Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.
Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.
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DOI：
10.1002/trc2.12286
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xiong C
中科院分区：
文献类型：
--
作者：
Wang G;Liu L;Li Y;Aschenbrenner AJ;Bateman RJ;Delmar P;Schneider LS;Kennedy RE;Cutter GR;Xiong C
Clinical trials for sporadic Alzheimer's disease generally use mixed models for repeated measures (MMRM) or, to a lesser degree, constrained longitudinal data analysis models (cLDA) as the analysis model with time since baseline as a categorical variable. Inferences using MMRM/cLDA focus on the between‐group contrast at the pre‐determined, end‐of‐study assessments, thus are less efficient (eg, less power). The proportional cLDA (PcLDA) and proportional MMRM (pMMRM) with time as a categorical variable are proposed to use all the post‐baseline data without the linearity assumption on disease progression. Compared with the traditional cLDA/MMRM models, PcLDA or pMMRM lead to greater gain in power (up to 20% to 30%) while maintaining type I error control. The PcLDA framework offers a variety of possibilities to model longitudinal data such as proportional MMRM (pMMRM) and two‐part pMMRM which can model heterogeneous cohorts more efficiently and model co‐primary endpoints simultaneously.
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