A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case–Control Studies
Article first published online: 14 SEP 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 845–854, September 2010
How to Cite
Gebregziabher, M. and Langholz, B. (2010), A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case–Control Studies. Biometrics, 66: 845–854. doi: 10.1111/j.1541-0420.2009.01322.x
- Issue published online: 14 SEP 2009
- Article first published online: 14 SEP 2009
- Received June 2008. Revised June 2009. Accepted June 2009.
- Case–control studies;
- Counting process;
- Missing data;
- Multiple myeloma;
Summary In individually matched case–control studies, when some covariates are incomplete, an analysis based on the complete data may result in a large loss of information both in the missing and completely observed variables. This usually results in a bias and loss of efficiency. In this article, we propose a new method for handling the problem of missing covariate data based on a missing-data-induced intensity approach when the missingness mechanism does not depend on case–control status and show that this leads to a generalization of the missing indicator method. We derive the asymptotic properties of the estimates from the proposed method and, using an extensive simulation study, assess the finite sample performance in terms of bias, efficiency, and 95% confidence coverage under several missing data scenarios. We also make comparisons with complete-case analysis (CCA) and some missing data methods that have been proposed previously. Our results indicate that, under the assumption of predictable missingness, the suggested method provides valid estimation of parameters, is more efficient than CCA, and is competitive with other, more complex methods of analysis. A case–control study of multiple myeloma risk and a polymorphism in the receptor Inter-Leukin-6 (IL-6-α) is used to illustrate our findings.