Analyzing longitudinal data with patients in different disease states during follow-up and death as final state

Le Cessie, de Vries, Buijs and Post have a new paper in Statistics in Medicine. This is concerned with estimating mean quality of life in breast cancer patients at different time points. Standard approaches to analyzing such longitudinal data would be generalized estimating equations (GEE). However, observations are often missing and assuming such data are missing completely at random (MCAR) is unrealistic or even missing at random. In the current study a three-state progressive illness-death model is considered where the illness state refers to presence of a relapse. Both Markov (or clock-forward) and semi-Markov (or clock-reset) models are considered. There was continuous observation of the illness-death process, whereas the quality of life was observed at a common set of time points. The authors propose to model quality of life scores conditional on the state occupied in the multi-state model. A more realistic missingness model can then be adopted by assuming MAR conditional on the occupied state. Inverse probability weighting is used to deal with the missing data. Standard error estimation is performed by bootstrapping.

While the model gives an improved picture compared to ignoring the disease state, the model still makes the assumption that quality of life is dependent on time and current disease state but not on the time since entry into the current disease state.