Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach

Abstract

AbstractBackgroundResearchers have suggested that longitudinal trajectories of mammographic breast density (MD) can be used to understand changes in breast cancer (BC) risk over a woman’s lifetime. Some have suggested, based on biological arguments, that the cumulative trajectory of MD encapsulates the risk of BC across time. Others have tried to connect changes in MD to the risk of BC.MethodsTo summarize the MD–BC association, we jointly model longitudinal trajectories of MD and time to diagnosis using data from a large ($$N = 40{,}087$$N=40,087) mammography cohort of Swedish women aged 40–80 years. Five hundred eighteen women were diagnosed with BC during follow-up. We fitted three joint models (JMs) with different association structures; Cumulative, current value and slope, and current value association structures.ResultsAll models showed evidence of an association between MD trajectory and BC risk ($$P < 0.001$$P<0.001for current value of MD,$$P < 0.001$$P<0.001and$$P =0.005$$P=0.005for current value and slope of MD respectively, and$$P < 0.001$$P<0.001for cumulative value of MD). Models with cumulative association structure and with current value and slope association structure had better goodness of fit than a model based only on current value. The JM with current value and slope structure suggested that a decrease in MD may be associated with an increased (instantaneous) BC risk. It is possible that this is because of increased screening sensitivity rather than being related to biology.ConclusionWe argue that a JM with a cumulative association structure may be the most appropriate/biologically relevant model in this context.