mmrm: Mixed Models for Repeated Measures

作者：Daniel Sabanés Bové, Liming Li, Julia Dedic, Doug Kelkhoff, Kevin Kunzmann, B. Lang, Christian Stock, Ya Wang, Dan James, Jonathan Sidi, Daniel Leibovitz, Daniel D. Sjoberg, Nikolas Ivan Krieger, Arryn Panagos, Jeremiah Jones · 年份：2022 · DOI：10.32614/cran.package.mmrm · 被引用次数：12 · 研究领域：Statistical Methods and Bayesian Inference、Statistical Methods and Inference、Probability and Risk Models

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) < doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E > for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) < doi:10.1177/009286150804200402 > for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.