Tackling Statistical Challenges in Longitudinal Analysis of Idiopathic CD4 Lymphocytopenia

Idiopathic CD4 lymphocytopenia (ICL) is a rare, enigmatic syndrome characterized by CD4 lymphopenia in the absence of any known cause of immunodeficiency. This talk addresses statistical considerations in modeling a natural history ICL study, which aimed to evaluate the clinical, genetic, immunologic, and prognostic characteristics of 91 patients with ICL over an 11- year period. I will discuss statistical challenges in modeling a rich and complex dataset including the handling of longitudinal measurements via linear mixed-models to evaluate T-cell count trajectories, and logistic regression to assess the prognostic value of T-cell counts, using tertiles derived from baseline measurements. Multiple outputation was applied to the logistic regression to handle the longitudinal nature of the data, allowing us to use methods designed for independent data in a repeated-measures context. Additionally, we compared the observed mortality and cancer prevalence in the ICL cohort to expected rates derived from the general
population in the Surveillance, Epidemiology, and End Results (SEER) Program. Our findings revealed that ICL continued to be associated with increased susceptibility to viral, encapsulated fungal, and mycobacterial diseases, as well as with a reduced response to novel antigens and an increased risk of cancer.