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5 - Clinical trials in lymphoma

from Part I - LYMPHOMA OVERVIEW

Published online by Cambridge University Press:  05 March 2010

Thomas M. Habermann
Affiliation:
Division of Hematology, Mayo Clinic College of Medicine, Rochester, MN 55901, USA
Susan M. Geyer
Affiliation:
Division of Hematology, Mayo Clinic College of Medicine, Rochester, MN 55901, USA
Robert Marcus
Affiliation:
Addenbrooke's NHS Foundation Trust, Cambridge
John W. Sweetenham
Affiliation:
Case Western Reserve University, Ohio
Michael E. Williams
Affiliation:
University of Virginia
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Summary

INTRODUCTION

Clinical trials are the backbone of the development and advancement of therapeutic interventions. The pace of development of new agents, the costs of data management, inclusion of quality-of-life assessments, symptom-control assessments and the evaluation of biologic correlates all present significant challenges for the future development of new therapeutic regimens. As these therapeutic regimens become increasingly targeted and our understanding of lymphoma deepens, the identification and assessment of relevant endpoints, be they clinical (e.g. physical examination, radiologic) or biologic (e.g. immunologic, genetic, metabolic etc.), also becomes increasingly complex. In turn, the more complex our endpoints and the more targeted our regimens, the more challenges are presented when designing and conducting clinical trials and analyzing and interpreting their results. While the field of clinical trial design is too broad and widely discussed to adequately give full discussion in this chapter, the reader is referred to more general references on this area. Given the complexity of issues when designing, monitoring, interpreting and analyzing data for a clinical trial, statistician input and collaboration is of paramount importance. This chapter focuses on the principles behind and details of clinical trials from the clinician perspective. As such, we discuss the fundamental considerations when designing trials as well as outline the types of trials typically conducted in clinical research.

BACKGROUND

The practice and science of clinical trials and research is in its relative infancy. Until 1750, the thinking was that of Galen, who attained an authority that remained unchallenged.

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Publisher: Cambridge University Press
Print publication year: 2007

