Semiparametric likelihood inference for left-truncated and right-censored data

Biostatistics
Chiung-Yu HuangJing Qin

Abstract

This paper proposes a new estimation procedure for the survival time distribution with left-truncated and right-censored data, where the distribution of the truncation time is known up to a finite-dimensional parameter vector. The paper expands on the Vardis multiplicative censoring model (Vardi, 1989. Multiplicative censoring, renewal processes, deconvolution and decreasing density: non-parametric estimation. Biometrika 76: , 751-761), establishes the connection between the likelihood under a generalized multiplicative censoring model and that for left-truncated and right-censored survival time data, and derives an Expectation-Maximization algorithm for model estimation. A formal test for checking the truncation time distribution is constructed based on the semiparametric likelihood ratio test statistic. In particular, testing the stationarity assumption that the underlying truncation time is uniformly distributed is performed by embedding the null uniform truncation time distribution in a smooth alternative (Neyman, 1937. Smooth test for goodness of fit. Skandinavisk Aktuarietidskrift 20: , 150-199). Asymptotic properties of the proposed estimator are established. Simulations are performed to evaluate the finite-sample perfor...Continue Reading

References

Apr 12, 2001·The New England Journal of Medicine·C WolfsonUNKNOWN Clinical Progression of Dementia Study Group
Oct 13, 2005·Statistics in Medicine·Masoud AsgharianXun Zhang
Aug 11, 2007·Biometrics·Micha Mandel, Rebecca A Betensky
Feb 11, 2012·Journal of the American Statistical Association·Jing QinYu Shen

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Citations

Sep 30, 2018·Biometrics·Fei Gao, Kwun Chuen Gary Chan
May 1, 2018·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Yifei SunChiung-Yu Huang

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