Correspondence
4. Dodd MJ, Miaskowski C, Lee KA: Occurrence of symptom clusters. J Natl Cancer Inst Monogr 76-78, 2004 5. Oh HS, Seo WS: Systematic review and meta-analysis of the correlates of cancer-related fatigue. Worldviews Evid Based Nurs 8:191-201, 2011 6. Stone P, Hardy J, Broadley K, et al: Fatigue in advanced cancer: A prospective controlled cross-sectional study. Br J Cancer 79:1479-1486, 1999
7. Yennu S, Urbauer DL, Bruera E: Factors associated with the severity and improvement of fatigue in patients with advanced cancer presenting to an outpatient palliative care clinic. BMC Palliat Care 11:16, 2012
DOI: 10.1200/JCO.2013.52.4363; published online ahead of print at www.jco.org on November 12, 2013
■ ■ ■
Reply to R.J. Chan We thank Chan1 for his thoughtful correspondence regarding our randomized controlled trial of methylphenidate and/or a nursing telephone intervention for fatigue in patients with advanced cancer.2 We agree with his comments regarding the need for more research on the role of telephone interventions and underpowering as a limitation of our negative findings. These issues were mentioned in our article. There are no major reasons to suspect that in patients with advanced cancer (defined as locally advanced and/or metastatic), the mechanisms of fatigue differ dramatically according to primary tumor site or that some subgroups with different biologic characteristics are more or less likely to respond to a telephone intervention. It is more likely that the opportunities to identify subgroups of patients who are likely to benefit more from a telephone intervention are related to some sociodemographic characteristics or to the presence of different symptom clusters of physical and/or emotional characteristics. For example, it might be possible that patients with a more significant contribution of mood to their fatigue expression might show a different response to a nursing telephone intervention. We agree with Chan1 about the importance of the associations between symptoms, and it is also possible that patients with higher levels of overall symptom expression might be more likely to benefit from telephone interventions.
We also agree with Chan1 that it is important in future research to establish if simple interventions conducted by individuals with a lower level of training might be as effective as more expensive nurse- or midlevel practitioner– based interventions.
Eduardo Bruera and Sriram Yennurajalingam The University of Texas MD Anderson Cancer Center, Houston, TX
ACKNOWLEDGMENT
E.B. is supported in part by National Institutes of Health National Institute of Nursing Research Grant No. RO1NR010162-01A1. S.Y. is supported in part by American Cancer Society Grant No. RSG-11-170-01-BCM (entitled “Research Scholar Grant for Independent Investigators Multimodal Study for Cancer Related Fatigue”). AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The author(s) indicated no potential conflicts of interest. REFERENCES 1. Chan RJ: Do nursing interventions targeting concurrent symptoms hold promise for managing fatigue in patients with advanced cancer? J Clin Oncol 32:853-854, 2014 2. Bruera E, Yennurajalingam S, Palmer JL, et al: Methylphenidate and/or a nursing telephone intervention for fatigue in patients with advanced cancer: A randomized, placebo-controlled, phase II trial. J Clin Oncol 31:2421-2427, 2013
DOI: 10.1200/JCO.2013.52.9412; published online ahead of print at www.jco.org on November 12, 2013
■ ■ ■
Some Practical Considerations for Phase III Studies With Biomarker Evaluations TO THE EDITOR: When a biomarker’s ability to identify patients benefitting from a targeted therapy is not fully established, it is valuable to evaluate treatment benefit in a phase III study in the biomarker-positive (B⫹) and biomarker-negative (B–) subgroups. In a recent article in Journal of Clinical Oncology, Freidlin et al1 detailed several important considerations for such scenarios and compare possible design options. We would like to raise a few additional considerations to complement their discussion and share some of our practical experiences from implementing phase III studies of buparlisib in patients with advanced and metastatic breast cancer (BELLE-2 [ClinicalTrials.gov ID: NCT01610284], BELLE-3 [ClinicalTrials.gov ID: NCT01633060], and BELLE-4 [ClinicalTrials.gov ID: NCT01572727]). Role of B– Freidlin et al1 are concerned with designs that test only the B⫹ and overall populations; they can erroneously recommend treatment 854
