Key result
Joint modeling of longitudinal and time-to-event data provided different conclusions compared to the time-dependent Cox model for predicting neoaortic valve regurgitation.
Why the study?
While time-dependent Cox models are commonly used for repeated measurements with time-to-event outcomes, newer joint modeling frameworks offer multiple advantages for evaluating longitudinal markers against clinical events.
Joint modeling of longitudinal and time-to-event data offers advantages over traditional time-dependent Cox models, including dynamic individualized predictions for patients with congenital heart disease.
Joint models may enable dynamic predictions in congenital heart disease; leaves open whether they outperform Cox models for clinical decisions.
BACKGROUND: Most patients with congenital heart disease survive into adulthood; however, residual abnormalities remain and management of the patients is life-long and personalized. Patients with surgical repair of transposition of the great arteries, for example, face the risk to develop neoaortic valve regurgitation. Cardiologists update the prognosis of the patient intuitively with updated information of the cardiovascular status of the patient, for instance from echocardiographic imaging. METHODS: Usually a time-dependent version of the Cox model is used to analyze repeated measurements with a time-to-event outcome. New statistical methods have been developed with multiple advantages, of which the most prominent one being the joint model for longitudinal and time-to-event outcome. In this tutorial, the joint modeling framework is introduced and applied to patients with transposition of the great arteries after surgery with a long-term follow-up, where repeated echocardiographic values of the neoaortic root are evaluated against the risk of neoaortic valve regurgitation. RESULTS: The data are analyzed with the time-dependent Cox model as benchmark method, and the results are compared with a joint model, leading to different conclusions. The flexibility of the joint model is shown by adding the growth rate of the neoaortic root to the model and adding repeated values of body surface area to obtain a multimarker model. Lastly, it is demonstrated how the joint model can be used to obtain personalized dynamic predictions of the event. CONCLUSIONS: The joint model for longitudinal and time-to-event data is an attractive method to analyze data in follow-up studies with repeated measurements. Benefits of the method include using the estimated natural trajectory of the longitudinal outcome, great flexibility through multiple extensions, and dynamic individualized predictions.
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Baart et al. (2021) studied Surgical repair of transposition of the great arteries. Joint modeling of longitudinal markers and time-to-event outcomes vs. Time-dependent Cox model was evaluated on Neoaortic valve regurgitation. Joint modeling of longitudinal and time-to-event data provided different conclusions compared to the time-dependent Cox model for predicting neoaortic valve regurgitation.
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