Propensity Score Estimates in Multilevel Models for Causal Inference

被引:9
|
作者
Eckardt, Patricia [1 ]
机构
[1] SUNY Stony Brook, Sch Nursing, Hlth Sci Ctr, Stony Brook, NY 11794 USA
关键词
adolescent; causal effects estimates; counterfactual; hierarchical linear modeling; nutrition; pediatric obesity prevention; potential outcomes; BODY-MASS INDEX; OBESITY PREVENTION; CHILDHOOD OBESITY; HEALTH; POLICY; ADOLESCENTS; STATISTICS; CHILDREN; OUTCOMES; STUDENT;
D O I
10.1097/NNR.0b013e318253a1c4
中图分类号
R47 [护理学];
学科分类号
1011 ;
摘要
Background: Teenage obesity is a national epidemic that requires school- and community-based initiatives to support healthy behaviors of students regarding exercise and nutrition to decrease the prevalence. Objectives: The aim of this study was to demonstrate a methodology for an estimation of causal effects of the adoption of healthy behaviors with a potential outcomes approach within a multilevel treatment setting of school program adoption of a socially supportive environment. Methods: Propensity score estimates within a multilevel model provided causal estimates of the impact of the adoption of health habits by students within supportive school environments (SSEs) and non-SSEs. A potential outcomes approach to causal modeling was shown with a secondary analysis of the National Longitudinal Study of Adolescent Health study. The student participants consisted of 13,854 adolescent students, with an accompanying sample of 164 school administrators. Results: The effect of healthy eating habits in an SSE was a statistically nonsignificant decrease in body mass index (BMI). The effect of healthy eating habits in a non-SSE was a statistically nonsignificant increase in BMI. The difference between the healthy habit practices for students in supportive and nonsupportive schools was a resultant difference in BMI of 0.3484. Discussion: The results demonstrate a difference in causal effects of eating habits in different school settings. Further research regarding causal effects of student habits and school programs is indicated.
引用
收藏
页码:213 / 223
页数:11
相关论文
共 50 条
  • [31] Applied comparison of large‐scale propensity score matching and cardinality matching for causal inference in observational research
    Stephen P. Fortin
    Stephen S Johnston
    Martijn J Schuemie
    BMC Medical Research Methodology, 21
  • [32] Propensity score analysis (PSA) for sensory causal inference - Global consumer psychographics and applications for phytonutrient supplements
    Kuesten, Carla
    Dang, Jennifer
    Nakagawa, Mild
    Bi, Jian
    Meiselman, Herbert L.
    FOOD QUALITY AND PREFERENCE, 2016, 51 : 77 - 88
  • [33] Statistical workshop on causal inference with observational data in addiction research - propensity score matching using R
    Chan, Gary C. K.
    Lim, Carmen C. W.
    Sun, Tianze
    Stjepanovic, Daniel
    Connor, Jason P.
    Hall, Wayne
    Leung, Janni
    DRUG AND ALCOHOL REVIEW, 2022, 41 : S19 - S20
  • [34] Partially pooled propensity score models for average treatment effect estimation with multilevel data
    Lee, Youjin
    Nguyen, Trang Q.
    Stuart, Elizabeth A.
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 2021, 184 (04) : 1578 - 1598
  • [35] The specification of the propensity score in multilevel observational studies
    Arpino, Bruno
    Mealli, Fabrizia
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2011, 55 (04) : 1770 - 1780
  • [36] Propensity score weighting for causal subgroup analysis
    Yang, Siyun
    Lorenzi, Elizabeth
    Papadogeorgou, Georgia
    Wojdyla, Daniel M.
    Li, Fan
    Thomas, Laine E.
    STATISTICS IN MEDICINE, 2021, 40 (19) : 4294 - 4309
  • [37] Covariate-balancing-propensity-score-based inference for linear models with missing responses
    Guo, Donglin
    Xue, Liugen
    Hu, Yuqin
    STATISTICS & PROBABILITY LETTERS, 2017, 123 : 139 - 145
  • [38] Inference for proportional hazard model with propensity score
    Lu, Bo
    Cai, Dingjiao
    Wang, Luheng
    Tong, Xingwei
    Xiang, Huiyun
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2018, 47 (12) : 2908 - 2918
  • [39] Using Propensity Scores for Causal Inference: Pitfalls and Tips
    Shiba, Koichiro
    Kawahara, Takuya
    JOURNAL OF EPIDEMIOLOGY, 2021, 31 (08) : 457 - 463
  • [40] A distributional approach for causal inference using propensity scores
    Tan, Zhiqiang
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2006, 101 (476) : 1619 - 1637