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Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits

Jingang Miao1, Samiran Sinha1*, Suojin Wang1, W Ryan Diver2 and Susan M Gapstur2

1Department of Statistics, Texas A&M University, College Station, TX 77843, USA

2Epidemiology Research Program, American Cancer Society, Inc. Atlanta, GA 30303, USA

*Corresponding Author:
Samiran Sinha
Department of Statistics, Texas A&M University
College Station, TX 77843, USA
Tel: 979-845-3160
Fax: 979-845-3144
E-mail: [email protected]

Received date: February 21, 2014; Accepted date: May 06, 2014; Published date: May 13, 2014

Citation: Miao J, Sinha S, Wang S, Ryan Diver W, Gapstur SM (2014) Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits. J Biomet Biostat 5:197. doi: 10.4172/2155-6180.1000197

Copyright: © 2014 Miao J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

Abstract

In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (heterogeneity) among the relative risk parameters associated with subtypes are of great interest to better understand disease etiology. Due to the heterogeneity of the relative risk parameters, when a risk factor is changed, the prevalence of one subtype may change more than that of another subtype does. Estimation of the heterogeneity parameters is difficult when disease trait information is only partially observed and the number of disease subtypes is large. We consider a robust semiparametric approach based on the pseudo-conditional likelihood for estimating these heterogeneity parameters. Through simulation studies, we compare the robustness and efficiency of our approach with that of the maximum likelihood approach. The method is then applied to analyze the associations of weight gain with risk of breast cancer subtypes using data from the American Cancer Society Cancer Prevention Study II Nutrition Cohort.

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