Data analysis for accelerated life tests with constrained randomization

Accelerated life tests (ALTs) often involve experimental protocols with constrained randomization such as subsampling or random block. As a result, life-time data may construct a grouped structure among the observations. In this paper, we develop a generalized linear mixed model (GLMM) approach for analyzing ALT data with a grouped structure in order to reflect random effects of groups in the model. The GLMM approach provides a flexible way to model censored failure time data with random effects. Particularly, for Weibull failure time distribution, we describe an iterative procedure for the model parameters estimation and derive the asymptotic variance-covariance matrix using the approximated likelihood function. Two examples of life-time data with subsampling and random block are analyzed by the proposed method, which is implemented by modern computer software.