© 2014 by American Society of Clinical Oncology
for B– as the result of an overall clinical benefit driven by a strong B⫹ treatment effect. Given that the B– subgroup is not the target subgroup of interest in such designs, we suggest avoiding a formal test for treatment effect in B– patients, using Bayesian decision tools instead to assess whether a prespecified, clinically relevant threshold is achieved. More specifically, we can avoid spending any significance level for B– by using the graphical test procedures from Bretz et al2 in the populations of interest. We can ensure that a clinically relevant effect in B– is achieved by requiring, for example, a minimum 90% posterior probability for the hazard ratio in the B– group to be superior to a prespecified threshold. Combining these two strategies maximizes the power to detect a treatment effect in the populations of interest while simultaneously allowing assessment of the B– effect. Adequate Test Procedures for Complex Trial Objectives Recently, graphical approaches have been proposed that visualize decision strategies in an easily communicable way and tailor advanced multiple test procedures to complex trial objectives such as assessing treatment benefit for multiple end points in more than one population. Freidlin et al1 discuss several designs that can be visualized and extended using this approach. In practice, clinical considerations need JOURNAL OF CLINICAL ONCOLOGY
Information downloaded from jco.ascopubs.org and provided by at Thammasat University Libraries on October 4, 2014 from Copyright © 2014 American Society of Clinical Oncology. All rights reserved. 203.131.222.3
Correspondence
H1
0.25
In summary, Freidlin et al1 give an excellent overview of different approaches to designs that integrate B⫹, B–, and overall populations. For implementation in practice, several considerations need to be taken into account, such as the role of B–, how to address complex objectives with multiple end points and populations, and the utility of adaptive designs with population selection.
H2
PFS 2.0%
0.5%
0.75
Sasikiran Goteti and Samit Hirawat
1.0
Novartis Pharmaceuticals, East Hanover, NJ
Cristian Massacesi and Nathalie Fretault H3
H4
Novartis Oncology, Paris, France
0%
0%
Frank Bretz
OS
Novartis Pharma, Basel, Switzerland Overall population
Biomarker positive
Bharani Dharan
Fig 1. Graphical visualization of the decision strategy for the BELLE-2 study. H, hypothesis; OS, overall survival; PFS, progression-free survival.
Novartis Pharmaceuticals, East Hanover, NJ
ACKNOWLEDGMENT
to be accounted for when designing trials with complex objectives. Figure 1 visualizes the test strategy for the BELLE-2 study, a phase III trial with the primary objective of demonstrating progression-free survival (PFS) in the overall and B⫹ populations and the secondary objective of demonstrating overall survival (OS) in the same populations. With the total significance level ␣ ⫽ 2.5% (onesided), the two hypotheses for PFS are tested at split levels 2% and 0.5% for the overall and B⫹ populations, respectively. The associated OS hypotheses are tested only if a statistically significant treatment effect was detected for PFS. For example, if PFS meets statistical significance in the overall population at 2.0%, that level is split and propagated further according to the prespecified weights on the directed edges. Accordingly, PFS can then be tested in B⫹ at 1% and OS in the overall population at 1.5%, although the actual significance levels have to be further adjusted to account for the group-sequential decision boundaries.3 Figure 1 displays the test strategy tailored to the considerations of BELLE-2 but can easily be adapted to address additional considerations such as biomarker prevalence, timing of analyses, relative importance of the trial objectives, and overall trial power.
