Summary. The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguing issues are examined here, including the robustness of the MLEs against departure from the normal random effects assumption, and difficulties with the profile likelihood approach to provide reliable estimates for the standard error of the MLEs. We provide insights into the robustness property and suggest to overcome the difficulty of reliable estimates for the standard errors by using bootstrap procedures. Numerical studies and data analysis illustrate our points.

The results of our nationwide, population-based study suggest that patients who undergo anaesthesia and surgery may be at increased risk of dementia.

The accelerated failure time (AFT) model is an attractive alternative to the Cox model when the proportionality assumption fails to capture the relation between the survival time and longitudinal covariates. Several complications arise when the covariates are measured intermittently at different time points for different subjects, possibly with measurement errors, or measurements are not available after the failure time. Joint modelling of the failure time and longitudinal data offers a solution to such complications. We explore the joint modelling approach under the AFT assumption when covariates are assumed to follow a linear mixed effects model with measurement errors. The procedure is based on maximizing the joint likelihood function where random effects are treated as missing data. A Monte Carlo EM algorithm is employed to estimate all the unknown parameters, including the unknown baseline hazard function. The performance of the proposed procedure is checked in simulation studies. A case study of reproductive egg-laying data for female Mediterranean fruit flies and their relation to longevity demonstrate the effectiveness of the new procedure.

Patterns of behavior were recorded every 10 min during a 2-h period each day from eclosion to death for individual Drosophila melanogaster (both sexes) and Ceratitis capitata (males-only) including walking, preening, feeding, flying, and resting for the former species, and walking, calling (signaling), supine (upside-down), and resting in the latter. Results reveal that, with the exception of preening in D. melanogaster, behavioral patterns are age-specific and the frequency of several behaviors (e.g. supine in medfly; walking and resting in D. melanogaster) are correlated with timeto-death. This is the first set of studies to report the age patterns over a range of behavioral categories throughout the lives of individuals and thus the first that systematically documents the behavior of individuals at advanced ages. We suggest that the new and unique behaviors (e.g. supine) that emerge from the aging process be referred to as degenerative behaviors, not only to distinguish them from the conventional behavioral classifications (innate, learned), but also to reflect their emergent nature.

No abstract