Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial.
Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial.
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DOI：
10.7554/elife.72357
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发表时间：
2022-01-05
期刊：
影响因子：
7.7
通讯作者：
Cole TJ
中科院分区：
文献类型：
--
作者：
Bann D;Wright L;Cole TJ
风险因素或干预措施可能会影响健康结果的变异性和平均值。理解这一点可以帮助病因学的理解和公共卫生翻译，因为改变结果均值和减少变异性的干预措施通常优于那些只影响均值的干预措施。然而，大多数常用的统计工具不测试变异性的差异。到目前为止，流行病学应用很少的工具，试图解释其结果的应用仍然很少。因此，我们提供了一个使用GAMLSS（位置，规模和形状的广义加性模型）进行调查的教程。使用1970年英国出生队列研究，以中年（42-46岁）测量的体重指数（BMI; N = 6007）和心理健康（Warwick-Edinburgh Mental Wellbeing Scale; N = 7104）作为结局。我们使用GAMLSS来调查多种风险因素（性别、儿童社会阶层和中年身体活动不足）与健康结果均值和变异性差异的关系。风险因素与结果变异性的相当大的差异相关-例如，男性的平均BMI略高，但变异性低28%;社会阶层较低和身体不活动均与较高的平均值和较高的变异性相关（分别高6.1%和13.5%）。对于心理健康，性别与平均值无关，而男性的变异性较低（-3.9%）;较低的社会阶层和身体不活动均与较低的平均值相关，但变异性较高（分别高出7.2%和10.9%）。研究结果强调了如何使用GAMLSS来调查风险因素或干预措施如何影响健康结果的变化。这种未充分利用的方法来分析连续分布的结果可能在流行病学，医学和心理科学中具有更广泛的实用性。在线提供了教程和复制语法来帮助实现这一点（https：//osf.io/5tvz6/）。DB由经济和社会研究理事会（资助号ES/M001660/1），医学科学院/惠康信托基金（“2040年公众健康跳板”奖：HOP 001/1025）支持; DB和LW由医学研究理事会（MR/V002147/1）支持。资助者在研究设计、数据收集和分析、出版决定或手稿编写中没有任何作用。
Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape). The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability. Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively). The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/). DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
影响因子：
4.4
作者：
Simanek AM;Meier HCS;D'Aloisio AA;Sandler DP
通讯作者：
Sandler DP