Support for medical editorial assistance was provided by Novartis Pharmaceuticals. We thank Nick Fitch, PhD, for his medical editorial assistance with this letter. AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Although all authors completed the disclosure declaration, the following author(s) and/or an author’s immediate family member(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO’s conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment or Leadership Position: Sasikiran Goteti, Novartis Pharmaceuticals (C); Samit Hirawat, Novartis Pharmaceuticals (C); Cristian Massacesi, Novartis Oncology (C); Nathalie Fretault, Novartis Pharma (C); Frank Bretz, Novartis Pharma (C); Bharani Dharan, Novartis Pharmaceuticals (C) Consultant or Advisory Role: None Stock Ownership: Samit Hirawat, Novartis Pharmaceuticals; Cristian Massacesi, Novartis; Nathalie Fretault, Novartis Pharmaceuticals; Frank Bretz, Novartis Pharma; Bharani Dharan, Novartis Pharmaceuticals Honoraria: None Research Funding: None Expert Testimony: None Patents, Royalties, and Licenses: None Other Remuneration: None REFERENCES
Utility of Adaptive Designs for Confirmatory Studies Adequately powering a trial with multiple end points while controlling the overall type 1 error often gives rise to concerns about the required sample size. This challenge can be effectively addressed by two-stage adaptive designs with population selection at the interim analysis.4,5 Such a design avoids unnecessary treatment exposure for a subpopulation if there is no evidence of treatment benefit. Accordingly, patients from the overall population are enrolled onto the trial at stage 1. At the end of stage 1, prespecified decision criteria will determine whether to stop the study for futility or continue the trial and possibly restrict stage 2 recruitment to B⫹. By using adequate statistical methodology, such a design provides confirmatory evidence of benefit in the selected population while combining evidence from both stages and controlling the overall type 1 error.6
1. Freidlin B, Sun Z, Gray R, et al: Phase III clinical trials that integrate treatment and biomarker evaluation. J Clin Oncol 31:3158-3161, 2013 2. Bretz F, Maurer W, Brannath W, et al: A graphical approach to sequentially rejective multiple test procedures. Stat Med 28:586-604, 2009 3. Maurer W, Bretz F: Multiple testing in group sequential trials using graphical approaches. Stat Biopharm Res 5:311-320, 2013 4. Brannath W, Zuber E, Branson M, et al: Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med 28:14451463, 2009 5. Food and Drug Administration: Guidance for industry: Enrichment strategies for clinical trials to support approval of human drugs and biological products, December 2012. www.fda.gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM332181.pdf 6. Food and Drug Administration: Guidance for industry: Adaptive design clinical trials for drugs and biologics, February 2010. www.fda.gov/downloads/ Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf
DOI: 10.1200/JCO.2013.53.7613; published online ahead of print at www.jco.org on February 3, 2014
■ ■ ■
www.jco.org
© 2014 by American Society of Clinical Oncology
Information downloaded from jco.ascopubs.org and provided by at Thammasat University Libraries on October 4, 2014 from Copyright © 2014 American Society of Clinical Oncology. All rights reserved. 203.131.222.3
855
4. Dodd MJ, Miaskowski C, Lee KA: Occurrence of symptom clusters. J Natl Cancer Inst Monogr 76-78, 2004 5. Oh HS, Seo WS: Systematic review and meta-analysis of the correlates of cancer-related fatigue. Worldviews Evid Based Nurs 8:191-201, 2011 6. Stone P, Hardy J, Broadley K, et al: Fatigue in advanced cancer: A prospective controlled cross-sectional study. Br J Cancer 79:1479-1486, 1999
7. Yennu S, Urbauer DL, Bruera E: Factors associated with the severity and improvement of fatigue in patients with advanced cancer presenting to an outpatient palliative care clinic. BMC Palliat Care 11:16, 2012
DOI: 10.1200/JCO.2013.52.4363; published online ahead of print at www.jco.org on November 12, 2013
■ ■ ■
Reply to R.J. Chan We thank Chan1 for his thoughtful correspondence regarding our randomized controlled trial of methylphenidate and/or a nursing telephone intervention for fatigue in patients with advanced cancer.2 We agree with his comments regarding the need for more research on the role of telephone interventions and underpowering as a limitation of our negative findings. These issues were mentioned in our article. There are no major reasons to suspect that in patients with advanced cancer (defined as locally advanced and/or metastatic), the mechanisms of fatigue differ dramatically according to primary tumor site or that some subgroups with different biologic characteristics are more or less likely to respond to a telephone intervention. It is more likely that the opportunities to identify subgroups of patients who are likely to benefit more from a telephone intervention are related to some sociodemographic characteristics or to the presence of different symptom clusters of physical and/or emotional characteristics. For example, it might be possible that patients with a more significant contribution of mood to their fatigue expression might show a different response to a nursing telephone intervention. We agree with Chan1 about the importance of the associations between symptoms, and it is also possible that patients with higher levels of overall symptom expression might be more likely to benefit from telephone interventions.
We also agree with Chan1 that it is important in future research to establish if simple interventions conducted by individuals with a lower level of training might be as effective as more expensive nurse- or midlevel practitioner– based interventions.
