Long-term survivor model with bivariate random effects: applications to bone marrow transplant and carcinoma study data

Statistics in Medicine
Xin Lai, Kelvin K W Yau

Abstract

Cured patients (or the so-called long-term survivors) are increasingly being observed in clinical trial studies. As exemplified in two data sets, the bone marrow transplantation study for leukaemia patients and the multi-centre study for patients with carcinoma in the oropharynx, a considerable portion of the patients in these studies are deemed to be cured. With the presence of random hospital/centre effects, a long-term survivor model with bivariate random effects is proposed to analyse clustered survival data with a possible portion of cured patients. This model extends earlier work by allowing random effects in both the cured fraction and the hazard function parts to follow a bivariate normal distribution, which gives a generalized model with an additional correlation parameter governing the relationship between the recovery probability and the instantaneous failure rate due to the hospital/centre effects. By adopting the GLMM formulation, random effects are incorporated in the model via the linear predictor terms. REML estimation of parameters is achieved via the EM algorithm. Application to the two sets of data illustrates the usefulness of the proposed model. A simulation study is conducted to assess the performance of t...Continue Reading

References

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Citations

Sep 17, 2011·Clinical Trials : Journal of the Society for Clinical Trials·Anna S C ConlonGreg Yothers
Jan 8, 2011·Statistics in Medicine·Yingwei Peng, Jeremy M G Taylor
Apr 8, 2011·Statistics in Medicine·Liming XiangKelvin K W Yau
Dec 4, 2012·Statistics in Medicine·Yi Niu, Yingwei Peng
Jul 12, 2019·Statistical Methods in Medical Research·Richard TawiahShu Kay Ng
Jun 25, 2016·Lifetime Data Analysis·Lajmi Lakhal-Chaieb, Thierry Duchesne
May 8, 2018·Biometrical Journal. Biometrische Zeitschrift·Yi NiuYingwei Peng
Dec 15, 2010·Journal of Animal Science·J ØdegårdT H E Meuwissen

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