biostudies-literatureHart KRNIA NIH HHSNational Institute on Aging1118-1128https://www.ebi.ac.uk/biostudies/studies/S-EPMC7937764biostudies-literatureUnknown77(3)Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non-Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling assumptions and is computationally tractable. We show that this new method provides reliable estimates of the underlying population structure and is from 20 to 200 times faster than conventional methods when the longitudinal features are non-Gaussian. We apply the approach to explore subgroups among research participants in the early stages of neurodegeneration.BiometricsScalable and robust latent trajectory class analysis using artificial likelihood.PMC7937764P30 AG013846P30 AG028383R01 AG055634P30 AG008017P30 AG053760P50 AG005146P30 AG010133P50 AG033514P50 AG005142P50 AG005681P30 AG066518P50 AG047366P50 AG047266P30 AG072977P30 AG066444P30 AG019610P50 AG023501P30 AG008051P30 AG010129P30 AG013854P50 AG005138P50 AG008702P30 AG010124P30 AG012300P50 AG005134P50 AG025688P50 AG047270U01 AG016976P50 AG005136P30 AG035982P30 AG010161P50 AG005131P50 AG005133P30 AG049638P50 AG016574P30 AG066511U24 AG072122P50 AG016573Fei THanfelt JJHart KRfalseScalable and robust latent trajectory class analysis using artificial likelihood.Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non-Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling assumptions and is computationally tractable. We show that this new method provides reliable estimates of the underlying population structure and is from 20 to 200 times faster than conventional methods when the longitudinal features are non-Gaussian. We apply the approach to explore subgroups among research participants in the early stages of neurodegeneration.2021-01-01T00:00:00Z2021 Sep2025-04-22T12:37:46.749Z2025-02-19T01:41:31.089ZS-EPMC79377643289690110.1111/biom.13366