Volume 26, Issue 30
Research Article

Baseline and treatment effect heterogeneity for survival times between centers using a random effects accelerated failure time model with flexible error distribution

Arnošt Komárek

Biostatistical Centre, Katholieke Universiteit Leuven, Kapucijnenvoer 35, 3000 Leuven, Belgium

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Emmanuel Lesaffre

Corresponding Author

E-mail address: emmanuel.lesaffre@med.kuleuven.be

Biostatistical Centre, Katholieke Universiteit Leuven, Kapucijnenvoer 35, 3000 Leuven, Belgium

Biostatistical Centre, Katholieke Universiteit Leuven, Kapucijnenvoer 35, 3000 Leuven, BelgiumSearch for more papers by this author
Catherine Legrand

European Organisation for Research and Treatment of Cancer, E. Mounierlaan 83/11, 1200 Brussels, Belgium

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First published: 01 October 2007
Citations: 11

Abstract

Nowadays, most clinical trials are conducted in different centers and even in different countries. In most multi‐center studies, the primary analysis assumes that the treatment effect is constant over centers. However, it is also recommended to perform an exploratory analysis to highlight possible center by treatment interaction, especially when several countries are involved. We propose in this paper an exploratory Bayesian approach to quantify this interaction in the context of survival data. To this end we used and generalized a random effects accelerated failure time model. The generalization consists in using a penalized Gaussian mixture as an error distribution on top of multivariate random effects that are assumed to follow a normal distribution. For computational convenience, the computations are based on Markov chain Monte Carlo techniques. The proposed method is illustrated on the disease‐free survival times of early breast cancer patients collected in the EORTC trial 10854. Copyright © 2007 John Wiley & Sons, Ltd.

Number of times cited according to CrossRef: 11

  • Multilevel model with random effects for clustered survival data with multiple failure outcomes, Statistics in Medicine, 10.1002/sim.8041, 38, 6, (1036-1055), (2018).
  • Semiparametric Bayesian accelerated failure time model with interval-censored data, Journal of Statistical Computation and Simulation, 10.1080/00949655.2014.915400, 85, 10, (2049-2058), (2014).
  • Hierarchical Failure Time Regression Using Mixtures for Classification of the Immune Response of Atlantic Salmon, Journal of Agricultural, Biological, and Environmental Statistics, 10.1007/s13253-014-0188-8, 19, 4, (501-521), (2014).
  • Multilevel mixed effects parametric survival models using adaptive Gauss–Hermite quadrature with application to recurrent events and individual participant data meta‐analysis, Statistics in Medicine, 10.1002/sim.6191, 33, 22, (3844-3858), (2014).
  • A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring, International Econometric Review, 10.33818/ier.278029, 6, 1, (24-41), (2014).
  • Interval estimation of random effects in proportional hazards models with frailties, Statistical Methods in Medical Research, 10.1177/0962280212474059, 25, 2, (936-953), (2013).
  • Bayesian random effects selection in mixed accelerated failure time model for interval‐censored data, Statistics in Medicine, 10.1002/sim.6002, 33, 6, (971-984), (2013).
  • Frailty modelling for survival data from multi‐centre clinical trials, Statistics in Medicine, 10.1002/sim.4250, 30, 17, (2144-2159), (2011).
  • Spatially Dependent Polya Tree Modeling for Survival Data, Biometrics, 10.1111/j.1541-0420.2010.01468.x, 67, 2, (391-403), (2010).
  • The regression analysis of correlated interval-censored data, Statistical Modelling: An International Journal, 10.1177/1471082X0900900403, 9, 4, (299-319), (2009).
  • Estimating local and global measures of association for bivariate interval censored data with a smooth estimate of the density, Statistics in Medicine, 10.1002/sim.3374, 27, 28, (5941-5955), (2008).

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