We present a three-step likelihood inference approach for the analysis of multidimensional longitudinal data when a large number of response variables are collected at each time occasion. We show the results obtained using this method to identify patients with different levels of Myasthenia Gravis severity and assess symptom progression over time.
Brusa, L., Pennoni, F., Bartolucci, F., Maggi, L. (2025). Dynamic Classification through Three-Step Estimation: Evidence from a Multinational Longitudinal Study of Myasthenia Gravis Patients. In E. di Bella, V. Gioia, C. Lagazio, S. Zaccarin (a cura di), Statistics for Innovation II. SIS 2025, Short Papers, Contributed Sessions 1 (pp. 270-276). Springer [10.1007/978-3-031-96303-2_44].
Dynamic Classification through Three-Step Estimation: Evidence from a Multinational Longitudinal Study of Myasthenia Gravis Patients
Brusa, L
;Pennoni, F;
2025
Abstract
We present a three-step likelihood inference approach for the analysis of multidimensional longitudinal data when a large number of response variables are collected at each time occasion. We show the results obtained using this method to identify patients with different levels of Myasthenia Gravis severity and assess symptom progression over time.
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