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两阶段抽样下的高效半参数推断及其遗传关联研究应用AI 解读

In modern epidemiological and clinical studies, the covariates of interest may involve genome sequencing, biomarker assay, or medical imaging and thus are prohibitively expensive to measure on a large number of subjects. A cost-effective solution is the two-phase design, under which the outcome and inexpensive covariates are observed for all subjects during the first phase and that information is used to select subjects for measurements of expensive covariates during the…

多元区间删失数据半参数回归模型的最大似然估计AI 解读

Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring…

失效时间结局病例队列研究中的变量选择AI 解读

Case-cohort designs are widely used in large cohort studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large, so an efficient variable selection method is necessary. In this paper, we study the properties of a variable selection procedure using the smoothly clipped absolute deviation penalty in a case-cohort design with a diverging number of parameters. We establish the consistency and asymptotic normality…

评论：多阶段化疗方案的 Q-learning 与 O-learningAI 解读

Xu, Müller, Wahed, and Thall proposed a Bayesian model to analyze an acute leukemia study involving multi-stage chemotherapy regimes. We discuss two alternative methods, Q-learning and O-learning, to solve the same problem from the machine learning point of view. The numerical studies show that these methods can be flexible and have advantages in some situations to handle treatment heterogeneity while being robust to model misspecification.

区间删失数据半参数变换模型的最大似然估计AI 解读

Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric…

In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future…

稀疏异步纵向数据的回归分析AI 解读

Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or…

删失数据下非参数遗传风险函数的有效估计AI 解读

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals' mutation carrier or non-carrier…

稀疏纵向协变量下的比例风险模型分析AI 解读

Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates which are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full likelihood analyses of joint models for longitudinal and survival data impose stringent modelling assumptions which are difficult to verify in practice and which are complicated both…

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling,…

估计最优动态治疗方案的新统计学习方法AI 解读

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert…

用于年龄依赖分类的针对性局部支持向量机AI 解读

We develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions,…

两阶段队列研究中半参数变换模型的有效估计AI 解读

Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on observed event times, event indicators, and inexpensive covariates is collected in the first phase, and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase; inexpensive covariates are also used in the data analysis to control for confounding and to evaluate interactions. This paper provides efficient estimation…

连续结局两阶段概率依赖抽样的半参数推断AI 解读

Summary Multiphased designs and biased sampling designs are two of the well-recognized approaches to enhance study efficiency. We propose a new and cost-effective sampling design, the two-phase probability-dependent sampling design, for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take…

缺少类别标识时的辅助标记分类AI 解读

Constructing classification rules for accurate diagnosis of a disorder is an important goal in medical practice. In many clinical applications, there is no clinically significant anatomical or physiological deviation exists to identify the gold standard disease status to inform development of classification algorithms. Despite absence of perfect disease class identifiers, there are usually one or more disease-informative auxiliary markers along with feature variables…

使用结局加权学习估计个体化治疗规则AI 解读

There is increasing interest in discovering individualized treatment rules for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal individualized treatment rule which is a deterministic function of patient specific characteristics maximizing expected clinical outcome. In this paper, we first show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional…

删失数据的幂变换线性分位数回归AI 解读

We propose a class of power-transformed linear quantile regression models for survival data subject to random censoring. The estimation procedure follows two sequential steps. First, for a given transformation parameter, we can easily obtain the estimates for the regression coefficients by minimizing a well-defined convex objective function. Second, we can estimate the transformation parameter based on a model discrepancy measure by constructing cumulative sum processes. We…

具有变系数的部分线性加性风险回归AI 解读

Please see the supplementary materials for a correction to this article.To explore the nonlinear interactions between some covariates and an exposure variable, we propose the partially linear additive hazards model for survival data. In a semiparametric setting, we construct a local pseudoscore function to estimate the varying and constant coefficients and establish the asymptotic normality of the proposed estimators. Moreover, we develop the weak convergence property for…

半参数回归模型中的惩罚估计函数与变量选择AI 解读

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the…

协变量随机缺失回归模型的筛极大似然估计AI 解读

Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of the regression coefficients. These estimators are shown to be consistent and asymptotically normal with their asymptotic covariance matrix that achieves the semiparametric efficiency…

加速失效时间模型的有效估计AI 解读

The accelerated failure time model provides a natural formulation of the effects of covariates on potentially censored response variable. The existing semiparametric estimators are computationally intractable and statistically inefficient. In this article we propose an approximate nonparametric maximum likelihood method for the accelerated failure time model with possibly time-dependent covariates. We estimate the regression parameters by maximizing a kernel-smoothed profile…

重复事件的含随机效应半参数变换模型AI 解读

In this article we study a class of semiparametric transformation models with random effects for the intensity function of the counting process. These models provide considerable flexibility in formulating the effects of possibly time-dependent covariates on the developments of recurrent events while accounting for the dependence of the recurrent event times within the same subject. We show that the nonparametric maximum likelihood estimators (NPMLEs) for the parameters of…

计数过程半参数变换模型的有效估计AI 解读

A class of semiparametric transformation models is proposed to characterise the effects of possibly time-varying covariates on the intensity functions of counting processes. The class includes the proportional intensity model and linear transformation models as special cases. Nonparametric maximum likelihood estimators are developed for the regression parameters and cumulative intensity functions of these models based on censored data. The estimators are shown to be…

含治愈比例的生存数据半参数变换模型AI 解读

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and…