Pattern mixture models for longitudinal quality of life studies in advanced stage disease

Donna K. Pauler, Sheryl McCoy, Carol Moinpour

Research output: Contribution to journalReview articlepeer-review

67 Scopus citations

Abstract

Analyses of longitudinal quality of life (QOL) for patients with advanced stage disease are frequently plagued by problems of non-random drop-out attributable to deteriorating health and/or death. As an example, Moinpour et al. cite specific challenges which limited their report and assessment of QOL for patients treated for advanced stage colorectal cancer in a clinical trial of several chemotherapeutic regimes performed by the Southwest Oncology Group. A particular source of confusion that arises in studies of advanced stage disease is whether or not to differentiate loss of follow-up due to death from drop-out where the patient is still alive but has dropped from the study. In this paper we examine exploratory data techniques for longitudinal QOL data with non-random missingness due to drop-out and censorship by death. We propose a pattern mixture model for longitudinal QOL, time of drop-out and survival, which allows for straightforward implementation of sensitivity analyses and explicit comparisons to the raw data. Our method is illustrated in the context of analysing the data and addressing the challenges posed by Moinpour et al.

Original languageEnglish (US)
Pages (from-to)795-809
Number of pages15
JournalStatistics in Medicine
Volume22
Issue number5
DOIs
StatePublished - Mar 15 2003

Keywords

  • Censorship
  • Non-ignorable missing data
  • Pattern mixture model
  • Quality of life

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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