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A test for detecting etiologic heterogeneity in epidemiological studies

Author

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  • S. Karagulle
  • Z. Kalaylioglu

Abstract

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.

Suggested Citation

  • S. Karagulle & Z. Kalaylioglu, 2016. "A test for detecting etiologic heterogeneity in epidemiological studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 538-549, March.
  • Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:538-549
    DOI: 10.1080/02664763.2015.1070808
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    References listed on IDEAS

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    1. Chatterjee, Nilanjan, 2004. "A Two-Stage Regression Model for Epidemiological Studies With Multivariate Disease Classification Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 127-138, January.
    2. Bull, Shelley B. & Mak, Carmen & Greenwood, Celia M. T., 2002. "A modified score function estimator for multinomial logistic regression in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 57-74, March.
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