Endoscopy-Related Bleeding and Thromboembolic Events in Patients on Direct Oral Anticoagulants or Vitamin K Antagonists

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Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Tumour volume distribution can yield information on tumour growth and tumour control

BackgroundIt is shown that tumour volume distributions can yield information on two aspects of cancer research: tumour induction and tumour control.Materials and methodsFrom the hypothesis that the intrinsic distribution of breast cancer volumes follows an exponential distribution, firstly the probability density function of tumour growth time was deduced via a mathematical transformation of the probability density functions of tumour volumes. In a second step, the distribution of tumour volumes was used to model the variation of the clonogenic cell number between patients in order to determine tumour control probabilities for radiotherapy patients.ResultsDistribution of lag times, i.e. the time from the appearance of the first fully malignant cell until a clinically observable cancer, can be used to deduce the probability of tumour induction as a function of patient age. The integration of the volume variation with a Poisson-TCP model results in a logistic function which explains population-averaged survival data of radiotherapy patients.ConclusionsThe inclusion of tumour volume distributions into the TCP formalism enables a direct link to be deduced between a cohort TCP model (logistic) and a TCP model for individual patients (Poisson). The TCP model can be applied to non-uniform tumour dose distributions.     

基于自动勾画和快速蒙特卡罗计算的放射治疗全身器官剂量数据库平台的可行性研究

目的:探讨建立一种放射治疗全身器官剂量数据库平台的可行性。方法:使用基于深度学习的自动勾画软件DeepViewer?1例食管癌患者的全身CT上勾画全身器官，然后利用基于GPU加速的蒙特卡罗软件ARCHER计算相应的器官剂量分布，最后利用Lyman-Kutcher-Burman（LKB）模型评估放疗患者正常组织并发症概率（NTCP）。结果:针对该病例，成功建立基于DeepViewer?ARCHER和LKB模型的全身器官剂量数据库，发现距离靶区越近的器官剂量越大，其中心脏与靶区间距离最小，剂量为14.11 Gy，但因其模型参数特殊，通过LKB模型计算的NTCP为0.00%；左、右肺的剂量分别为3.19和1.16 Gy，但是NTCP值却很大，分别为2.13%和1.60%。对于距离靶区较远的头颈部器官（视交叉、视神经和眼）和腹部器官（直肠、膀胱和股骨头）剂量分别约为9和2 mGy，并且NTCP均近似为0.00%。结论:研究结果证明通过自动勾画软件DeepViewer?蒙特卡罗软件ARCHER和LKB模型建立全身器官剂量数据库的可行性。     

A simulation study of three sequential methods for the comparison of two treatment groups when the response criterion is censored

Three recent sequential methods, group sequential analysis (GSA), the sequential probability ratio test (SPRT) and the triangular test (TT) are well suited to randomized clinical trials with a censored response criterion, as they do not require matched pairs of patients. We undertook a simulation study to investigate their statistical properties and to compare these three methods with the fixed-sample design. Our results suggest that the three methods have the expected statistical properties for size and power; they allow an important reduction of the average number of events before stopping, except with GSA when there is no treatment difference; the triangular test (closed design) appears the optimal design, as the variance of the number of events is smaller than with the sequential probability ratio test (open design) and analysis after every twenty new events does not alter the statistical properties of these sequential methods and enhances their usefulness.     

Laboratory screening method for selection of healthy volunteers

The aim of laboratory screening in Phase I is to exclude subjects with subclinical illness, who might be at increased risk in the study, and who might also adversely influence interpretation of the results. A new method for laboratory screening, based on Bayesian probability theory, is proposed, which consists of: 1. Drawing up a list of diseases to be excluded. 2. Defining for each disease, the maximum acceptable risk that an included subject could be affected by it. 3. Identifying one test for each disease. 4. Using a contingency table to calculate the specificity of the test and integrating the estimated prevalence of the disease from epidemiological data. 5. Applying the percentage obtained by the calculation of specificity to the previously determined distribution of values in the volunteer population to identify the threshold value for inclusion. Use of this deductive method in screening volunteers for Phase I trials affords increased security of selection, while reducing the number of non-pertinent exclusions because of laboratory findings.     

Decision making in diagnostic radiology

Radiology can be more interesting if the principles of decision making are understood and used to the advantage of the radiologist, referring clinician and patient. This article seeks to revise the principles that radiologists use intuitively. Particular emphasis is placed on the importance of the pre-test probability, as it can be shown to: (i) vary with the referral pool of patients; (ii) be a major determinant of the amount of additional information gained from the test; (iii) alter the sensitivity and specificity of the test; and (iv) determine largely the significance of a positive or negative test result. An investigation helps a clinician by providing information that will move the probability of disease above an action threshold or below its exclusion threshold. If this does not occur as a result of one study, another is selected. This article also describes the factors involved in such a decision tree analysis, as well as discussing the reasons for both selecting a particular examination and deciding when a test should not be performed.