Determination of the optimal sample size for a clinical trial accounting for the population size [0.03%]
考虑总体规模的临床试验最优样本量的确定方法研究
Nigel Stallard,Frank Miller,Simon Day et al.
Nigel Stallard et al.
The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be inf...
Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction [0.03%]
带有治愈率的乳腺癌生存数据的分半参数贝叶斯量效函数估计
Cherry Gupta,Juliana Cobre,Adriano Polpo et al.
Cherry Gupta et al.
Existing cure-rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. This paper proposes a novel class of cure-rate model, the transform-both-sides ...
An approximate likelihood estimator for the prevalence of infections in vectors using pools of varying sizes [0.03%]
利用不同大小样本估计媒介感染率的近似值方法
James D Santos,Diana Dorgam
James D Santos
There are several arthropods that can transmit disease to humans. To make inferences about the rate of infection of these arthropods, it is common to collect a large sample of vectors, divide them into groups (called pools), and apply a tes...
Time-dependent classification accuracy curve under marker-dependent sampling [0.03%]
基于标志物的抽样下的时间依赖分类准确度曲线
Zhaoyin Zhu,Xiaofei Wang,Paramita Saha-Chaudhuri et al.
Zhaoyin Zhu et al.
Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or...
Ensemble survival trees for identifying subpopulations in personalized medicine [0.03%]
用于个性化医学中识别亚种群的集成生存决策树方法
Yu-Chuan Chen,James J Chen
Yu-Chuan Chen
Recently, personalized medicine has received great attention to improve safety and effectiveness in drug development. Personalized medicine aims to provide medical treatment that is tailored to the patient's characteristics such as genomic ...
Functional exploratory data analysis for high-resolution measurements of urban particulate matter [0.03%]
高分辨率城市颗粒物测量的函数探索性数据分析
M Giovanna Ranalli,Giorgia Rocco,Giovanna Jona Lasinio et al.
M Giovanna Ranalli et al.
In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugi...
Modeling continuous covariates with a "spike" at zero: Bivariate approaches [0.03%]
在零点有“尖峰”的连续协变量建模：双变量方法
Carolin Jenkner,Eva Lorenz,Heiko Becher et al.
Carolin Jenkner et al.
In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examp...
Bounds on the average causal effects in randomized trials with noncompliance by covariate adjustment [0.03%]
协变量调整在随机临床试验中依从性偏差条件下的平均因果效应的界估计
Na Shan,Ping-Feng Xu
Na Shan
In randomized trials with noncompliance, causal effects cannot be identified without strong assumptions. Therefore, several authors have considered bounds on the causal effects. Applying an idea of VanderWeele (), Chiba () gave bounds on th...
The predictive distribution of the residual variability in the linear-fixed effects model for clinical cross-over trials [0.03%]
临床交叉试验中线性固定效应模型残差变异性的预测分布
Anja Bertsche,Gerhard Nehmiz,Jan Beyersmann et al.
Anja Bertsche et al.
In the linear model for cross-over trials, with fixed subject effects and normal i.i.d. random errors, the residual variability corresponds to the intraindividual variability. While population variances are in general unknown, an estimate c...
Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models [0.03%]
基于Bootstrap的多变量回归模型中许多类别的分类变量被优先选择
Susanne Rospleszcz,Silke Janitza,Anne-Laure Boulesteix
Susanne Rospleszcz
Automated variable selection procedures, such as backward elimination, are commonly employed to perform model selection in the context of multivariable regression. The stability of such procedures can be investigated using a bootstrap-based...