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References

Everitt, B. S.Medical Statistics from A to Z: a Guide for Clinicians and Medical Students (Cambridge: Cambridge University Press, 2003).Google Scholar
Friedman, L. M., Furberg, C. D. and DeMets, D. L.Fundamentals of Clinical Trials (New York, NY: Springer-Verlag, 1998).CrossRefGoogle Scholar
Green, S., Benedetti, J. and Crowley, J.Clinical Trials in Oncology (Boca Raton, FL: Chapman & Hall/CRC, 2003).Google Scholar
Hill, A. B.Memories of the British streptomycin trial in tuberculosis. Control. Clin. Trials 11 (1990), 77–79.CrossRefGoogle ScholarPubMed
Piantadosi, S.Clinical Trials: a Methodologic Perspective (New York, NY: Wiley, 1997).Google Scholar
Pocock, S. J.Clinical Trials: a Practical Approach (Chichester: Wiley, 1983).Google Scholar
Spilker, B.Guide to Clinical Trials (New York, NY: Raven Press, 1991).Google Scholar
Cheson, B. D., Pfistner, B., Juwied, M. E.et al. Revised response criteria for malignant lymphomas. From the members of the International Harmonization Project (IHP) of the Competence Network Malignant Lymphoma. Blood 106(S) (2005), 10a.Google Scholar
Fisher, R. I., Gaynor, E. R., Dahlberg, S.et al. Comparison of a standard regimen (CHOP) with three intensive chemotherapy regimens for advanced non-Hodgkin's lymphoma. N. Engl. J. Med. 328 (1993), 1002–1006.CrossRefGoogle ScholarPubMed
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Gordon, L. I., Andersen, J., Habermann, T. M.et al. Phase I trial of dose escalation with growth factor support in patients with previously untreated diffuse aggressive lymphomas: determination of the maximum tolerated dose of ProMACE–CytaBOM. J. Clin. Oncol. 14 (1996), 1275–1281.CrossRefGoogle ScholarPubMed
Habermann, T. M., Weller, E., Morrison, V. A.et al. Rituximab-CHOP versus CHOP alone or with maintenance rituximab in older patients with diffuse large B-cell lymphoma. J. Chin. Oncol. 24 (2006), 3121–3127.Google ScholarPubMed
A predictive model for aggressive non-Hodgkin's lymphoma. The International Non-Hodgkin's Lymphoma Prognostic Factors Project. N. Engl. J. Med. 329 (1993), 987–994.CrossRef
Solal-Céligny, P., Roy, P., Colombat, P.et al. Follicular lymphoma international index. Blood 104 (2004), 1258–1265.CrossRefGoogle Scholar
Bryant, J. and Day, R.Incorporating toxicity considerations into the design of two-stage phase II clinical trials. Biometrics 51 (1995), 1372–1383.CrossRefGoogle ScholarPubMed
Conaway, M. R. and Petroni, G. R.Bivariate sequential designs for phase II trials. Biometrics 51 (1995), 656–664.CrossRefGoogle ScholarPubMed
Green, S. and Dahlberg, S.Planned versus attained design in phase II clinical trials. Stat. Med. 11 (1992), 853–862.CrossRefGoogle ScholarPubMed
Hunsberger, S., Rubinstein, L. V., Dancey, J. and Korn, E. L.Dose escalation trial designs based on a molecularly targeted endpoint. Stat. Med. 24 (2005), 2171–2181.CrossRefGoogle ScholarPubMed
Liu, P.-Y., Dahlberg, S. and Crowley, J.Selection designs for pilot studies based on survival. Biometrics 49 (1993), 391–398.CrossRefGoogle ScholarPubMed
Sargent, D. J. and Goldberg, R. M.A flexible design for multiple armed screening trials. Stat. Med. 20 (2001), 1051–1060.CrossRefGoogle ScholarPubMed
Simon, R., Wittes, R. E. and Ellengerg, S. S.Randomized phase II clinical trials. Cancer Treat. Rep. 69 (1985), 1375–1381.Google ScholarPubMed
Thall, P. F. and Russel, K. E.A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. Biometrics 54 (1998), 251–264.CrossRefGoogle ScholarPubMed
Zhang, W., Sargent, D. J. and Mandrekar, S.An adaptive dose-finding design incorporating both toxicity and efficacy. Stat. Med. 25 (2006), 2365–2383.CrossRefGoogle ScholarPubMed
Cox, D. R.Regression models and life-tables. J. Roy. Stat. Soc. Ser. B 34 (1972), 187–200.Google Scholar
Kaplan, E. and Meier, P.Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (1958), 457–481.CrossRefGoogle Scholar
Lunceford, J. K., Davidian, M., Tsiatis, A. A.et al. Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials. Biometrics 58 (2002), 48–57.CrossRefGoogle ScholarPubMed
O'Brien, P. C. and Fleming, T. R.A multiple testing procedure for clinical trials. Biometrics 35 (1979), 549–556.CrossRefGoogle ScholarPubMed
Therneau, T. M. and Grambsch, P. M.Modeling Survival Data: Extending the Cox Model (New York, NY: Springer-Verlag, 2000).CrossRefGoogle Scholar
Everitt, B. S.Medical Statistics from A to Z: a Guide for Clinicians and Medical Students (Cambridge: Cambridge University Press, 2003).Google Scholar
