Skip to main content
SpringerLink
Log in

Chemotherapeutic impact on pain and global health-related quality of life in hormone-refractory prostate cancer: Dynamically Modified Outcomes (DYNAMO) analysis of a randomized controlled trial

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

This paper applies the Dynamically Modified Outcomes (DYNAMO) model to a clinical trial of two chemotherapeutic regimens on global health-related quality of life (GHRQL) in hormone-refractory prostate cancer.

Methods

DYNAMO identifies the causal influences operating in a clinical trial and their mediation, moderation, and modulation by uncontrolled variables. The Southwest Oncology Group trial S9916 randomized assignment to mitoxantrone plus prednisone (M + P) versus docetaxel plus estramustine (D + E) treatments. In this application, we examine baseline-adjusted impacts of worst pain (McGill Pain Questionnaire) on GHRQL (EORTC Quality of Life Questionnaire-C30) at 10 weeks.

Results

The average treatment levels of pain did not differ, hence, the average mediated effect of treatment on GHRQL was zero. Nonetheless, M + P reduced the impact (the relational outcome) of pain on GHRQL by 54% relative to D + E. Individual variation in the relational outcome (modulation) was of the same magnitude as the average difference between the groups. Performance status moderated the direct effects of treatment, with D + E being more effective in good, but not poor, performance strata.

Conclusions

The DYNAMO approach comprehensively accounted for treatment effects. Rather than a single average effect, there were three distinct treatment effects: one direct effect for each performance status level and a direct effect on the relationship between pain and GHRQL.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. These standard adjustments have no effect on the interpretation of the key features of the model, but merely condition responses on baseline values.

  2. In this and all statements drawn from Figs. 3 and 4, the conclusions about therapeutic impact pertain to receiving one therapy instead of the other. The attributed causal impacts are relative, not absolute.

Abbreviations

DYNAMO:

Dynamically Modified Outcomes

M + P:

Mitoxantrone plus prednisone

D + E:

Docetaxel plus estramustine

GHRQL:

Global health-related quality of life

SWOG:

Southwest Oncology Group

PS:

Performance status (0 = fully active; 1 = restricted in physically strenuous activity, but ambulatory and able to do light work; 2 = ambulatory and capable of self-care, but unable to carry out any work activities; 3 = capable of limited self-care, confined to bed or chair for more than 50% of waking hours; 4 = completely disabled)

MPQ:

McGill Pain Questionnaire

EORTC QLQ-C30 PR25:

European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 and Prostate Cancer Module

DCE:

Direct causal effect

ACE:

Average causal effect

ICE:

Individual causal effect

References

  1. Donaldson, G. W. (2003). General linear contrasts on latent variable means: Structural equation hypothesis tests for multivariate clinical trials. Statistics in Medicine, 22, 2893–2917. doi:10.1002/sim.1558.

    Article  PubMed  Google Scholar 

  2. Donaldson, G. W., & Moinpour, C. M. (2002). Individual differences in quality-of-life treatment response. Medical Care, 40(6 Suppl), III39–III53. doi:10.1097/00005650-200206001-00007.

    PubMed  Google Scholar 

  3. Moinpour, C. M., Donaldson, G. W., & Redman, M. W. (2007). Do general dimensions of quality of life add clinical value to symptom data? Journal of the National Cancer Institute. Monographs, 37, 31–38. doi:10.1093/jncimonographs/lgm007.

    Article  PubMed  Google Scholar 

  4. Petrylak, D. P., Tangen, C. M., Hussein, M. H. A., Lara, P. N., Jr., Jones, J. A., Taplin, M. E., et al. (2004). Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer. The New England Journal of Medicine, 351(15), 1513–1520. doi:10.1056/NEJMoa041318.

    Article  PubMed  CAS  Google Scholar 

  5. Berry, D. L., Moinpour, C. M., Jiang, C. S., Ankerst, D. P., Petrylak, D. P., Vinson, L. V., et al. (2006). Quality of life and pain in advanced stage prostate cancer: Results of a Southwest Oncology Group randomized trial comparing docetaxel and estramustine to mitoxantrone and prednisone. Journal of Clinical Oncology, 24(18), 2828–2835. doi:10.1200/JCO.2005.04.8207.

    Article  PubMed  CAS  Google Scholar 

  6. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.

    Article  PubMed  CAS  Google Scholar 

  7. Curran, P. J., & Hussong, A. M. (2003). The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology, 112(4), 526–544. doi:10.1037/0021-843X.112.4.526.

    Article  PubMed  Google Scholar 

  8. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling changes and event occurrence. New York: Oxford University Press.

    Google Scholar 

  9. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford Press.

    Google Scholar 

  10. Sinibaldi, V. J., Carducci, M. A., Moore-Cooper, S., Laufer, M., Zahurak, M., & Eisenberger, M. A. (2002). Phase II evaluation of docetaxel plus one-day oral estramustine phosphate in the treatment of patients with androgen independent prostate carcinoma. Cancer, 94(5), 1457–1465. doi:10.1002/cncr.10350.

