title: Incomplete quality of life data in lung transplant research: comparing cross sectional, repeated measures ANOVA, and multi-level analysis.
abstract: BACKGROUND
  In longitudinal studies on Health Related Quality of Life (HRQL) it frequently occurs that patients have one or more missing forms, which may cause bias, and reduce the sample size. Aims of the present study were to address the problem of missing data in the field of lung transplantation (LgTX) and HRQL, to compare results obtained with different methods of analysis, and to show the value of each type of statistical method used to summarize data.
  
  
  METHODS
  Results from cross-sectional analysis, repeated measures on complete cases (ANOVA), and a multi-level analysis were compared. The scores on the dimension 'energy' of the Nottingham Health Profile (NHP) after transplantation were used to illustrate the differences between methods.
  
  
  RESULTS
  Compared to repeated measures ANOVA, the cross-sectional and multi-level analysis included more patients, and allowed for a longer period of follow-up. In contrast to the cross sectional analyses, in the complete case analysis, and the multi-level analysis, the correlation between different time points was taken into account. Patterns over time of the three methods were comparable. In general, results from repeated measures ANOVA showed the most favorable energy scores, and results from the multi-level analysis the least favorable. Due to the separate subgroups per time point in the cross-sectional analysis, and the relatively small number of patients in the repeated measures ANOVA, inclusion of predictors was only possible in the multi-level analysis.
  
  
  CONCLUSIONS
  Results obtained with the various methods of analysis differed, indicating some reduction of bias took place. Multi-level analysis is a useful approach to study changes over time in a data set where missing data, to reduce bias, make efficient use of available data, and to include predictors, in studies concerning the effects of LgTX on HRQL.
authors.affiliation: Office for Medical Technology Assessment, University Medical Center Groningen, The Netherlands. k.m.vermeulen@mta.umcg.nl
authors.forename: Karin M
authors.initials: KM
authors.lastname: Vermeulen
authors.forename: Wendy J
authors.initials: WJ
authors.lastname: Post
authors.forename: Mark M
authors.initials: MM
authors.lastname: Span
authors.forename: Wim
authors.initials: W
authors.lastname: van der Bij
authors.forename: Gerard H
authors.initials: GH
authors.lastname: Koëter
authors.forename: Elisabeth M
authors.initials: EM
authors.lastname: TenVergert
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D016477:Artifacts; D015982:Bias; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D015340:Epidemiologic Research Design; D005163:Factor Analysis, Statistical; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008171:Lung Diseases / Q000453:epidemiology* / Q000601:surgery*; D016040:Lung Transplantation / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011788:Quality of Life; D015203:Reproducibility of Results; D012106:Research; D018401:Sample Size; D012680:Sensitivity and Specificity