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Exploring the varying covariate effects in proportional odds models with censored data

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  • Wang, Qihua
  • Tong, Xingwei
  • Sun, Liuquan

Abstract

In this article, we consider a proportional odds model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable for analysis of right-censored data. A local maximum likelihood approach is presented to estimate nonlinear interactions (the coefficient functions) and the baseline function. The proposed estimators are shown to be consistent and asymptotically normal with the asymptotic variance estimated consistently. Also, we develop local profile likelihood ratio method to construct confidence region of coefficient functions. Simulation studies are conducted to evaluate the performances of the proposed estimators, and compare the normal approximation based confidence regions and local profile likelihood ratio based confidence regions. The method is illustrated with Stanford heart transplant data.

Suggested Citation

  • Wang, Qihua & Tong, Xingwei & Sun, Liuquan, 2012. "Exploring the varying covariate effects in proportional odds models with censored data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 168-189.
    • Handle: RePEc:eee:jmvana:v:109:y:2012:i:c:p:168-189
    DOI: 10.1016/j.jmva.2012.02.013
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    1. Lu Tian & David Zucker & L.J. Wei, 2005. "On the Cox Model With Time-Varying Regression Coefficients," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 172-183, March.
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    4. Qihua Wang & J. N. K. Rao, 2002. "Empirical Likelihood‐based Inference in Linear Models with Missing Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 563-576, September.
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    6. Zeng, Donglin & Lin, D.Y. & Yin, Guosheng, 2005. "Maximum Likelihood Estimation for the Proportional Odds Model With Random Effects," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 470-483, June.
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    Cited by:

    1. Shanshan Lu & Jingjing Wu & Xuewen Lu, 2019. "Efficient estimation of the varying-coefficient partially linear proportional odds model with current status data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 173-194, March.

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