Bayesian

贝叶斯
  • 文章类型: Journal Article
    虽然人们普遍认为体育锻炼是健康生活方式的重要组成部分,目前尚不清楚有多少人遵守关于体育活动的公共卫生建议。身体活动指南(PAG)由CDC发布,为美国成年人提供指导方针,但是很难评估这些指南的遵守情况。PAG通过建议活动在至少10分钟的发作中发生而进一步使依从性评估复杂化。为了更好地了解各种仪器量化活动的测量能力,并提出一种评估与PAG相关的活动的方法,爱荷华州立大学的研究人员对爱荷华州四个不同县的1000多名参与者进行了身体活动测量调查(PAMS)。在本文中,我们开发了一个由两部分组成的贝叶斯测量误差模型,并将其应用于PAMS数据,以评估爱荷华州成年人群对PAG的依从性.该模型准确地说明了PAG中提出的10分钟回合要求。测量误差模型纠正了有偏差的估计,并考虑了活动的日常变化。该模型也适用于具有全国代表性的国家健康和营养检查调查。
    While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provides guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicates adherence assessment by recommending activity to occur in at least 10 min bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance with the PAG in the Iowa adult population. The model accurately accounts for the 10 min bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day-to-day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey.
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  • 文章类型: Journal Article
    In the context of an increasing number of publications of trial data analysed by Bayesian methods, clinicians need support to better understand Bayesian statistical methods. The existing checklists are intended for people who already know these methods. We aimed to establish and validate a checklist that contains a group of items considered crucial in interpreting the results of a phase III RCT analysed with Bayesian methods.
    A team of biostatisticians created a checklist of previously reported items and additional items identified from a literature review. Using three different articles in three rounds, the items were then validated by residents in anaesthesiology with no skills in statistics.
    Based on an initial item list, three rounds led to a consensus checklist. Eleven items were considered important information to be specified for understanding the validity of the results. Of these, three were considered essential: specification of the prior, source of the prior (when prior is informative), and the effect size point estimate with its credible interval.
    The checklist can help clinicians interpret the results of a phase III randomised clinical trial analysed by Bayesian methods, even clinicians with no particular knowledge of statistics, to ensure that the major elements of the statistical section are present and valid. Care should be taken in interpreting the results of a trial analysed by Bayesian methods that are not reported with these three essential items because the validity of the results cannot be established.
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