## Thursday, 3 November 2011

### Relative survival multistate Markov model

Ella Huszti, Michal Abrahamowicz, Ahmadou Alioum, Christine Binquet and Catherine Quantin have a new paper in Statistics in Medicine. This develops a illness-death Markov model with piecewise constant intensities in order to fit a relative survival model. Such models seek to compare the mortality rates from disease states with those in the general population, so that the hazard from state r to absorbing state D are given by
$\lambda_{rD}(t ; z) = \lambda_{\mbox{pop}}(t ; z) + \lambda^{*}_{rD}(t)\exp(\beta_{rD} z)$
The population hazard is generally assumed known and taken from external sources like life tables. Transition intensities between transient states in the Markov model are not subject to any such restrictions. One can think this as being a model where there is an unobservable cause of death state "death from natural causes" which has the same transition intensity regardless of the current transient state.

The paper has quite a lot of simulation results, most of which seem unnecessary. They simulate data from the relative survival model and show, unsurprisingly, that a misspecified model that assumes proportionality with respect to the whole hazard (rather than the excess hazard) is biased. They also compare the results with Cox regression models (on each transition intensity) and Lunn-McNeil competing risks model (i.e. assuming a Cox model assuming common baseline for the competing risks).
The data are a mixture of clinic visits that yield interval censored times of events such as recurrence, but times of death are known exactly. Presumably for the Cox models a further approximation of interval-censored to right-censored data is made.