Efficient Estimation of Partly Linear Transformation Model with Interval-censored Competing Risks Data
Date
2019-09-19
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Abstract
We consider the class of semiparametric generalized odds rate transformation models to estimate the cause-specific cumulative incidence function, which is an important quantity under competing risks framework, and assess the contribution of covariates with interval-censored competing risks data. The model is able to handle both linear and non-linear components. The baseline cumulative incidence functions and non-linear components of different competing risks are approximated with B-spline basis functions or Bernstein polynomials, and the estimated parameters are obtained by employing the sieve maximum likelihood estimation. We designed two examples in the simulation studies and the simulation results show that the method performs well. We used the proposed method to analyze the HIV data obtained from patients in a large cohort study in sub-Saharan Africa.
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Keywords
semi-parametric model, interval-censored data, competing risks, estimation.
Citation
Wang, Y. (2019). Efficient Estimation of Partly Linear Transformation Model with Interval-censored Competing Risks Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.