Age-at-onset subsets of bipolar I disorders: A critical insight into admixture analyses

Gaussian mixture analysis is frequently used to model the age-at-onset (AAO) in bipolar I disorder and identify homogeneous subsets of patients. This study aimed to examine whether, using admixture analysis of AAO, cross-sectional designs (which cause right truncation), unreliable diagnosis for individuals younger than 10 years old (which causes left truncation) and the selection criterion used for admixture analysis impact the number of identified subsets. A simulation study was performed. Different criteria - the likelihood ratio test (LRT), the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) - were compared using no, left and/or right truncation simulated data. The error rate of each criterion (percentage of erroneous number of detected subsets) was estimated. An application to two real databases, including 2,876 and 1,393 patients, is provided. Without data truncation and regardless of the distribution of AAO, the LRT and the AIC had much higher error rates (12% and 33%, respectively) than the BIC (1%). For a homogeneous population, the error rate increased with the introduction of left truncation. This study shows that the number of subsets identified using admixture analysis may depend on the sample size, the selection criterion, and the study design.