Quality of Life Research

, Volume 20, Issue 3, pp 371–381 | Cite as

Assessing longitudinal quality of life in prostate cancer patients and their spouses: a multilevel modeling approach

  • Lixin Song
  • Laurel L. Northouse
  • Thomas M. Braun
  • Lingling Zhang
  • Bernadine Cimprich
  • David L. Ronis
  • Darlene W. Mood
Article

Abstract

Purpose

This study aimed at examining the relationship between quality of life (QOL) in prostate cancer (PCa) patients and partners and how baseline demographics, cancer-related factors, and time-varying psychosocial and symptom covariates affect their QOL over time.

Methods

Guided by a modified Stress-Coping Model, this study used multilevel modeling to analyze longitudinal data from a randomized clinical trial that tested a family-based intervention to improve QOL in couples managing PCa. Patients and partners from the usual-care control group (N = 134 dyads) independently completed the measurements at baseline, and at 4-, 8-, and 12-month follow-ups.

Results

Correlations of QOL between patients and partners over time were small to moderate. Patients’ lower education level, partners’ older age, higher family income, and localized cancer at baseline were associated with better QOL in couples. Over time, couples’ QOL improved as their social support and cancer-related dyadic communication increased and as couples’ uncertainty, general symptoms, and patients’ prostate cancer-related sexual and hormonal symptoms decreased.

Conclusions

Evidence indicates that couples’ QOL during cancer survivorship is affected by multiple contextual factors (e.g., baseline demographics and time-varying psychosocial factors and symptoms). Intervention research is needed to explore comprehensive strategies to improve couples’ QOL during the continuum of PCa survivorship.

Keywords

Prostate cancer Symptom Family Quality of life Multilevel model Communication Uncertainty 

Abbreviations

PCa

Prostate cancer

QOL

Quality of life

MLM

Multilevel modeling

Notes

Acknowledgments

The authors gratefully acknowledge the expert guidance and contributions of Mr. Brady West at the University of Michigan Center for Statistical Consultation and Research, Drs. George Knafl and Merle Mishel at the University of North Carolina-Chapel Hill School of Nursing. The study in this report was funded in part by grant F31NR010990 from the National Institute of Nursing Research (L. Song, Prinicipal Investigator) and R01CA10738 from National Cancer Institute (L. Northouse, Principal Investigator). L. Song is currently supported by a postdoctoral training grant at the University of North Carolina School of Nursing (5T32NR007091, M. Mishel, Principal Investigator).

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Lixin Song
    • 1
  • Laurel L. Northouse
    • 2
  • Thomas M. Braun
    • 3
  • Lingling Zhang
    • 4
  • Bernadine Cimprich
    • 2
  • David L. Ronis
    • 2
    • 5
  • Darlene W. Mood
    • 6
  1. 1.University of North Carolina-Chapel Hill School of NursingChapel HillUSA
  2. 2.University of Michigan School of NursingAnn ArborUSA
  3. 3.University of Michigan School of Public HealthAnn ArborUSA
  4. 4.University of Michigan, Center for Statistical Consultation and ResearchAnn ArborUSA
  5. 5.VA Ann Arbor Healthcare System, Health Services Research & DevelopmentAnn ArborUSA
  6. 6.College of NursingWayne State UniversityDetroitUSA

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