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1.

Objective

To compare the original synthetic control (OSC) method with alternative approaches (Generalized [GSC], Micro [MSC], and Bayesian [BSC] synthetic control methods) and re-evaluate the impact of a significant restructuring of urgent and emergency care in Northeast England, which included the opening of the UK's first purpose-built specialist emergency care hospital.

Data Sources

Simulations and data from Secondary Uses Service data, a single comprehensive repository for patient-level health care data in England.

Study Design

Hospital use of individuals exposed and unexposed to the restructuring is compared. We estimate the impact using OSC, MSC, BSC, and GSC applied at the general practice level. We contrast the estimation methods' performance in a Monte Carlo simulation study.

Data Collection/Extraction Methods

Hospital activity data from Secondary Uses Service for patients aged over 18 years registered at a general practice in England from April 2011 to March 2019.

Principal Findings

None of the methods dominated all simulation scenarios. GSC was generally preferred. In contrast to an earlier evaluation that used OSC, GSC reported a smaller impact of the opening of the hospital on Accident and Emergency (A&E) department (also known as emergency department or casualty) visits and no evidence for any impact on the proportion of A&E patients seen within 4 h.

Conclusions

The simulation study highlights cases where the considered methods may lead to biased estimates in health policy evaluations. GSC was found to be the most reliable method of those considered. Considering more disaggregated data over a longer time span and applying GSC indicates that the specialist emergency care hospitals in Northumbria had less impact on A&E visits and waiting times than suggested by the original evaluation which applied OSC to more aggregated data.  相似文献   
2.
目的探讨现实世界研究(RWS)获得现实世界证据(RWE)的统计学问题。方法对现实世界数据(RWD),RWS的因果推断方法及RWE的评价方式进行总结,分析现存问题。结果获得可靠的RWE用于支持药械监管是RWS的重要目标,适当的RWD通用标准是进行数据质量评价的前提,规范化的数据治理和完善的数据质量评价体系是规范开展RWS的基础,因果推断方法的规范化实施是获得可靠RWE的质量保障,临床上可解释的、有创新性价值的结论是RWE的首要前提,在具体实践中需要客观的RWE判定标准,RWS结果的可验证性是不可忽视的RWE的重要特征。结论单一的临床研究往往无法满足因果关系的判定标准,需要多个研究从不同角度加以验证,RWS与随机对照试验(RCT)互为验证的因果推断研发模式值得推广。  相似文献   
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摘要 目的:提出了一种基于运动姿态与肌电融合的脑卒中患者上肢运动功能实时评估方法。 方法:选取一定数量处于不同Brunnstrom分期的脑卒中患者。首先运用姿态与肌电检测传感器,分别采集康复动作评估过程中不同分期患者上肢运动姿态与表面肌电数据;其次分别提取患肢运动姿态与表面肌电数据特征,在融合姿态与肌电特征信息基础上获取患者上肢运动功能评估数据集;最后,选取具有较好实时分类性能的模糊支持向量机作为分类器,运用评估数据集测试上肢运动功能评估效果。 结果:基于模糊支持向量机构建的运动功能分类器经过离线训练后,姿态与肌电融合的分类器对脑卒中患者上肢运动功能实时评估准确率平均达到83%,且较单纯基于运动姿态(75%)或肌电(74%),具有更好的评估效果。 结论:姿态与肌电融合的上肢运动功能实时评估方法,能较准确地对脑卒中患者上肢运动功能进行实时Brunstrom分期。  相似文献   
5.
This paper introduces a test of superiority of new anti-infective drug B over comparator drug A based on a randomized clinical trial. This test can be used to demonstrate assay (trial) sensitivity for noninferiority trials and rigorously tailor drug choice for individual patients. Our approach uses specialized baseline covariates XA,XB, which should predict the benefits of drug A and drug B, respectively. Using a response surface model for the treatment effect, we test for superiority at the (XA,XB) point that is most likely to show superiority. We identify this point based on estimates from a novel half-blind pseudo likelihood, where we augment a blinded likelihood (mixed over the treatment indicator) with likelihoods for the overall success rates for drug A and drug B (mixed over XA,XB). The augmentation results in much better estimates than those based on the mixed blinded likelihood alone but, interestingly, the estimates almost behave as if they were based on fully blinded data. We also develop an analogous univariate method using XA for settings where XB has little variation. Permutation methods are used for testing. If the “half-blind” test rejects, pointwise confidence interval can be used to identify patients who would benefit from drug B. We compare the new tests to other methods with an example and via simulations.  相似文献   
6.
