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Pattern Mixture Models for Longitudinal Quality of Life Studies in Advanced Stage Disease by Donna K. Pauler; Sheryl McCoy; Carol Moinpour is a Mathematics article available to read on EtoBox.

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## 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__. Copyright © 2003 John Wile

Who reads Pattern Mixture Models for Longitudinal Quality of Life Studies in Advanced Stage Disease?

Author

Donna K. Pauler; Sheryl McCoy; Carol Moinpour

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2003

Language

EN

Field

Mathematics (Physical Sciences)