A curvilinear bivariate random changepoint model to assess temporal order of markers [0.03%]
一种用于评估标志物时间顺序的二次曲线双变量随机变点模型
Corentin Segalas,Catherine Helmer,Hélène Jacqmin-Gadda
Corentin Segalas
In biomedical research, various longitudinal markers measuring different quantities are often collected over time. For example, repeated measures of psychometric scores are very informative about the degradation process toward dementia. The...
Is the R coefficient of interest in cluster randomized trials with a binary outcome? [0.03%]
分组随机试验中二分类结果的R系数是否有意义？
Ariane M Mbekwe Yepnang,Agnès Caille,Sandra M Eldridge et al.
Ariane M Mbekwe Yepnang et al.
In cluster randomized trials, the intraclass correlation coefficient (ICC) is classically used to measure clustering. When the outcome is binary, the ICC is known to be associated with the prevalence of the outcome. This association challen...
Variable selection and estimation in causal inference using Bayesian spike and slab priors [0.03%]
基于Bayesian spike and slab先验的因果推断中的变量选择与估计方法研究
Brandon Koch,David M Vock,Julian Wolfson et al.
Brandon Koch et al.
Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Standard variable selection techniques aim to maximize predictive abi...
Bayesian cure fraction models with measurement error in the scale mixture of normal distribution [0.03%]
基于尺度混合正态分布测量误差的贝叶斯治愈分数模型
Anna R S Marinho,Rosangela H Loschi
Anna R S Marinho
Cure fraction models have been widely used to model time-to-event data when part of the individuals survives long-term after disease and are considered cured. Most cure fraction models neglect the measurement error that some covariates may ...
V Vancak,Y Goldberg,S Z Levine
V Vancak
The number needed to treat is often used to measure the efficacy of a binary outcome in randomized clinical trials. There are three different available measures of the number needed to treat. Two of these measures, Furukawa and Leucht's and...
Estimating the age at onset distribution of the asymptomatic stage of a genetic disease based on pedigree data [0.03%]
基于谱系数据估计遗传病无症状期的发病年龄分布
Marianne A Jonker,Priya Vart,Mar Rodriguez Girondo
Marianne A Jonker
Information on the age at onset distribution of the asymptomatic stage of a disease can be of paramount importance in early detection and timely management of that disease. However, accurately estimating this distribution is challenging, be...
Semiparametric isotonic regression analysis for risk assessment under nested case-control and case-cohort designs [0.03%]
嵌套病例对照和病例队列设计下的半参数单调回归风险评估分析方法
Wen Li,Ruosha Li,Ziding Feng et al.
Wen Li et al.
Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric mode...
Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula [0.03%]
假设联合编下的边际危险模型与竞争风险的亚分布危险模型的比较
Takeshi Emura,Jia-Han Shih,Il Do Ha et al.
Takeshi Emura et al.
For the analysis of competing risks data, three different types of hazard functions have been considered in the literature, namely the cause-specific hazard, the sub-distribution hazard, and the marginal hazard function. Accordingly, medica...
A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis [0.03%]
生存分析的灵活参数模型：二元幂广义威布尔分布
M C Jones,Angela Noufaily,Kevin Burke
M C Jones
We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the 'power generalized Weibull' distribution as an attractive vehicle for univariate parametric survival...
Flexible modeling of ratio outcomes in clinical and epidemiological research [0.03%]
临床和流行病学研究中比率结果的灵活建模方法
Moritz Berger,Matthias Schmid
Moritz Berger
In medical studies one frequently encounters ratio outcomes. For modeling these right-skewed positive variables, two approaches are in common use. The first one assumes that the outcome follows a normal distribution after transformation (e....