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Strengthening causal inference in antisocial behaviour research

Strengthening causal inference in antisocial behaviour research
加强反社会行为研究中的因果推理
批准号:
2083187
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
翻译
第一年(2018):旋转第二年(2019):博士项目:加强反社会行为研究中的因果推断:使用创新的统计方法和纵向数据集阐明环境风险因素的作用,包括行为问题(CP)和相关的反社会行为,是英国儿童和青少年心理健康服务转介的最常见原因。然而,推测的危险因素与脑瘫的发展之间的因果关系,而不是相互关系,还没有得到彻底的研究。为了设计有效的CP干预措施,必须检查和发展病因学理论,确定可改变的因果风险因素,并了解这些因素何时以及对谁增加了CP的可能性和严重性。拟议的PHD项目将通过以下方式解决现有文献中的空白:1)总结目前关于CP风险因素的最严格证据;2)使用创新的统计方法和大型数据集,确定先前确定的心理和生物风险因素是否对CP的发展产生因果影响;3)确定这些因果风险因素可能如何受到其他因素的影响。由于建议的方法(特别是G方法、孟德尔随机化和双研究分析)依赖于不同的假设和设计,它们将允许对证据进行三角测量,并产生比已发表研究报告更可靠的结果和可靠的结论。因此,预计拟议的项目将大大加深我们对CP发展的了解。
英文摘要
YEAR 1 (2018): RotationsYEAR 2 (2019): PhD Project: Strengthening causal inference in antisocial behaviour research: Elucidating the role of environmental risk factors using innovative statistical methods and longitudinal datasetsConduct problems (CP), and associated antisocial behaviour, are the most common reason for referral to Child and Adolescent Mental Health Services in the UK. However, the causal, as opposed to correlational, relationship between putative risk factors and the development of CP has not been thoroughly investigated. In order to design effective interventions for CP, it is essential to examine and develop etiological theories, identify modifiable causal risk factors and understand when and for whom these factors increase the likelihood and severity of CP.The proposed PhD project will address gaps in the existing literature by 1) summarising the most stringent evidence currently available on risk factors for CP; 2) determining, using innovative statistical methods and large datasets, whether previously identified psychosocial and biological risk factors causally influence the development of CP; 3) establishing how these causal risk factors may be influenced by other factors. As the proposed approaches (specially: g-methods, Mendelian randomisation and twin-study analyses) rely on different assumptions and designs, they will allow triangulation of evidence and lead to more robust results and reliable conclusions than those reported in published research. Therefore it is anticipated that the proposed project will considerably further our understanding of the development of CP.
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使用倾向分(Propensity Score)和主分层(Principal Stratification)进行因果推断
  • 批准号:
    10401003
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    11.0万元
  • 批准年份:
    2004
  • 负责人:
    张俊妮
  • 依托单位: