title: Impact of missing data due to selective dropouts in cohort studies and clinical trials.
abstract: BACKGROUND
  Many cohort studies and clinical trials use repeated measurements of laboratory markers to track disease progression and to evaluate new therapies. A major problem in the analysis of such studies is that marker data are censored in some patients owing to withdrawal, loss to follow-up, or death. The objective of this paper is to evaluate the impact of selective dropouts attributable to death or disease progression on the estimates of marker change among different groups.
  
  
  METHODS
  Data on CD4 cell count in human immunodeficiency virus 1-infected individuals from a clinical trial and a cohort study are used to illustrate this problem and a possible solution. Simulation studies are also presented.
  
  
  RESULTS
  When the rate of dropout is greater in subjects whose marker status is declining rapidly, commonly used methods, like random effects models, that ignore informative dropouts lead to overoptimistic statements about the marker trends in all compared groups, because subjects with steeper marker drops tend to have shorter follow-up times and hence are weighted less in the estimation of the group rate of the average marker decline.
  
  
  CONCLUSIONS
  The potential biases attributable to incomplete data require greater recognition in longitudinal studies. Sensitivity analyses to assess the effect of dropouts are important.
authors.affiliation: Medical Statistics Unit, London School of Hygiene and Tropical Medicine, London, UK. Gtouloum@atlas.uoa.gr
authors.forename: Giota
authors.initials: G
authors.lastname: Touloumi
authors.forename: Stuart J
authors.initials: SJ
authors.lastname: Pocock
authors.forename: Abdel G
authors.initials: AG
authors.lastname: Babiker
authors.forename: Janet H
authors.initials: JH
authors.lastname: Darbyshire
mesh_terms: D000293:Adolescent; D000328:Adult; D015982:Bias; D018791:CD4 Lymphocyte Count; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D016049:Didanosine / Q000627:therapeutic use; D015658:HIV Infections / Q000150:complications / Q000188:drug therapy / Q000276:immunology; D006467:Hemophilia A / Q000097:blood / Q000150:complications / Q000276:immunology; D006801:Humans; D007223:Infant; D016018:Least-Squares Analysis; D008137:Longitudinal Studies; D008875:Middle Aged; D008962:Models, Theoretical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
chemical_list: D016049:Didanosine