In medical research,latent subgroups often emerge with characteristics or trends distinct from the general population,yet identifying them directly remain challenging.The latent variable mixture modeling,grounded in the idea that a population consists of a limited mixture of subgroups,assigns latent categories to individuals based on posterior probabilities.This model is suitable for both cross-sectional and longitudinal datasets.Approaching from a statistical perspective,this paper thoroughly explicates the foundational principles of four prevalent methods within the latent variable mixture modeling realm,outlining the essential modeling workflow.By integrating insights from previous cases and real-world data,we review the rational applications of these methods.The latent variable mixture modeling stands as a flexible classification tool for identifying and analyzing latent categories within research populations,further facilitating the in-depth exploration of predictors influencing these latent categories and their consequent effects on outcome variables.
关键词
潜变量混合模型/潜在类别分析/增长混合模型/潜类别增长模型/临床研究
Key words
Latent variable mixture modeling/Latent class analysis/Growth mixture modeling/Latent class growth modeling/Clinical research
维普
万方数据