Joint Estimation of Incidence and Diagnostic Error Rates from Irregular Longitudinal Data
不规则纵向数据中发生率与诊断错误率的联合估计AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要聚焦临床纵向状态进展与误诊建模,未明确研究所列面板计量方法或其他对应方向。
不规则纵向数据中发生率与诊断错误率的联合估计AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Longitudinal studies often involve the repeated diagnosis across time of each patient's status with respect to a progressive categorical process. When the occurrence of a change in status is not readily apparent, two factors can make modeling and assessing the incidence rates of progression difficult. First, because diagnoses may be difficult, they may not be performed with the frequency necessary to pinpoint exact times of incidence. Second, uncertainty in the diagnostic process can obscure identification of the time interval in which incidence occurs. When serial diagnoses are fallible, even small error rates can seriously disrupt interpretation and make using the aforementioned methods difficult or impossible. For example, if false diagnoses (both false positives and negatives) occur independently with probability .05 in a longitudinal study involving four serial diagnoses, 19% of the strings of serial diagnoses would be expected to contain at least one error. If the underlying process is progressive, many of these errors would be noticeable: At face value, some patterns of diagnoses would describe regressions. Errors yielding patterns of diagnoses that are progressive would not be detectable. Simply omitting any subjects with inconsistent patterns from the analysis introduces bias. Another possible approach, using the first reported incidence of progression, also introduces bias (Schlesselman 1977). To analyze clinical data on the diagnosis of sexual maturation among subjects with sickle-cell disease, models are developed for jointly parameterizing incidence and error rates. An EM algorithm is presented that allows tractable maximum likelihood estimation even when the times of diagnoses are irregular and vary among subjects. Likelihood ratio tests are used to assess relationships between categorical covariates and both incidence and error rates. Data from the Cooperative Study of Sickle Cell Disease are analyzed to describe the age distribution for the onset of puberty (according to the Tanner stage index) among homozygous (SS) males. Clear delays in maturation are apparent among SS males. Diagnostic error rates for Tanner staging appear to vary with the subject's age. False-positive diagnoses appear to be more common than false-negative diagnoses.
网络模型中基于成对差分的分布回归方法
I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains…
序贯多次随机分配试验中评估动态治疗方案的纵向—生存联合框架
Sequential multiple assignment randomized trials (SMARTs) provide a systematic framework for constructing and evaluating dynamic treatment regimens (DTRs). In clinical studies, longitudinal biomarkers are routinely collected to monitor disease progression and define treatment response. However, the integration of longitudinal biomarker data into survival analysis for DTR evaluation within a SMART remains unexplored. We propose a joint longitudinal-survival framework to…
fixest:快速且功能丰富的 R 计量估计框架
fixest is an R package for fast and flexible econometric estimation. It provides a unified framework for applied research, with comprehensive support for a diverse class of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid…
基于复合的正则化回归:多流分布模型的可识别性、结构化惩罚与统计保证
Regression by composition provides a flexible framework for constructing conditional distributions through sequential group actions. However, when multiple flows act on the same distribution, the model becomes non-identifiable, leading to flat likelihood regions and unstable estimates. We introduce a structured regularization framework that resolves this issue by assigning flow-specific penalties. The resulting estimator is defined as a penalized maximum likelihood problem…
区域组成数据的时空自回归模型
Compositional data, such as regional shares of economic sectors or property transactions, are central to understanding structural change in economic systems across space and time. This paper introduces a spatiotemporal multivariate autoregressive model tailored for panel data with composition-valued responses at each areal unit and time point. The proposed framework enables the joint modelling of temporal dynamics and spatial dependence under compositional constraints, and…
固定效应与任意初始条件下的面板 AR(1) 修正极大似然估计再研究
In this paper we consider two generalizations of Lancaster's (Review of Economic Studies, 2002) Modified Maximum Likelihood estimator (MMLE) for the panel AR(1) model with fixed effects, arbitrary initial conditions, and strictly exogenous covariates when the time dimension of the panel, T, is fixed. When the autoregressive parameter rho=1, the limiting modified profile log-likelihood function for this model has a stationary point of inflection, and rho is first-order…