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学习最优个体化治疗规则的稳健协变量平衡方法AI 解读

Abstract One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, for which an outcome regression model or a propensity score model usually needs to be assumed with most existing statistical methods.…

聚类干扰下随机政策效应的高效非参数估计AI 解读

Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, i.e., there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment…

学习最优动态治疗方案时控制累积不良风险AI 解读

Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward…

刻画精神障碍决策行为的半参数逆强化学习方法AI 解读

Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to…

随机试验与真实世界数据的弹性整合：处理效应异质性估计AI 解读

Abstract We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based…

多变量纵向与生存数据的半参数潜在类别模型AI 解读

In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood…

通过降维构建简约的个性化剂量探索模型AI 解读

Summary Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to an optimization on a lower-dimensional subspace of the covariates. We exploit the fact that the individualized dose rule can be…

针对多领域潜在结局的个体化治疗规则学习AI 解读

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning…

利用电子健康记录优化个体化治疗策略的匹配学习AI 解读

Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In…

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring…

分量分别删失的复合终点半参数回归分析AI 解读

Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression-free survival is a widely used composite endpoint, with disease progression and death as the two components. Progression-free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the…

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.

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…

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…

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…

重复测量与生存时间联合分析中极大似然估计的渐近结果AI 解读

Maximum likelihood estimation has been extensively used in the joint analysis of repeated measurements and survival time. However, there is a lack of theoretical justification of the asymptotic properties for the maximum likelihood estimators. This paper intends to fill this gap. Specifically, we prove the consistency of the maximum likelihood estimators and derive their asymptotic distributions. The maximum likelihood estimators are shown to be semiparametrically efficient.

相依删失下边际比例风险回归的似然方法AI 解读

In many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by…