Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year


IMPACT FACTOR 2015: 0.667
5-year IMPACT FACTOR: 1.188

SCImago Journal Rank (SJR) 2015: 0.495
Source Normalized Impact per Paper (SNIP) 2015: 0.180
Impact per Publication (IPP) 2015: 0.319

Mathematical Citation Quotient (MCQ) 2015: 0.04

Online
ISSN
1557-4679
See all formats and pricing
In This Section

Comparing Approaches to Causal Inference for Longitudinal Data: Inverse Probability Weighting versus Propensity Scores

Ashkan Ertefaie
  • McGill University
/ David A Stephens
  • McGill University
Published Online: 2010-03-08 | DOI: https://doi.org/10.2202/1557-4679.1198

In observational studies for causal effects, treatments are assigned to experimental units without the benefits of randomization. As a result, there is the potential for bias in the estimation of the treatment effect. Two methods for estimating the causal effect consistently are Inverse Probability of Treatment Weighting (IPTW) and the Propensity Score (PS). We demonstrate that in many simple cases, the PS method routinely produces estimators with lower Mean-Square Error (MSE). In the longitudinal setting, estimation of the causal effect of a time-dependent exposure in the presence of time-dependent covariates that are themselves affected by previous treatment also requires adjustment approaches. We describe an alternative approach to the classical binary treatment propensity score termed the Generalized Propensity Score (GPS). Previously, the GPS has mainly been applied in a single interval setting; we use an extension of the GPS approach to the longitudinal setting. We compare the strengths and weaknesses of IPTW and GPS for causal inference in three simulation studies and two real data sets. Again, in simulation, the GPS appears to produce estimators with lower MSE.

Keywords: inverse probability weighting; propensity scores; longitudinal data

About the article

Published Online: 2010-03-08



Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1198. Export Citation

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Wayne Martin
Preventive Veterinary Medicine, 2014, Volume 113, Number 3, Page 281
[2]
Jeremy M. G. Taylor, Jincheng Shen, Edward H. Kennedy, Lu Wang, and Douglas E. Schaubel
Statistics in Medicine, 2014, Volume 33, Number 2, Page 257
[3]
Richard J. Cook, Ker-Ai Lee, Meaghan Cuerden, and Cecilia A. Cotton
Statistics in Medicine, 2013, Volume 32, Number 25, Page 4380

Comments (0)

Please log in or register to comment.
Log in