Eduardo Bruera and Sriram Yennurajalingam The University of Texas MD Anderson Cancer Center, Houston, TX
ACKNOWLEDGMENT
E.B. is supported in part by National Institutes of Health National Institute of Nursing Research Grant No. RO1NR010162-01A1. S.Y. is supported in part by American Cancer Society Grant No. RSG-11-170-01-BCM (entitled “Research Scholar Grant for Independent Investigators Multimodal Study for Cancer Related Fatigue”). AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The author(s) indicated no potential conflicts of interest. REFERENCES 1. Chan RJ: Do nursing interventions targeting concurrent symptoms hold promise for managing fatigue in patients with advanced cancer? J Clin Oncol 32:853-854, 2014 2. Bruera E, Yennurajalingam S, Palmer JL, et al: Methylphenidate and/or a nursing telephone intervention for fatigue in patients with advanced cancer: A randomized, placebo-controlled, phase II trial. J Clin Oncol 31:2421-2427, 2013
DOI: 10.1200/JCO.2013.52.9412; published online ahead of print at www.jco.org on November 12, 2013
■ ■ ■
Some Practical Considerations for Phase III Studies With Biomarker Evaluations TO THE EDITOR: When a biomarker’s ability to identify patients benefitting from a targeted therapy is not fully established, it is valuable to evaluate treatment benefit in a phase III study in the biomarker-positive (B⫹) and biomarker-negative (B–) subgroups. In a recent article in Journal of Clinical Oncology, Freidlin et al1 detailed several important considerations for such scenarios and compare possible design options. We would like to raise a few additional considerations to complement their discussion and share some of our practical experiences from implementing phase III studies of buparlisib in patients with advanced and metastatic breast cancer (BELLE-2 [ClinicalTrials.gov ID: NCT01610284], BELLE-3 [ClinicalTrials.gov ID: NCT01633060], and BELLE-4 [ClinicalTrials.gov ID: NCT01572727]). Role of B– Freidlin et al1 are concerned with designs that test only the B⫹ and overall populations; they can erroneously recommend treatment 854
© 2014 by American Society of Clinical Oncology
for B– as the result of an overall clinical benefit driven by a strong B⫹ treatment effect. Given that the B– subgroup is not the target subgroup of interest in such designs, we suggest avoiding a formal test for treatment effect in B– patients, using Bayesian decision tools instead to assess whether a prespecified, clinically relevant threshold is achieved. More specifically, we can avoid spending any significance level for B– by using the graphical test procedures from Bretz et al2 in the populations of interest. We can ensure that a clinically relevant effect in B– is achieved by requiring, for example, a minimum 90% posterior probability for the hazard ratio in the B– group to be superior to a prespecified threshold. Combining these two strategies maximizes the power to detect a treatment effect in the populations of interest while simultaneously allowing assessment of the B– effect. Adequate Test Procedures for Complex Trial Objectives Recently, graphical approaches have been proposed that visualize decision strategies in an easily communicable way and tailor advanced multiple test procedures to complex trial objectives such as assessing treatment benefit for multiple end points in more than one population. Freidlin et al1 discuss several designs that can be visualized and extended using this approach. In practice, clinical considerations need JOURNAL OF CLINICAL ONCOLOGY
Information downloaded from jco.ascopubs.org and provided by at Thammasat University Libraries on October 4, 2014 from Copyright © 2014 American Society of Clinical Oncology. All rights reserved. 203.131.222.3
Correspondence
H1
0.25
In summary, Freidlin et al1 give an excellent overview of different approaches to designs that integrate B⫹, B–, and overall populations. For implementation in practice, several considerations need to be taken into account, such as the role of B–, how to address complex objectives with multiple end points and populations, and the utility of adaptive designs with population selection.
H2
PFS 2.0%
0.5%
0.75
Sasikiran Goteti and Samit Hirawat
1.0
Novartis Pharmaceuticals, East Hanover, NJ
Cristian Massacesi and Nathalie Fretault H3
H4
Novartis Oncology, Paris, France
0%
0%
Frank Bretz
OS
Novartis Pharma, Basel, Switzerland Overall population
Biomarker positive
Bharani Dharan
Fig 1. Graphical visualization of the decision strategy for the BELLE-2 study. H, hypothesis; OS, overall survival; PFS, progression-free survival.
