Continuous Time Causal Mediation Models for Social Behavior in Health

健康社会行为的连续时间因果中介模型

• 批准号: 9235059
• 负责人: JEFFREY M ALBERT
• 金额: $ 40.11万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9235059
• 关键词: Accounting; Address; Affect; Area; Behavior; Behavior Therapy; Behavioral; Behavioral Model; Biological; Biological Factors; Biological Process; Caregivers; Categories; Child; Complex; Data; Data Set; Dental; Dental Care; Dental caries; Development; Differential Equation; Disadvantaged; Disease; Extinction (Psychology); Family; Family Study; Feedback; Funding Opportunities; Future; Health; Health behavior; Individual; Intervention; Intervention Studies; Link; Longitudinal Studies; Longitudinal observational study; Measurement; Mediation; Methodology; Methods; Mission; Modeling; Modification; Nature; Neighborhoods; Non-linear Models; Observational Study; Outcome; Pathway interactions; Pattern; Perception; Plant Roots; Probability; Process; Property; Randomized; Research; Research Design; Research Personnel; Risk Factors; Role; Sample Size; Schools; Social Behavior; Social Change; Social Environment; Social Sciences; Statistical Methods; Time; United States National Institutes of Health; Very Low Birth Weight Infant; Work; base; behavioral study; computer program; design; disadvantaged population; discrete time; early childhood; flexibility; health application; health data; health disparity; high risk; improved; intervention effect; longitudinal design; mathematical methods; novel; programs; psychologic; psychosocial; response; simulation; social; social engagement; temporal measurement; user-friendly

Health research has been increasingly focused on discovering the mechanisms through which an exposure or intervention affects health outcomes. Social science has become a part of this picture as the importance of social behavior and other social factors in health has been increasingly recognized. Causal mediation analysis methods have been developed to explore such mechanisms. However, these approaches are rooted in discrete time models, and fail to account for the continuous time nature of many, if not most, social and behavioral, as well as biological, processes. The proposed research will develop a new continuous time causal mediation analysis model (CMM). In addition to allowing a description of the dynamic interplay among variables in time, the new model will allow the incorporation of continuous time features such as time lags and extinction effects. The model will accommodate arbitrary data patterns, multiple causal links, continuous and categorical variables, and interaction/modification effects. We start with a differential equation model based on a potential outcome (causal) framework, and show the connection to a nonlinear model form that may be fit to data. An integration approach may be used to estimate mediation/path effects and predict the effect of new interventions. Secondly, we will extend CMMs to handle multi-level data, for example, family, neighborhood or other social variables, which may themselves unfold over time. In addition, we will model the impact of individuals on one another, providing a explanation for emergent social behavior. We will apply our new methods to a randomized study of family interventions to improve dental care use in children and a longitudinal observational study of family, neighborhood, behavioral and biologic factors in the progression of dental caries in disadvantaged and high risk (including very low birth weight) children. The new methods will be evaluated and refined through simulation studies. The new continuous time mediation models will be further used to study design issues, including the number and timing of measurements, and to investigator new designs, for example that randomize different subjects to different measurement times. Finally, we will develop user-friendly computer programs to allow behavioral and other health researchers to apply the new methods. The new methods will allow a more valid and thorough exploration of causal mechanisms involving social behavior, with broad applicability across heath areas. They will also facilitate the development of effective behavioral and other interventions to improve health outcomes and decrease health disparities.

健康研究越来越关注于发现一种疾病的机制