Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in the GEE setup. In this study, an application of mAIC is showed in selecting important covariates associated with pregnancy related complications of Bangladeshi women.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Budapest: Akademiai Kiado.
Akhter, H. H., Chowdhury, M. E., and Sen, A. (1996). A Cross-Sectional Study on Maternal Morbidity in Bangladesh. Dhaka: Bangladesh Institute of Research for
Promotion of Essential and Reproductive Health and Technologies.
Cantoni, E., Flemming, J. M., and Ronchetti, E. (2005). Variable selection for marginal longitudinal generalized linear models. Biometrics, 61, 507-514.
Cantoni, E., Flemming, J. M., and Ronchetti, E. (2008). Longitudinal variable selection by cross-validation in the case of many covariates. Statistics in Medicine. (in press)
Ceesay, S. M., Prentice, A. M., Cole, T. J., Foord, F., Poskitt, E., Weaver, L. T., et al. (1997). Effects on birth weight and perinatal mortality of maternal dietary supplements in rural Gambia: 5-year randomized controlled trial. British Medical Journal, 315, 786-790.
Chakraborty, N., Islam, M. A., Chowdhury, R. I., and Bari, W. (2003). Analysis of ante-partum maternal morbidity in rural Bangladesh. Australian Journal of Rural Health, 11, 22-27.
Choolani, M., and Ratnam, S. S. (1995). Maternal morbidity: a global overview. Journal of the Indian Medical Association, 93, 36-40.
Chowdhury, M. E., Botlero, R., Koblinsky, M., Saha, S. K., Dieltiens, G., and Ronsmans, C. (2007). Determinants of reduction in maternal mortality in Matlab, Bangladesh:
a 30-year cohort study. Lancet, 370, 1320-1328.
Gulshan, J., Chowdhury, R. I., Islam, M. A., and Akhter, H. H. (2005). GEE models for maternal morbidity in rural Bangladesh. Austrian Journal of Statistics, 34, 295-304.
Islam, M. A., Chowdhury, R. I., Chakraborty, N., and Bari, W. (2004). A multistage
model for maternal morbidity during antenatal, delivery and postpartum periods. Statistics in Medicine, 23, 137-58.
Joe, H. (1997). Multivariate Dependence Concept. London: Chapman & Hall.
Joe, H., and Latif, A. H. M. M. (2005). Computations for familial analysis of Binary traits. Computational Statistics, 20, 439-448.
Liang, K.-Y., and Zeger, S. L. (1986). Longitudinal data analysis with generalized linear models. Biometrika, 73, 13-22.
Linhart, L., and Zucchini,W. (1986). Model Selection. New York: JohnWiley and Sons.
McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). London: Chapman & Hall.
Molenberghs, G., and Lesaffre, E. (1994). Marginal modeling of correlated ordinal data using a multivariate Plackett distribution. Journal of the American Statistical Association, 89, 633-44.
Pan, W. (2001a). Akaike’s information criterion in generalized estimating equations. Biometrics, 57, 120-125.
Pan, W. (2001b). Model selection in estimating equations. Biometrics, 57, 529-534.
Pan, W., and Lee, C. T. (2001). Bootstrap model selection in generalized linear models. Journal of Agricultural, Biological & Environmental Statistics, 6, 49-61.
Zeger, S. L., and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121-130.
Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41-61.
How to Cite
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.