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受约束分类中的生物标志物线性组合AI 解读

Multiple biomarkers are often combined to improve disease diagnosis. The uniformly optimal combination, i.e., with respect to all reasonable performance metrics, unfortunately requires excessive distributional modeling, to which the estimation can be sensitive. An alternative strategy is rather to pursue local optimality with respect to a specific performance metric. Nevertheless, existing methods may not target clinical utility of the intended medical test, which usually…

动态回归中单调性约束的恢复AI 解读

Dynamic regression models, including the quantile regression model and Aalen's additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this article, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and…

计数过程的广义分位数回归及其重复事件应用AI 解读

In survival analysis, quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest. In this paper, we discuss how quantile regression can be extended to model counting processes, and thus lead to a broader regression framework for survival data. We specifically investigate the proposed modeling of counting processes for recurrent events data. We show that the new recurrent events model retains the…

分位数微积分与删失回归AI 解读

Quantile regression has been advocated in survival analysis to assess evolving covariate effects. However, challenges arise when the censoring time is not always observed and may be covariate-dependent, particularly in the presence of continuously-distributed covariates. In spite of several recent advances, existing methods either involve algorithmic complications or impose a probability grid. The former leads to difficulties in the implementation and asymptotics, whereas…

使用分位数回归模型进行生存分析AI 解读

Quantile regression offers great flexibility in assessing covariate effects on event times, thereby attracting considerable interests in its applications in survival analysis. But currently available methods often require stringent assumptions or complex algorithms. In this article we develop a new quantile regression approach for survival data subject to conditionally independent censoring. The proposed martingale-based estimating equations naturally lead to a simple…

标记边际分布的半参数估计AI 解读

In many applications, the outcome of interest is a mark such that its observation is contingent upon occurrence of an event. With incomplete follow-up data, the marginal mark distribution is, however, nonparametrically nowhere identifiable in many practical situations. To address this problem, we suggest a semiparametric model that postulates a normal copula for the association between the mark and survival time, but leaves the marginals unspecified. We show identifiability…

终止事件发生时的复发事件频次AI 解读

Recurrent events arise in many longitudinal medical studies where time to a terminal event or failure is the primary endpoint. With incomplete follow-up data, the analysis of recurrent events is a challenge owing to their association with the failure. One specific quantity of interest rarely addressed in the statistical literature is the recurrence frequency at the failure time; an example is hospitalization frequency, which is often used as a rough measure of lifetime…

删失终生医疗费用的校准回归AI 解读

Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates;…

多状态加速停留时间模型的删失回归AI 解读

Summary Many disease processes are characterized by two or more successive health states, and it is often of interest and importance to assess state-specific covariate effects. However, with incomplete follow-up data such inference has not been satisfactorily addressed in the literature. We model the logarithm-transformed sojourn time in each state as linearly related to the covariates; however, neither the distributional form of the error term nor the dependence structure…

协变量含测量误差的逻辑回归一致性函数方法AI 解读

We propose consistent functional methods for logistic regression in which some covariates are not accurately ascertainable. Among existing methods for generalized linear models, the conditional-score approach to normal errors does not guarantee the convergence of its estimators, and the corrected-score method generally is not applicable to the logistic-regression score function. In this article, after constructing a correction-amenable estimation procedure with the true…

准确协变量无法获得时的 Cox 回归：非参数校正方法AI 解读

Many survival studies involve covariates that are not accurately ascertainable; CD4 lymphocyte count in HIV/AIDS research is a typical example for which the gold standard of the measurement is not available. This article proposes a consistent estimation procedure for Cox regression under the additive measurement error model. Distinct from existing methods, the proposed estimation for regression coefficients does not require any additional assumptions. We establish the…

两样本多状态加速停留时间模型AI 解读

Many medical studies involve observations of patients experiencing multiple consecutive states during their follow-up. Since treatments may have differential effects on these states, comparison is of interest with respect to individual sojourn times. In this article I generalize the univariate accelerated failure time model for multistate processes and model the treatment effects as state-specific time scale changes. I propose a two-sample inference procedure that…

两样本多状态加速停留时间模型AI 解读

Many medical studies involve observations of patients experiencing multiple consecutive states during their follow-up. Since treatments may have differential effects on these states, comparison is of interest with respect to individual sojourn times. In this article I generalize the univariate accelerated failure time model for multistate processes and model the treatment effects as state-specific time scale changes. I propose a two-sample inference procedure that…