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Incomplete quality of life data in randomized trials: missing forms

D Curran1, G Molenberghs, P M Fayers

  • 1European Organization for Research and Treatment of Cancer (EORTC) Data Center, Brussels, Belgium. dcu@eortc.be

Statistics in Medicine
|April 29, 1998
PubMed
Summary

Handling missing quality of life (QOL) data is complex. This paper reviews methods for incomplete longitudinal QOL data analysis, addressing bias and efficiency concerns.

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Area of Science:

  • Biostatistics
  • Health Outcomes Research
  • Longitudinal Data Analysis

Background:

  • Analyzing quality of life (QOL) data presents challenges including repeated measures, categorical responses, multidimensional scales, and missing data.
  • Integrating QOL with length of life adds further complexity.
  • Missing data in QOL research can introduce bias and reduce statistical efficiency.

Purpose of the Study:

  • To provide an overview of methods for analyzing incomplete longitudinal QOL data.
  • To consolidate approaches from both QOL and missing data literature.
  • To highlight the importance of addressing bias and conducting sensitivity analyses.

Main Methods:

  • Review of analytical methods for incomplete longitudinal QOL data.
  • Categorization of methods including complete case, available case, summary measures, imputation, and likelihood-based approaches.

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  • Discussion of bias and the necessity of sensitivity analyses.

    • Main Results:

    • Several methods exist for handling missing QOL data, but no single standard method is established.
    • Common approaches include complete case analysis, available case analysis, summary measures, imputation techniques, and likelihood-based methods.
    • The choice of method impacts potential bias and statistical power.

    Conclusions:

    • Effective analysis of longitudinal QOL data requires careful consideration of missing data.
    • A range of statistical methods are available, each with implications for bias and efficiency.
    • Sensitivity analyses are crucial to assess the robustness of findings to missing data assumptions.