Abstract Cox regression with huge data sets. In demographic and epidemiologic research today, huge data sets are very common. There are several reasons for this. Most obvious is the fact that large data bases and population registers are built all over the world.
Researchers have an easy access to huge numbers of individual life histories in different contexts.
Second, information on each individual tends to be more and more detailed. Third, statistical methods, like Cox regression, are developed to take care of complicated data structures like time varying covariates, truncation and censoring. Their drawback is that computing time tends to grow faster than linearly with sample size. A common technique to deal with time varying covariates is to immediately cut a spell into two pieces at the duration where a change in the covariate value occurs. The first piece is right censored with the first value of the covariate, while the second piece is left truncated with the new value of the covariate.