Joint analysis of bivariate longitudinal ordinal outcomes and competing risks survival times with nonparametric distributions for random effects

Statistics in Medicine
Ning LiChi-Hong Tseng

Abstract

We propose a semiparametric joint model for bivariate longitudinal ordinal outcomes and competing risks failure time data. The association between the longitudinal and survival endpoints is captured by latent random effects. This approach generalizes previous joint analysis that considers only one response variable at the longitudinal endpoint. One unique feature of the proposed model is that we relax the commonly used normality assumption for random effects and leave the distribution completely unspecified. We use a modified version of the vertex exchange method in conjunction with an expectation-maximization algorithm to estimate the random effects distribution and model parameters. We show via simulations that robust parameter estimates are obtained from the proposed method under various scenarios. We illustrate the approach using cough severity and frequency data from a scleroderma lung study.

References

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Citations

Sep 27, 2016·Biometrical Journal. Biometrische Zeitschrift·An-Min TangNian-Sheng Tang
Sep 9, 2016·BMC Medical Research Methodology·Graeme L HickeyRuwanthi Kolamunnage-Dona
Mar 5, 2016·Biological Reviews of the Cambridge Philosophical Society·Sandra HamelJean-Michel Gaillard
Aug 7, 2019·Statistics in Medicine·Cécile Proust-LimaJean-François Dartigues

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