Novartis Pharmaceuticals, East Hanover, NJ
ACKNOWLEDGMENT
to be accounted for when designing trials with complex objectives. Figure 1 visualizes the test strategy for the BELLE-2 study, a phase III trial with the primary objective of demonstrating progression-free survival (PFS) in the overall and B⫹ populations and the secondary objective of demonstrating overall survival (OS) in the same populations. With the total significance level ␣ ⫽ 2.5% (onesided), the two hypotheses for PFS are tested at split levels 2% and 0.5% for the overall and B⫹ populations, respectively. The associated OS hypotheses are tested only if a statistically significant treatment effect was detected for PFS. For example, if PFS meets statistical significance in the overall population at 2.0%, that level is split and propagated further according to the prespecified weights on the directed edges. Accordingly, PFS can then be tested in B⫹ at 1% and OS in the overall population at 1.5%, although the actual significance levels have to be further adjusted to account for the group-sequential decision boundaries.3 Figure 1 displays the test strategy tailored to the considerations of BELLE-2 but can easily be adapted to address additional considerations such as biomarker prevalence, timing of analyses, relative importance of the trial objectives, and overall trial power.
Support for medical editorial assistance was provided by Novartis Pharmaceuticals. We thank Nick Fitch, PhD, for his medical editorial assistance with this letter. AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Although all authors completed the disclosure declaration, the following author(s) and/or an author’s immediate family member(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO’s conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment or Leadership Position: Sasikiran Goteti, Novartis Pharmaceuticals (C); Samit Hirawat, Novartis Pharmaceuticals (C); Cristian Massacesi, Novartis Oncology (C); Nathalie Fretault, Novartis Pharma (C); Frank Bretz, Novartis Pharma (C); Bharani Dharan, Novartis Pharmaceuticals (C) Consultant or Advisory Role: None Stock Ownership: Samit Hirawat, Novartis Pharmaceuticals; Cristian Massacesi, Novartis; Nathalie Fretault, Novartis Pharmaceuticals; Frank Bretz, Novartis Pharma; Bharani Dharan, Novartis Pharmaceuticals Honoraria: None Research Funding: None Expert Testimony: None Patents, Royalties, and Licenses: None Other Remuneration: None REFERENCES
Utility of Adaptive Designs for Confirmatory Studies Adequately powering a trial with multiple end points while controlling the overall type 1 error often gives rise to concerns about the required sample size. This challenge can be effectively addressed by two-stage adaptive designs with population selection at the interim analysis.4,5 Such a design avoids unnecessary treatment exposure for a subpopulation if there is no evidence of treatment benefit. Accordingly, patients from the overall population are enrolled onto the trial at stage 1. At the end of stage 1, prespecified decision criteria will determine whether to stop the study for futility or continue the trial and possibly restrict stage 2 recruitment to B⫹. By using adequate statistical methodology, such a design provides confirmatory evidence of benefit in the selected population while combining evidence from both stages and controlling the overall type 1 error.6
1. Freidlin B, Sun Z, Gray R, et al: Phase III clinical trials that integrate treatment and biomarker evaluation. J Clin Oncol 31:3158-3161, 2013 2. Bretz F, Maurer W, Brannath W, et al: A graphical approach to sequentially rejective multiple test procedures. Stat Med 28:586-604, 2009 3. Maurer W, Bretz F: Multiple testing in group sequential trials using graphical approaches. Stat Biopharm Res 5:311-320, 2013 4. Brannath W, Zuber E, Branson M, et al: Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med 28:14451463, 2009 5. Food and Drug Administration: Guidance for industry: Enrichment strategies for clinical trials to support approval of human drugs and biological products, December 2012. www.fda.gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM332181.pdf 6. Food and Drug Administration: Guidance for industry: Adaptive design clinical trials for drugs and biologics, February 2010. www.fda.gov/downloads/ Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf
DOI: 10.1200/JCO.2013.53.7613; published online ahead of print at www.jco.org on February 3, 2014
■ ■ ■
www.jco.org
© 2014 by American Society of Clinical Oncology
Information downloaded from jco.ascopubs.org and provided by at Thammasat University Libraries on October 4, 2014 from Copyright © 2014 American Society of Clinical Oncology. All rights reserved. 203.131.222.3
855
