Application of Survival Analysis Methods to Long Term Care Insurance

With the introduction of compulsory long term care (LTC) insurance
in Germany in 1995, a large claims portfolio with a significant
proportion of censored observations became available. In first part
of this paper we present an analysis of part of this portfolio
using the Cox proportional hazard model (Cox, 1972) to estimate
transition intensities. It is shown that this approach allows the
inclusion of censored observations as well as the inclusion of time
dependent risk factors such as time spent in LTC. This is in
contrast to the more commonly used Poisson regression with
graduation approach (see for example Renshaw and Haberman 1995)
where censored observations and time dependent risk factors are
ignored. In the second part we show how these estimated transition
intensities can be used in a multiple state Markov process (see
Haberman and Pitacco, 1999) to calculate premiums for LTC insurance
plans.