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
  • [41] PROGNOSTIC MODELS AND THE PROPENSITY SCORE
    DRAKE, C
    FISHER, L
    INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 1995, 24 (01) : 183 - 187
  • [42] Combining propensity score-based stratification and weighting to improve causal inference in the evaluation of health care interventions
    Linden, Ariel
    JOURNAL OF EVALUATION IN CLINICAL PRACTICE, 2014, 20 (06) : 1065 - 1071
  • [43] Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference
    Qin, Jing
    Yu, Tao
    Li, Pengfei
    Liu, Hao
    Chen, Baojiang
    STATISTICS IN MEDICINE, 2019, 38 (08) : 1442 - 1458
  • [44] Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation
    Linden, Ariel
    Adams, John L.
    JOURNAL OF EVALUATION IN CLINICAL PRACTICE, 2012, 18 (02) : 317 - 325
  • [45] MIXTURE MODELING METHODS FOR CAUSAL INFERENCE WITH MULTILEVEL DATA
    Kim, Jee-Seon
    Steiner, Peter M.
    Lim, Wen-Chiang
    ADVANCES IN MULTILEVEL MODELING FOR EDUCATIONAL RESEARCH: ADDRESSING PRACTICAL ISSUES FOUND IN REAL-WORLD APPLICATIONS, 2016, : 335 - 359
  • [46] Randomized Experiments in Education, with Implications for Multilevel Causal Inference
    Raudenbush, Stephen W.
    Schwartz, Daniel
    ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 7, 2020, 2020, 7 : 177 - 208
  • [47] Graphical models, causal inference, and econometric models
    Spirtes, Peter
    JOURNAL OF ECONOMIC METHODOLOGY, 2005, 12 (01) : 3 - 34
  • [48] Estimating Causal Effects With Propensity Score Models: An Evaluation of the Touch Condom Media Campaign in Pakistan
    Beaudoin, Christopher E.
    Chen, Hongliang
    Agha, Sohail
    JOURNAL OF HEALTH COMMUNICATION, 2016, 21 (04) : 415 - 423
  • [49] Propensity score approaches with multilevel data: A simulation study
    Borges, Gabriela L.
    Moreira, Marisleane
    Sanni Ali, M.
    Barreto, Mauricio L.
    Smeeth, Liam
    Fiaccone, Rosemeire L.
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2020, 29 : 387 - 387
  • [50] Bayesian inference over ICA models: application to multibiometric score fusion with quality estimates
    Rouigueb, A.
    Chitroub, S.
    Bouridane, A.
    JOURNAL OF APPLIED STATISTICS, 2014, 41 (10) : 2123 - 2140