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To properly account for dependence between longitudinal observations, statistical methods for high-dimensional linear mixed models (LMMs) have been developed. However, few packages implementing these high-dimensional LMMs are available in the statistical software . Additionally, some packages suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. We illustrate Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies evaluating fixed and random effects estimation along with computation time. A real-world example is provided using data from a study of lupus in children with 15,424 genetic and clinical predictors. Whereas it is computationally prohibitive to fit other LMMs to this data, LMM-PROBE successfully identifies genes and clinical factors associated with a new lupus biomarker and predicts it over time. Supplementary materials are available online.<\/jats:p>","DOI":"10.1007\/s11222-025-10649-z","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T06:55:57Z","timestamp":1748847357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm"],"prefix":"10.1007","volume":"35","author":[{"given":"Anja","family":"Zgodic","sequence":"first","affiliation":[]},{"given":"Ray","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Jiajia","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Olejua","sequence":"additional","affiliation":[]},{"given":"Alexander C.","family":"McLain","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"issue":"47","key":"10649_CR1","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1080\/02664763.2019.1657814","volume":"5","author":"O Ariyo","year":"2020","unstructured":"Ariyo, O., Quintero, A., Munoz, J., Verbeke, G., Lesaffre, E.: Bayesian model selection in linear mixed models for longitudinal data. 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Supplementary Material: The Supplementary Material contains the proofs of Propositions 1 and 2, convergence assessments of LMM-PROBE, and additional results from simulations and real data analyses. (.pdf); 2. R package: An R package lmmprobe, with data from the example, also available on Github (Zgodic et\u00a0al. ).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplementary information"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"Code is available in the R package lmmprobe.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"109"}}