title: Linear mixed effects models.
abstract: Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for most methods described in previous chapters. In some experiments, the amount of random variation differs between experimental groups. In other experiments, there are multiple sources of variability, such as both between-subject variation and technical variation. As demonstrated in this chapter, linear mixed effects models provide a versatile and powerful framework in which to address research objectives efficiently and appropriately.
authors.affiliation: Division of Biostatistics, Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA.
authors.forename: Ann L
authors.initials: AL
authors.lastname: Oberg
authors.forename: Douglas W
authors.initials: DW
authors.lastname: Mahoney
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D015519:Bone Density / Q000502:physiology; D001943:Breast Neoplasms / Q000188:drug therapy; D003627:Data Interpretation, Statistical; D004452:Echocardiography; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D015337:Multicenter Studies as Topic / Q000379:methods; D010024:Osteoporosis / Q000175:diagnosis; D012107:Research Design; D013318:Stroke Volume / Q000502:physiology; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis
chemical_list: D000970:Antineoplastic Agents