The weighted average treatment effect is a causal measure for the comparison of interventions in a specific target population, which may be different from the population where data are sampled from. For instance, when the goal is to introduce a new treatment to a target population, the question is what efficacy (or effectiveness) can be gained by switching patients from a standard of care (control) to this new treatment, for which the average treatment effect for the control estimand can be applied. In this paper, we propose two estimators based on augmented inverse probability weighting to estimate the weighted average treatment effect for a well-defined target population (ie, there exists a predefined target function of covariates that characterizes the population of interest, for example, a function of age to focus on elderly diabetic patients using samples from the US population). The first proposed estimator is doubly robust if the target function is known or can be correctly specified. The second proposed estimator is doubly robust if the target function has a linear dependence on the propensity score, which can be used to estimate the average treatment effect for the treated and the average treatment effect for the control. We demonstrate the properties of the proposed estimators through theoretical proof and simulation studies. We also apply our proposed methods in a comparison of glucagon-like peptide-1 receptor agonists therapy and insulin therapy among patients with type 2 diabetes, using the UK Clinical Practice Research Datalink data.  相似文献   
7.
The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.  相似文献   
8.
BackgroundAutoimmune (Hashimoto’s thyroiditis) is characterized by a strong female preponderance, which may suggest that sex hormones have an impact on thyroid autoimmunity. The aim of this study was to investigate whether testosterone determines vitamin D action on thyroid antibody titers and thyroid function tests in men with autoimmune thyroiditis and low testosterone levels.MethodsThe study included 36 men with testosterone deficiency, 17 of whom had been treated for at least 26 weeks with oral testosterone undecanoate (120 mg daily). Because of coexistent euthyroid Hashimoto’s thyroiditis, all participants were then treated with vitamin D (100 μg daily). Serum titers of thyroid peroxidase and thyroglobulin antibodies, serum levels of thyrotropin, free thyroid hormones, testosterone and 25-hydroxyvitamin D, as well as Jostel’s thyrotropin index, SPINA-GT and SPINA-GD were assessed before vitamin D treatment and 26 weeks later.ResultsWith the exception of testosterone levels, there were no significant differences between both study groups in serum hormone levels, antibody titers and thyroid function tests. All participants completed the study. In addition to increasing 25-hydroxyvitamin D levels, vitamin D increased SPINA-GT and reduced thyroid peroxidase and thyroglobulin antibody titers. In testosterone-treated men, vitamin D increased testosterone levels. Vitamin D did not affect serum levels of thyrotropin, free thyroid hormones, Jostel’s thyrotropin index and SPINA-GD. Treatment-induced changes in thyroid antibody titers and SPINA-GT were more pronounced in testosterone-treated than testosterone-naïve men.ConclusionsThe obtained results suggest that the beneficial effect on thyroid autoimmunity and thyroid secretory function is stronger in men receiving testosterone therapy.  相似文献   
9.
The treatment effect in subgroups of patients is often of interest in randomized controlled clinical trials, as this may provide useful information on how to treat which patients best. When a specific subgroup is characterized by the absence of certain events that happen postrandomization, a naive analysis on the subset of patients without these events may be misleading. The principal stratification framework allows one to define an appropriate causal estimand in such settings. Statistical inference for the principal stratum estimand hinges on scientifically justified assumptions, which can be included with Bayesian methods through prior distributions. Our motivating example is a large randomized placebo-controlled trial of siponimod in patients with secondary progressive multiple sclerosis. The primary objective of this trial was to demonstrate the efficacy of siponimod relative to placebo in delaying disability progression for the whole study population. However, the treatment effect in the subgroup of patients who would not relapse during the trial is relevant from both a scientific and patient perspective. Assessing this subgroup treatment effect is challenging as there is strong evidence that siponimod reduces relapses. We describe in detail the scientific question of interest, the principal stratum estimand, the corresponding analysis method for binary endpoints, and sensitivity analyses. Although our work is motivated by a randomized clinical trial, the approach has broader appeal and could be adapted for observational studies.  相似文献   
10.
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