We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time-to-event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.

, , , , , , ,
doi.org/10.1002/bimj.201300028, hdl.handle.net/1765/74100
Biometrical Journal
Department of Biostatistics

Njagi, E. N., Molenberghs, G., Kenward, M., Verbeke, G., & Rizopoulos, D. (2014). A characterization of missingness at random in a generalized shared-parameter joint modeling framework for longitudinal and time-to-event data, and sensitivity analysis. Biometrical Journal. doi:10.1002/bimj.201300028