了一种具有协变量调整删减权重的分层比例子分布危险模型。我们考虑了权重函数的一般类别,以考虑广义病例队列设计。然后,我们推导出最优权重函数,它能在权重函数的一般类别中使参数估计的渐近方差最小化。结果表明,所提出的估计器具有一致性和渐近正态分布。模拟研究表明:(i) 当普查分布取决于协变量时,建议的具有协变量调整权重的估计器是无偏的;(ii) 建议的具有最优权重函数的估计器提高了参数估计效率。我们将提出的方法应用于干细胞移植和糖尿病数据集。
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引用次数: 0
A calibrated Bayesian method for the stratified proportional hazards model with missing covariates. 缺失协变量分层比例风险模型的校正贝叶斯方法。
IF 1.3 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-04-01 DOI: 10.1007/s10985-021-09542-4
Soyoung Kim, Jae-Kwang Kim, Kwang Woo Ahn

Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion. The inverse probability weighting method is widely used to handle missing covariates. However, obtaining asymptotic variance in frequentist inference is complicated because it involves estimating parameters for propensity scores. In this paper, we propose a new approach based on an approximate Bayesian method without using Taylor expansion to handle missing covariates for survival data. We consider a stratified proportional hazards model so that it can be used for the non-proportional hazards structure. Two cases for missing pattern are studied: a single missing pattern and multiple missing patterns. The proposed estimators are shown to be consistent and asymptotically normal, which matches the frequentist asymptotic properties. Simulation studies show that our proposed estimators are asymptotically unbiased and the credible region obtained from posterior distribution is close to the frequentist confidence interval. The algorithm is straightforward and computationally efficient. We apply the proposed method to a stem cell transplantation data set.

在评估协变量对生存结果的影响时,经常会遇到协变量缺失的情况。从分析中排除缺失的数据可能会导致参数估计有偏差,从而得出误导性的结论。反概率加权法被广泛应用于协变量缺失的处理。然而,在频率推理中获得渐近方差是复杂的,因为它涉及到估计倾向分数的参数。在本文中,我们提出了一种新的方法,基于近似贝叶斯方法,不使用泰勒展开来处理缺失协变量的生存数据。我们考虑了一个分层的比例风险模型,以便它可以用于非比例风险结构。研究了两种缺失模式:单个缺失模式和多个缺失模式。证明了所提估计量是一致的和渐近正态的,这与频域渐近性质相匹配。仿真研究表明,我们提出的估计是渐近无偏的,由后验分布得到的可信区域接近于频率置信区间。该算法简单,计算效率高。我们将提出的方法应用于干细胞移植数据集。
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引用次数: 0
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Lifetime Data Analysis
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