title: On the practical application of mixed effects models for repeated measures to clinical trial data.
abstract: The use of mixed effects models for repeated measures (MMRM) for clinical trial analyses has recently gained broad support as a primary analysis methodology. Some questions of practical implementation detail remain, however. For example, whether and how to incorporate clinical trial data that is collected at nonprotocol-specified timepoints or clinic visits has not been systematically studied. In this paper, we compare different methods for applying MMRM to trials wherein data is available at protocol-specified timepoints, as well as nonprotocol-specified timepoints due to patient early discontinuation. The methods under consideration included observed case MMRM, per protocol visits MMRM, interval last observation carried forward (LOCF) MMRM, and a hybrid of the per protocol visits and interval LOCF MMRM approaches. Simulation results reveal that the method that best controls the type I error rate is the per protocol visits method. This method is also associated with the least precision among the competing methods. Thus, in confirmatory clinical trials wherein control of type I error rates is critical, per protocol visits MMRM is recommended. However, in exploratory trials where strict type I error control is not as critical, one may prefer interval LOCF MMRM due to its increased precision. Points to consider with respect to both study design (e.g., assigning schedule of events) and subsequent analysis are offered.
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA. andersen_scott_w@lilly.com
authors.forename: Scott W
authors.initials: SW
authors.lastname: Andersen
authors.forename: Brian A
authors.initials: BA
authors.lastname: Millen
mesh_terms: D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D010352:Patient Dropouts; D013997:Time Factors; D016896:Treatment Outcome