    Article  PubMed  CAS  Google Scholar 

  11. Tannock, I. F., Osoba, D., Stockler, M. R., Ernst, D. S., Neville, A. J., Moore, M. J., et al. (1996). Chemotherapy with mitoxantrone plus prednisone or prednisone alone for symptomatic hormone-resistant prostate cancer: A Canadian randomized trial with palliative end points. Journal of Clinical Oncology, 14(6), 1756–1764.

    PubMed  CAS  Google Scholar 

  12. Melzack, R. (1987). The short-form McGill pain questionnaire. Pain, 30, 191–197. doi:10.1016/0304-3959(87)91074-8.

    Article  PubMed  CAS  Google Scholar 

  13. Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365–376. doi:10.1093/jnci/85.5.365.

    Article  PubMed  CAS  Google Scholar 

  14. Fayers, P. M., Aaronson, N. K., Bjordal, K., Groenvold, M., Curran, D., Bottomley, A., et al. (2001). EORTC QLQ-C30 scoring manual. Brussels: EORTC.

    Google Scholar 

  15. Borghede, G., & Sullivan, M. (1996). Measurement of quality of life in localized prostatic cancer patients treated with radiotherapy. Development of a prostate cancer-specific module supplementing the EORTC QLQ-C30. Quality of Life Research, 5, 212–222. doi:10.1007/BF00434743.

    Article  PubMed  CAS  Google Scholar 

  16. Muthén, L. K., & Muthén, B. (1998–2005). Mplus user’s guide. Los Angeles: Muthén & Muthén.

  17. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.

    Google Scholar 

  18. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. doi:10.1037/0022-3514.51.6.1173.

    Article  PubMed  CAS  Google Scholar 

  19. Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 9, 602–618. doi:10.1177/0193841X8100500502.

    Article  Google Scholar 

  20. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. doi:10.1146/annurev.psych.58.110405.085542.

    Article  PubMed  Google Scholar 

  21. Albert, J. M. (2008). Mediation analysis via potential outcomes models. Statistics in Medicine, 27(8), 1282–1304. doi:10.1002/sim.3016.

    Article  PubMed  Google Scholar 

  22. Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59(10), 877–883. doi:10.1001/archpsyc.59.10.877.

    Article  PubMed  Google Scholar 

  23. Kraemer, H. C., Lowe, K. K., & Kupfer, D. J. (2005). To your health: How to understand what research tells us about risk. New York: Oxford University Press.

    Google Scholar 

  24. Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. The American Journal of Psychiatry, 158(6), 848–856. doi:10.1176/appi.ajp.158.6.848.

    Article  PubMed  CAS  Google Scholar 

  25. Edwards, D. (1995). Introduction to graphical modeling. New York: Springer-Verlag.

    Google Scholar 

  26. Edwards, D. (1995). MIM release 3.2. Boston: Free Software Foundation.

Download references

Acknowledgments

The authors would like to thank the patients who contributed HRQL data to S9916 and the Clinical Research Associates at the Southwest Oncology Group institutions who monitored the submission of the HRQL forms. We recognize the contributions of Dr. Donna L. Berry, the HRQL Study Coordinator for S9916, and Dr. Daniel P. Petrylak, the therapeutic trial study coordinator.

Funding sources: This investigation was supported in part by the following PHS Cooperative Agreement grant numbers awarded by the National Cancer Institute, DHHS: CA38926, CA32102, CA37135, CA25224, CA46441, CA37981, CA45808,CA27057, CA12644, CA68183, CA22433, CA35261, CA58861, CA20319, CA46113, CA58882, CA76447, CA04919, CA16385, CA35090, CA03096, CA67663, CA45450, CA35431, CA45807, CA58416, CA14028, CA45377, CA63845, CA42777, CA46136, CA11083, CA35119, CA58658, CA46282, CA76129, CA46368, CA35176, CA86780, CA46462, CA35192, CA35178, CA67575, CA63844, CA12213, CA74647, CA35128, CA35996, CA58686, CA13612, CA45461, CA58723, CA63848, CA35281, CA63850, CA76132, CA74811, and supported in part by Aventis.

Author information

Authors and Affiliations

  1. Southwest Oncology Group Statistical Center, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Carol M. Moinpour

  2. Pain Research Center, Department of Anesthesiology, University of Utah, Salt Lake City, UT, USA

    Gary W. Donaldson & Yoshio Nakamura

Authors
  1. Carol M. Moinpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gary W. Donaldson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshio Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carol M. Moinpour.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moinpour, C.M., Donaldson, G.W. & Nakamura, Y. Chemotherapeutic impact on pain and global health-related quality of life in hormone-refractory prostate cancer: Dynamically Modified Outcomes (DYNAMO) analysis of a randomized controlled trial. Qual Life Res 18, 147–155 (2009). https://doi.org/10.1007/s11136-008-9433-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-008-9433-3

Keywords

Search

Navigation