The impact of missing data on estimation of health-related quality of life outcomes: An analysis of a randomized longitudinal clinical trial

Hongyan Du*, Elizabeth A. Hahn, David Cella

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Missing responses for health-related quality of life (HRQL) outcomes are common in clinical trials and may introduce bias as such data are often not missing at random. To evaluate the missingness (dropout) effect when comparing two treatment groups in a longitudinal randomized trial, we analyzed the Functional Assessment of Cancer Therapy Trial Outcome Index (TOI) change over 12 months for newly diagnosed patients with chronic myeloid leukemia. HRQL assessment was expected at baseline and months 1, 2, 3, 4, 5, 6, 9 and 12. We defined completers as those with baseline and month 12 TOI, and dropouts as all others as long as they had a baseline score. We defined censoring time as the time interval between baseline and the scheduled month 12 visit dates and approximate time-to-dropout as the time interval from baseline to the midpoint between date of the last reported TOI and the scheduled next visit date. A mixed-effects model was first built to assess treatment effect; a pattern-mixture model and a joint model were then built to account for non-ignorable dropout. Intermittent missing data were assumed to be missing at random. A square root transformation of TOI scores was taken to fulfill the normality and homogeneity assumption at each time point in all the models. The mixed-effects model revealed significant (P < 0.001) between-group differences at each visit except for baseline. The joint model generated similar parameter estimates as the separate longitudinal and survival sub-models with a significant association parameter (P = 0.039) indicating negative association between slope of TOI and hazard of dropout and thus non-ignorable dropout. The pattern-mixture model parameter estimates were fairly similar to those generated from the joint model. When non-ignorable missing data exist in longitudinal studies, a joint model is useful to quantify the relationship between dropout and outcome. In addition, it is important to examine underlying assumptions and utilize multiple missing data models including the pattern mixture model to assess sensitivity of model based inference to assumptions about missing mechanisms.

Original languageEnglish (US)
Pages (from-to)134-144
Number of pages11
JournalHealth Services and Outcomes Research Methodology
Issue number3-4
StatePublished - Dec 2011


  • Joint modeling
  • Longitudinal study
  • Missing data
  • Mixed-effects model
  • Pattern mixture model
  • Time-to-dropout
  • Treatment effectiveness

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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