Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event [0.03%]
具有零膨胀和终事件的聚集复发事件的贝叶斯半参数推断
Xinyuan Tian,Maria Ciarleglio,Jiachen Cai et al.
Xinyuan Tian et al.
Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop...
Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies [0.03%]
移动健康研究中一元时间序列缺失数据下的单位根非平稳性检验
Charlotte Fowler,Xiaoxuan Cai,Justin T Baker et al.
Charlotte Fowler et al.
The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, tes...
Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study [0.03%]
基于网络的观察性研究中的双重稳健估计重新审视母教育对青少年学业成绩的影响
Vanessa McNealis,Erica E M Moodie,Nema Dean
Vanessa McNealis
In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their ow...
Arnab Kumar Maity,Sanjib Basu,Santu Ghosh
Arnab Kumar Maity
Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling ba...
Saptarshi Chatterjee,Shrabanti Chowdhury,Sanjib Basu
Saptarshi Chatterjee
The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy m...
A pseudo-response approach to constructing confidence intervals for the subset of patients expected to benefit from a new treatment [0.03%]
一种构造新疗法受益患者亚组置信区间的伪响应方法
Wei Liu,Zhiwei Zhang,Zonghui Hu et al.
Wei Liu et al.
In precision medicine, there is much interest in estimating the expected-to-benefit (EB) subset, i.e. the subset of patients who are expected to benefit from a new treatment based on a collection of baseline characteristics. There are many ...
Variable selection for individualised treatment rules with discrete outcomes [0.03%]
具有离散结果的个体化治疗规则的变量选择
Zeyu Bian,Erica E M Moodie,Susan M Shortreed et al.
Zeyu Bian et al.
An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many vari...
Jarcy Zee,Laura Mariani,Laura Barisoni et al.
Jarcy Zee et al.
Many existing methods for estimating agreement correct for chance agreement by adjusting the observed proportion agreement by the probability of chance agreement based on different assumptions. These assumptions may not always be appropriat...
Ranking tailoring variables for constructing individualized treatment rules: an application to schizophrenia [0.03%]
排名定制变量以制定个性化治疗方案：在精神分裂症中的应用
Jiacheng Wu,Nina Galanter,Susan M Shortreed et al.
Jiacheng Wu et al.
As with many chronic conditions, matching patients with schizophrenia to the best treatment options is difficult. Selecting antipsychotic medication is especially challenging because many of the medications can have burdensome side effects....
Models and methods for analysing clustered recurrent hospitalisations in the presence of COVID-19 effects [0.03%]
考虑COVID-19影响的聚类重复住院分析模型与方法
Xuemei Ding,Kevin He,John D Kalbfleisch
Xuemei Ding
Recurrent events such as hospitalisations are outcomes that can be used to monitor dialysis facilities' quality of care. However, current methods are not adequate to analyse data from many facilities with multiple hospitalisations, especial...