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Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement

Machteld Vandecandelaere, Stijn Vansteelandt UGent, Bieke De Fraine and Jan Van Damme (2016) MULTIVARIATE BEHAVIORAL RESEARCH. 51(6). p.843-864
abstract
One of the main objectives of many empirical studies in the social and behavioral sciences is to assess the causal effect of a treatment or intervention on the occurrence of a certain event. The randomized controlled trial is generally considered the gold standard to evaluate such causal effects. However, for ethical or practical reasons, social scientists are often bound to the use of nonexperimental, observational designs. When the treatment and control group are different with regard to variables that are related to the outcome, this may induce the problem of confounding. A variety of statistical techniques, such as regression, matching, and subclassification, is now available and routinely used to adjust for confounding due to measured variables. However, these techniques are not appropriate for dealing with time-varying confounding, which arises in situations where the treatment or intervention can be received at multiple timepoints. In this article, we explain the use of marginal structural models and inverse probability weighting to control for time-varying confounding in observational studies. We illustrate the approach with an empirical example of grade retention effects on mathematics development throughout primary school.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
journal title
MULTIVARIATE BEHAVIORAL RESEARCH
volume
51
issue
6
pages
843 - 864
Web of Science type
Article
Web of Science id
000390878800009
ISSN
0027-3171
DOI
10.1080/00273171.2016.1155146
UGent publication?
yes
classification
U
copyright statement
I have transferred the copyright for this publication to the publisher
id
8507228
handle
http://hdl.handle.net/1854/LU-8507228
date created
2017-02-02 23:00:55
date last changed
2017-03-07 15:12:03
@article{8507228,
  abstract     = {One of the main objectives of many empirical studies in the social and behavioral sciences is to assess the causal effect of a treatment or intervention on the occurrence of a certain event. The randomized controlled trial is generally considered the gold standard to evaluate such causal effects. However, for ethical or practical reasons, social scientists are often bound to the use of nonexperimental, observational designs. When the treatment and control group are different with regard to variables that are related to the outcome, this may induce the problem of confounding. A variety of statistical techniques, such as regression, matching, and subclassification, is now available and routinely used to adjust for confounding due to measured variables. However, these techniques are not appropriate for dealing with time-varying confounding, which arises in situations where the treatment or intervention can be received at multiple timepoints. In this article, we explain the use of marginal structural models and inverse probability weighting to control for time-varying confounding in observational studies. We illustrate the approach with an empirical example of grade retention effects on mathematics development throughout primary school.},
  author       = {Vandecandelaere, Machteld and Vansteelandt, Stijn and De Fraine, Bieke and Van Damme, Jan},
  issn         = {0027-3171},
  journal      = {MULTIVARIATE BEHAVIORAL RESEARCH},
  number       = {6},
  pages        = {843--864},
  title        = {Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement},
  url          = {http://dx.doi.org/10.1080/00273171.2016.1155146},
  volume       = {51},
  year         = {2016},
}

Chicago
Vandecandelaere, Machteld, Stijn Vansteelandt, Bieke De Fraine, and Jan Van Damme. 2016. “Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement.” Multivariate Behavioral Research 51 (6): 843–864.
APA
Vandecandelaere, M., Vansteelandt, S., De Fraine, B., & Van Damme, J. (2016). Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement. MULTIVARIATE BEHAVIORAL RESEARCH, 51(6), 843–864.
Vancouver
1.
Vandecandelaere M, Vansteelandt S, De Fraine B, Van Damme J. Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement. MULTIVARIATE BEHAVIORAL RESEARCH. 2016;51(6):843–64.
MLA
Vandecandelaere, Machteld, Stijn Vansteelandt, Bieke De Fraine, et al. “Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement.” MULTIVARIATE BEHAVIORAL RESEARCH 51.6 (2016): 843–864. Print.