A test for detecting etiologic heterogeneity in epidemiological studies

Current statistical methods for analyzing epidemiological data with disease subtype information allow us to acquire knowledge not only for risk factor-disease subtype association but also, on a more profound account, heterogeneity in these associations by multiple disease characteristics (so-called etiologic heterogeneity of the disease). Current interest, particularly in cancer epidemiology, lies in obtaining a valid p-value for testing the hypothesis whether a particular cancer is etiologically heterogeneous. We consider the two-stage logistic regression model along with pseudo-conditional likelihood estimation method and design a testing strategy based on Rao's score test. An extensive Monte Carlo simulation study is carried out, false discovery rate and statistical power of the suggested test are investigated. Simulation results indicate that applying the proposed testing strategy, even a small degree of true etiologic heterogeneity can be recovered with a large statistical power from the sampled data. The strategy is then applied on a breast cancer data set to illustrate its use in practice where there are multiple risk factors and multiple disease characteristics of simultaneous concern.


A multi-layered graphical model of the relation among SNPS, GENES, and pathways based on subgraph search
Ersoy, Gökhan; Aydın Son, Yeşim; Can, Tolga; Department of Bioinformatics (2015)
The analysis of Single Nucleotide Polymorphisms (SNPs) through Genome Wide Association Studies (GWAS) presents great potential for describing disease loci and gaining insight into the underlying etiology of diseases. Recently described combined p-value approach allows identification of associations at gene and pathway level. The integrated programs like METU-SNP produce simple lists of either SNP id/gene id/pathway title and their p-values and significance status or SNP id/disease id/pathway information. In...
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Background: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SE...
Citation Formats
S. Karagulle and Z. I. Kalaylıoğlu Akyıldız, “A test for detecting etiologic heterogeneity in epidemiological studies,” JOURNAL OF APPLIED STATISTICS, pp. 538–549, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40694.