Exploring different multi-state structures when studying patterns of antidepressant medication use in women with breast cancer and matched healthy controls

Abstract

Abstract Background: A breast cancer diagnosis is related with psychological distress and possibly with an increased use of antidepressants. Multi-state models can be used to study a number of clinically meaningful research questions related to patterns of medication use such as the total probability of being or total length of stay in an antidepressant medication or discontinuation period over time. Methods: Different multi-state structures may correspond to different research questions and vary from simple ones, such as a single-event survival analysis setting, up to bidirectional and recurrent multi-state structures, with each structure having its own traits, advantages, interpretations, and limitations. There are also a number of factors to consider when applying a multi-state model such as choosing a time-scale for the transition rates, borrowing information across transitions and relaxing the proportional hazards assumption. We explore the use of different multi-state structures when studying antidepressant medication use patterns among women diagnosed with invasive breast cancer (n=18313) and age-matched cancer-free women (n=92454) from the Swedish population, using the information in Breast Cancer Data Base Sweden 2.0 (BCBaSe 2.0). Results: While each structure has its own utility, the more complex structures -bidirectional and recurrent– are able to take into account the intermittent nature of the prescribed medication data and deliver estimates about the total probability and total length of stay of an individual in an antidepressant medication status or discontinuation periods during their follow-up. Sensitivity analyses over different definitions of the medication cycle and different approaches when modelling the transition intensity rates (Markov, semi-Markov or mix of the two) show that the estimates of the complex structures are quite stable. However, even these structures present limitations that should be taken into account during the implementation of the models. Conclusions: In this study we provide insight into the use, interpretation and limitations of different multi-state structures when applied to prescribed medication data. The conclusions drawn provide a better understanding of the application of multi-state models in epidemiology.