This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Comparison of the Inverse Probability of Treatment Weighted (IPTW) Estimator With a Naïve Estimator in the Analysis of Longitudinal Data With Time-Dependent Confounding: A Simulation Study

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Thaddeus Haight (Division of Epidemiology, School of Public Health, University of California, Berkeley)
Romain Neugebauer (Division of Biostatistics, School of Public Health, University of California, Berkeley)
Ira Tager (Division of Epidemiology, School of Public Health, University of California, Berkeley)
Mark van der Laan (Division of Biostatistics, School of Public Health, University of California, Berkeley)
Abstract

A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional regression, and an IPTW (Inverse Probability of Treatment Weighted) estimator, to true causal parameters for a given MSM (Marginal Structural Model). The study was extracted from a larger epidemiological study (Longitudinal Study of Effects of Physical Activity and Body Composition on Functional Limitation in the Elderly, by Tager et. al [accepted, Epidemiology, September 2003]), which examined the causal effects of physical activity and body composition on functional limitation. The simulation emulated the larger study in terms of the exposure and outcome variables of interest-- physical activity (LTPA), body composition (LNFAT), and physical limitation (PF), but used one time-dependent confounder (HEALTH) to illustrate the effects of estimating causal effects in the presence of time-dependent confounding. In addition to being a time-dependent confounder (i.e. predictor of exposure and outcome over time), HEALTH was also affected by past treatment. Under these conditions, naïve estimates are known to give biased estimates of the causal effects of interest (Robins, 2000). The true causal parameters for LNFAT (-0.61) and LTPA (-0.70) were obtained by assessing the log-odds of functional limitation for a 1-unit increase in LNFAT and participation in vigorous exercise in an ideal experiment in which the counterfactual outcomes were known for every possible combination of LNFAT and LTPA for each subject. Under conditions of moderate confounding, the IPTW estimates for LNFAT and LTPA were -0.62 and -0.94, respectively, versus the naïve estimates of -0.78 and -0.80. For increased levels of confounding of the LNFAT and LTPA variables, the IPTW estimates were -0.60 and -1.28, respectively, and the naïve estimates were -0.85 and -0.87. The bias of the IPTW estimates, particularly under increased levels of confounding, was explored and linked to violation of particular assumptions regarding the IPTW estimation of causal parameters for the MSM.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1139&context=ucbbiostat
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Berkeley Electronic Press in its series U.C. Berkeley Division of Biostatistics Working Paper Series with number 1139.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 11 Jul 2004
Date of revision:
Handle: RePEc:bep:ucbbio:1139

Note: oai:bepress.com:ucbbiostat-1139
Contact details of provider:
Web page: http://www.bepress.com

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords: Causal inference; Marginal Structural Models (MSMs); Inverse Probability of Treatment causal inference; Marginal Structural Models (MSMs); Inverse Probability of Treatment causal inference; Marginal Structural Models (MSMs); Inverse Probability of Treatment causal inference; Marginal Structural Models (MSMs); Inverse Probability of Treatment Weighted Estimator (IPTW); longitudinal study; functional limitation; body composition; physical activity ;

Statistics
Access and download statistics

Did you know? Over 80% of the top 1000 economists are registered on RePEc.

This page was last updated on 2009-10-23.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.