title: Predicting missing biomarker data in a longitudinal study of Alzheimer disease.
abstract: OBJECTIVE
  To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).
  
  
  METHODS
  The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005-2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.
  
  
  RESULTS
  CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ(42). Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.
  
  
  CONCLUSIONS
  The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD.
authors.affiliation: Division of Epidemiology, University of California, Berkeley, USA. rlo@berkeley.edu
authors.forename: Raymond Y
authors.initials: RY
authors.lastname: Lo
authors.forename: William J
authors.initials: WJ
authors.lastname: Jagust
mesh_terms: D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000235:genetics; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid; D015415:Biomarkers / Q000134:cerebrospinal fluid*; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000235:genetics; D015331:Cohort Studies; D015140:Dementia, Vascular / Q000134:cerebrospinal fluid / Q000175:diagnosis / Q000235:genetics; D019788:Fluorodeoxyglucose F18; D006710:Homocysteine / Q000097:blood; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D009483:Neuropsychological Tests; D010352:Patient Dropouts; D010446:Peptide Fragments / Q000134:cerebrospinal fluid; D049268:Positron-Emission Tomography; D012107:Research Design; D012307:Risk Factors; D016875:tau Proteins / Q000134:cerebrospinal fluid
chemical_list: D016229:Amyloid beta-Peptides; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42); D016875:tau Proteins; D006710:Homocysteine; D019788:Fluorodeoxyglucose F18