Jump to ContentJump to Main Navigation

The International Journal of Biostatistics

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

2 Issues per year

IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475

SCImago Journal Rank (SJR) 2014: 1.247
Source Normalized Impact per Paper (SNIP) 2014: 1.078
Impact per Publication (IPP) 2014: 1.206

Mathematical Citation Quotient (MCQ) 2014: 0.07

Simple Optimal Weighting of Cases and Controls in Case-Control Studies

Sherri Rose1 / Mark J. van der Laan2

1University of California, Berkeley

2University of California, Berkeley

Citation Information: The International Journal of Biostatistics. Volume 4, Issue 1, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1115, September 2008

Publication History

Published Online:

Researchers of uncommon diseases are often interested in assessing potential risk factors. Given the low incidence of disease, these studies are frequently case-control in design. Such a design allows a sufficient number of cases to be obtained without extensive sampling and can increase efficiency; however, these case-control samples are then biased since the proportion of cases in the sample is not the same as the population of interest. Methods for analyzing case-control studies have focused on utilizing logistic regression models that provide conditional and not causal estimates of the odds ratio. This article will demonstrate the use of the prevalence probability and case-control weighted targeted maximum likelihood estimation (MLE), as described by van der Laan (2008), in order to obtain causal estimates of the parameters of interest (risk difference, relative risk, and odds ratio). It is meant to be used as a guide for researchers, with step-by-step directions to implement this methodology. We will also present simulation studies that show the improved efficiency of the case-control weighted targeted MLE compared to other techniques.

Keywords: case control sampling; causal effect; counterfactual; double robust estimation; estimating function; inverse probability of treatment weighting; locally efficient estimation; marginal structural models; targeted maximum likelihood estimation

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.

Sherri Rose, Julie Shi, Thomas G. McGuire, and Sharon-Lise T. Normand
Statistics in Biosciences, 2015
James M.S. Wason and Frank Dudbridge
The American Journal of Human Genetics, 2012, Volume 90, Number 5, Page 760
Shu-Ling Chong, Nan Liu, Sylvaine Barbier, and Marcus Eng Hock Ong
BMC Medical Research Methodology, 2015, Volume 15, Number 1
Mark J. van der Laan and Richard J. C. M. Starmans
Advances in Statistics, 2014, Volume 2014, Page 1
Erika Cule and Maria De Iorio
Genetic Epidemiology, 2013, Volume 37, Number 7, Page 704

Comments (0)

Please log in or register to comment.