OBJECTIVES: Missing patient reported outcome (PRO) data can arise from several processes. In the context of survival designs, the missingness may very well be outcome-dependent. This type of missing data is known as missing not at random (MNAR). In this circumstance, the PRO scores will produce biased treatment efficacy estimates. This bias can lead to the incorrect inference of no difference in treatment efficacy across arms. Outcome-dependent missingness poses serious challenges and requires the use of statistical approaches that properly adjust for this type of missing data.

METHODS: Currently, the state of the art in modeling PROs in a survival analysis context is to simultaneously model observed scores and the drop-out mechanism. This is referred to as a joint process model. However, interest in item response theory (IRT) approaches has grown in recent years. An IRT-based framework has been developed for evaluating PRO-based scores in survival analysis context. A longitudinal IRT model that incorporates the drop-out mechanism has been created. This allows the IRT scores and the drop-out mechanism to be modeled simultaneously. A simulation study based on empirical PRO data is employed here to illustrate the advantages of such an approach.

RESULTS: The results of the simulation study show that the IRT approach to jointly model Quality of Life (QoL) scores and drop-out allows for the accurate estimation of separation of the treatment arms. Other approaches, such as a longitudinal IRT model that ignores drop-out, fail to detect separation.

CONCLUSIONS: An IRT-based method has been developed that can better estimate the treatment effect in a survival analysis context. Notably, this approach can be implemented in standard commercial IRT software.