Multilevel Hazard Models in Log-Time Metric? 

 Has anybody figured out how to estimate multilevel hazard models with time-varying covariates in log-time metric (i.e., an accelerated failure time model)?  

 Together with two colleagues from the Medical School, I’m working on the effect of contextual variables on mortality.  We're using a large longitudinal dataset of around ½ million married couples and nine years of follow up. Our key independent variable is time varying. In recent years, much work has been done on multilevel hazard models, for example, that done by Harvey Goldstein and colleages. But the standard recommendation for estimating such models in the presence of time-varying covariates is to approximate the Cox proportional hazard model using a conditional (i.e, fixed effects) logistic regression, which makes hefty demands on memory. Given the size of our data, we can implement this standard strategy only for a subset of our data.  

 We are hoping that the log-time metric would make better use of memory and allow us to use the entire sample. The question is: has anybody already developed software to estimate multilevel hazard models with time-varying covariates in the log-time metric? Or can't it be done in principle? Either way, I'd be grateful for pointers.