Friedman, L. M., Furberg, C. D. and DeMets, D. L.Fundamentals of Clinical Trials (New York, NY: Springer-Verlag, 1998).CrossRefGoogle Scholar
Green, S., Benedetti, J. and Crowley, J.Clinical Trials in Oncology (Boca Raton, FL: Chapman & Hall/CRC, 2003).Google Scholar
Hill, A. B.Memories of the British streptomycin trial in tuberculosis. Control. Clin. Trials 11 (1990), 77–79.CrossRefGoogle ScholarPubMed
Piantadosi, S.Clinical Trials: a Methodologic Perspective (New York, NY: Wiley, 1997).Google Scholar
Pocock, S. J.Clinical Trials: a Practical Approach (Chichester: Wiley, 1983).Google Scholar
Spilker, B.Guide to Clinical Trials (New York, NY: Raven Press, 1991).Google Scholar
Cheson, B. D., Pfistner, B., Juwied, M. E.et al. Revised response criteria for malignant lymphomas. From the members of the International Harmonization Project (IHP) of the Competence Network Malignant Lymphoma. Blood 106(S) (2005), 10a.Google Scholar
Fisher, R. I., Gaynor, E. R., Dahlberg, S.et al. Comparison of a standard regimen (CHOP) with three intensive chemotherapy regimens for advanced non-Hodgkin's lymphoma. N. Engl. J. Med. 328 (1993), 1002–1006.CrossRefGoogle ScholarPubMed
Flinn, I. W., Kopecky, K. J., Foucar, K.et al. Long-term follow-up of remission duration, mortality, and second malignancies in hairy cell leukemia patients treated with pentostatin. Blood 96 (2000), 2981–2986.Google ScholarPubMed
Gordon, L. I., Andersen, J., Habermann, T. M.et al. Phase I trial of dose escalation with growth factor support in patients with previously untreated diffuse aggressive lymphomas: determination of the maximum tolerated dose of ProMACE–CytaBOM. J. Clin. Oncol. 14 (1996), 1275–1281.CrossRefGoogle ScholarPubMed
Habermann, T. M., Weller, E., Morrison, V. A.et al. Rituximab-CHOP versus CHOP alone or with maintenance rituximab in older patients with diffuse large B-cell lymphoma. J. Chin. Oncol. 24 (2006), 3121–3127.Google ScholarPubMed
A predictive model for aggressive non-Hodgkin's lymphoma. The International Non-Hodgkin's Lymphoma Prognostic Factors Project. N. Engl. J. Med. 329 (1993), 987–994.CrossRef
Solal-Céligny, P., Roy, P., Colombat, P.et al. Follicular lymphoma international index. Blood 104 (2004), 1258–1265.CrossRefGoogle Scholar
Bryant, J. and Day, R.Incorporating toxicity considerations into the design of two-stage phase II clinical trials. Biometrics 51 (1995), 1372–1383.CrossRefGoogle ScholarPubMed
Conaway, M. R. and Petroni, G. R.Bivariate sequential designs for phase II trials. Biometrics 51 (1995), 656–664.CrossRefGoogle ScholarPubMed
Green, S. and Dahlberg, S.Planned versus attained design in phase II clinical trials. Stat. Med. 11 (1992), 853–862.CrossRefGoogle ScholarPubMed
Hunsberger, S., Rubinstein, L. V., Dancey, J. and Korn, E. L.Dose escalation trial designs based on a molecularly targeted endpoint. Stat. Med. 24 (2005), 2171–2181.CrossRefGoogle ScholarPubMed
Liu, P.-Y., Dahlberg, S. and Crowley, J.Selection designs for pilot studies based on survival. Biometrics 49 (1993), 391–398.CrossRefGoogle ScholarPubMed
Sargent, D. J. and Goldberg, R. M.A flexible design for multiple armed screening trials. Stat. Med. 20 (2001), 1051–1060.CrossRefGoogle ScholarPubMed
Simon, R., Wittes, R. E. and Ellengerg, S. S.Randomized phase II clinical trials. Cancer Treat. Rep. 69 (1985), 1375–1381.Google ScholarPubMed
Thall, P. F. and Russel, K. E.A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. Biometrics 54 (1998), 251–264.CrossRefGoogle ScholarPubMed
Zhang, W., Sargent, D. J. and Mandrekar, S.An adaptive dose-finding design incorporating both toxicity and efficacy. Stat. Med. 25 (2006), 2365–2383.CrossRefGoogle ScholarPubMed
Cox, D. R.Regression models and life-tables. J. Roy. Stat. Soc. Ser. B 34 (1972), 187–200.Google Scholar
Kaplan, E. and Meier, P.Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (1958), 457–481.CrossRefGoogle Scholar
Lunceford, J. K., Davidian, M., Tsiatis, A. A.et al. Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials. Biometrics 58 (2002), 48–57.CrossRefGoogle ScholarPubMed
O'Brien, P. C. and Fleming, T. R.A multiple testing procedure for clinical trials. Biometrics 35 (1979), 549–556.CrossRefGoogle ScholarPubMed
Therneau, T. M. and Grambsch, P. M.Modeling Survival Data: Extending the Cox Model (New York, NY: Springer-Verlag, 2000).CrossRefGoogle Scholar

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