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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1年(2018年):RotationsYEAR 2(2019年):博士项目:加强反社会行为研究中的因果推理:使用创新的统计方法和纵向banketsConduct问题(CP)阐明环境风险因素的作用,以及相关的反社会行为,是转介到英国儿童和青少年心理健康服务的最常见原因。然而,因果关系,而不是相关性,推定的危险因素和CP的发展之间的关系还没有得到彻底的调查。为了设计有效的干预措施,CP,它是必不可少的检查和发展的病因学理论,确定可修改的因果风险因素,并了解何时以及为谁这些因素增加CP的可能性和严重程度。拟议的博士项目将解决现有文献的差距1)总结目前最严格的证据CP的风险因素; 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社会和生物风险因素是否因果影响CP的发展; 3)确定这些因果风险因素如何受到其他因素的影响。由于所提出的方法(特别是:g方法,孟德尔随机化和双胞胎研究分析)依赖于不同的假设和设计,它们将允许证据的三角测量,并导致比已发表的研究报告更稳健的结果和可靠的结论。因此,我们预期建议的工程计划会大大加深我们对郊野公园发展的认识。
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: