Skip to main content
Top
Published in: BMC Medical Research Methodology 1/2019

Open Access 01-12-2019 | Targeted Therapy | Research article

Patient recruitment strategies for adaptive enrichment designs with time-to-event endpoints

Authors: Ryuji Uozumi, Shinjo Yada, Atsushi Kawaguchi

Published in: BMC Medical Research Methodology | Issue 1/2019

Login to get access

Abstract

Background

Adaptive enrichment designs for clinical trials have great potential for the development of targeted therapies. They enable researchers to stop the recruitment process for a certain population in mid-course based on an interim analysis. However, adaptive enrichment designs increase the total trial period owing to the stoppage in patient recruitment to make interim decisions. This is a major drawback; it results in delays in the submission of clinical trial reports and the appearance of drugs on the market. Here, we explore three types of patient recruitment strategy for the development of targeted therapies based on the adaptive enrichment design.

Methods

We consider recruitment methods which provide an option to continue recruiting patients from the overall population or only from the biomarker-positive population even during the interim decision period. A simulation study was performed to investigate the operating characteristics by comparing an adaptive enrichment design using the recruitment methods with a non-enriched design.

Results

The number of patients was similar for both recruitment methods. Nevertheless, the adaptive enrichment design was beneficial in settings in which the recruitment period is expected to be longer than the follow-up period. In these cases, the adaptive enrichment design with continued recruitment from the overall population or only from the biomarker-positive population even during the interim decision period conferred a major advantage, since the total trial period did not differ substantially from that of trials employing the non-enriched design. By contrast, the non-enriched design should be used in settings in which the follow-up period is expected to be longer than the recruitment period, since the total trial period was notably shorter than that of the adaptive enrichment design. Furthermore, the utmost care is needed when the distribution of patient recruitment is concave, i.e., when patient recruitment is slow during the early period, since the total trial period is extended.

Conclusions

Adaptive enrichment designs that entail continued recruitment methods are beneficial owing to the shorter total trial period than expected in settings in which the recruitment period is expected to be longer than the follow-up period and the biomarker-positive population is promising.
Literature
1.
go back to reference Aggarwal S. Targeted cancer therapies. Nat Rev Drug Discov. 2010; 9(6):427–8.CrossRef Aggarwal S. Targeted cancer therapies. Nat Rev Drug Discov. 2010; 9(6):427–8.CrossRef
2.
go back to reference Kamel HFM, Al-Amodi HSAB. Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine. Genomics Proteomics Bioinforma. 2017; 15(4):220–35.CrossRef Kamel HFM, Al-Amodi HSAB. Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine. Genomics Proteomics Bioinforma. 2017; 15(4):220–35.CrossRef
3.
go back to reference Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009; 361(10):947–57.CrossRef Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009; 361(10):947–57.CrossRef
4.
go back to reference Solomon BJ, Mok T, Kim DW, Wu YL, Nakagawa K, Mekhail T, Felip E, Cappuzzo F, Paolini J, Usari T, Iyer S, Reisman A, Wilner KD, Tursi J, Blackhall F. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N Engl J Med. 2014; 371(23):2167–77.CrossRef Solomon BJ, Mok T, Kim DW, Wu YL, Nakagawa K, Mekhail T, Felip E, Cappuzzo F, Paolini J, Usari T, Iyer S, Reisman A, Wilner KD, Tursi J, Blackhall F. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N Engl J Med. 2014; 371(23):2167–77.CrossRef
5.
go back to reference Baselga J, Cortés J, Kim SB, Im S-A, Hegg R, Im YH, Roman L, Pedrini JL, Pienkowski T, Knott A, Clark E, Benyunes MC, Ross G, Swain SM. Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med. 2012; 366(2):109–19.CrossRef Baselga J, Cortés J, Kim SB, Im S-A, Hegg R, Im YH, Roman L, Pedrini JL, Pienkowski T, Knott A, Clark E, Benyunes MC, Ross G, Swain SM. Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med. 2012; 366(2):109–19.CrossRef
6.
go back to reference Swain SM, Kim SB, Cortés J, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Knott A, Clark E, Ross G, Benyunes MC, Baselga J. Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (CLEOPATRA study): overall survival results from a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2013; 14(6):461–71.CrossRef Swain SM, Kim SB, Cortés J, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Knott A, Clark E, Ross G, Benyunes MC, Baselga J. Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (CLEOPATRA study): overall survival results from a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2013; 14(6):461–71.CrossRef
7.
go back to reference Swain SM, Baselga J, Kim SB, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Heeson S, Clark E, Ross G, Benyunes MC, Cortés J. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med. 2015; 372(8):724–34.CrossRef Swain SM, Baselga J, Kim SB, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Heeson S, Clark E, Ross G, Benyunes MC, Cortés J. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med. 2015; 372(8):724–34.CrossRef
8.
go back to reference Fujitani K, Yang HK, Mizusawa J, Kim YW, Terashima M, Han SU, Iwasaki Y, Hyung WJ, Takagane A, Park DJ, Yoshikawa T, Hahn S, Nakamura K, Park CH, Kurokawa Y, Bang YJ, Park BJ, Sasako M, Tsujinaka T. Gastrectomy plus chemotherapy versus chemotherapy alone for advanced gastric cancer with a single non-curable factor (REGATTA): a phase 3, randomised controlled trial. Lancet Oncol. 2016; 17(3):309–18.CrossRef Fujitani K, Yang HK, Mizusawa J, Kim YW, Terashima M, Han SU, Iwasaki Y, Hyung WJ, Takagane A, Park DJ, Yoshikawa T, Hahn S, Nakamura K, Park CH, Kurokawa Y, Bang YJ, Park BJ, Sasako M, Tsujinaka T. Gastrectomy plus chemotherapy versus chemotherapy alone for advanced gastric cancer with a single non-curable factor (REGATTA): a phase 3, randomised controlled trial. Lancet Oncol. 2016; 17(3):309–18.CrossRef
9.
go back to reference Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med. 2016; 35(3):325–47.CrossRef Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med. 2016; 35(3):325–47.CrossRef
10.
go back to reference Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharm Stat. 2011; 10(4):347–56.CrossRef Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharm Stat. 2011; 10(4):347–56.CrossRef
11.
go back to reference Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics. 1999; 55(4):1286–90.CrossRef Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics. 1999; 55(4):1286–90.CrossRef
12.
go back to reference Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988; 75(4):800–2.CrossRef Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988; 75(4):800–2.CrossRef
13.
go back to reference Marcus R, Peritz E, Gabriel KR. On closed testing procedures with special reference to ordered analysis of variance. Biometrika. 1976; 63(3):655–60.CrossRef Marcus R, Peritz E, Gabriel KR. On closed testing procedures with special reference to ordered analysis of variance. Biometrika. 1976; 63(3):655–60.CrossRef
14.
go back to reference Schoenfeld D. The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika. 1981; 68(1):316–9.CrossRef Schoenfeld D. The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika. 1981; 68(1):316–9.CrossRef
15.
go back to reference Brannath W, Zuber E, Branson M, Bretz F, Gallo P, Posch M, Racine-Poon A. Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med. 2009; 28(10):1445–63.CrossRef Brannath W, Zuber E, Branson M, Bretz F, Gallo P, Posch M, Racine-Poon A. Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med. 2009; 28(10):1445–63.CrossRef
16.
go back to reference Friede T, Parsons N, Stallard N. A conditional error function approach for subgroup selection in adaptive clinical trials. Stat Med. 2012; 31(30):4309–20.CrossRef Friede T, Parsons N, Stallard N. A conditional error function approach for subgroup selection in adaptive clinical trials. Stat Med. 2012; 31(30):4309–20.CrossRef
17.
go back to reference Krisam J, Kieser M. Optimal interim decision rules based on a binary surrogate outcome for adaptive biomarker-based trials in oncology. Stat Biopharm Res. 2017; 9(4):321–32.CrossRef Krisam J, Kieser M. Optimal interim decision rules based on a binary surrogate outcome for adaptive biomarker-based trials in oncology. Stat Biopharm Res. 2017; 9(4):321–32.CrossRef
18.
go back to reference Uozumi R, Hamada C. Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints. J Biopharm Stat. 2017; 27(1):84–100.CrossRef Uozumi R, Hamada C. Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints. J Biopharm Stat. 2017; 27(1):84–100.CrossRef
19.
go back to reference Johnson NL, Kotz S, Balakrishnan N. Continuous Univariate Distributions, vol 1, 2nd ed. New York: Wiley; 1994. Johnson NL, Kotz S, Balakrishnan N. Continuous Univariate Distributions, vol 1, 2nd ed. New York: Wiley; 1994.
20.
go back to reference Food and Drug Administration. Guidance for industry: adaptive designs for clinical trials of drugs and biologics, draft.Silver Spring, MD: U.S. Food and Drug Administration; 2018. Food and Drug Administration. Guidance for industry: adaptive designs for clinical trials of drugs and biologics, draft.Silver Spring, MD: U.S. Food and Drug Administration; 2018.
21.
go back to reference O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979; 35(3):549–56.CrossRef O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979; 35(3):549–56.CrossRef
22.
go back to reference Lan KG, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika. 1983; 70(3):659–63.CrossRef Lan KG, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika. 1983; 70(3):659–63.CrossRef
23.
go back to reference Gallo P, Mao L, Shih VH. Alternative views on setting clinical trial futility criteria. J Biopharm Stat. 2014; 24(5):976–93.CrossRef Gallo P, Mao L, Shih VH. Alternative views on setting clinical trial futility criteria. J Biopharm Stat. 2014; 24(5):976–93.CrossRef
24.
go back to reference Togo K, Iwasaki M. Optimal timing for interim analyses in clinical trials. J Biopharm Stat. 2013; 23(5):1067–80.CrossRef Togo K, Iwasaki M. Optimal timing for interim analyses in clinical trials. J Biopharm Stat. 2013; 23(5):1067–80.CrossRef
25.
go back to reference Freidlin B, Othus M, Korn EL. Information time scales for interim analyses of randomized clinical trials. Clin Trials. 2016; 13(4):391–9.CrossRef Freidlin B, Othus M, Korn EL. Information time scales for interim analyses of randomized clinical trials. Clin Trials. 2016; 13(4):391–9.CrossRef
26.
go back to reference Counsell N, Biri D, Fraczek J, Hackshaw A. Publishing interim results of randomised clinical trials in peer-reviewed journals. Clin Trials. 2017; 14(1):67–77.CrossRef Counsell N, Biri D, Fraczek J, Hackshaw A. Publishing interim results of randomised clinical trials in peer-reviewed journals. Clin Trials. 2017; 14(1):67–77.CrossRef
27.
go back to reference Wang H, Rosner GL, Goodman SN. Quantifying over-estimation in early stopped clinical trials and the "freezing effect" on subsequent research. Clin Trials. 2016; 13(6):621–31.CrossRef Wang H, Rosner GL, Goodman SN. Quantifying over-estimation in early stopped clinical trials and the "freezing effect" on subsequent research. Clin Trials. 2016; 13(6):621–31.CrossRef
28.
go back to reference Benner L, Kieser M. Timing of the interim analysis in adaptive enrichment designs. J Biopharm Stat. 2018; 28(4):622–32.CrossRef Benner L, Kieser M. Timing of the interim analysis in adaptive enrichment designs. J Biopharm Stat. 2018; 28(4):622–32.CrossRef
Metadata
Title
Patient recruitment strategies for adaptive enrichment designs with time-to-event endpoints
Authors
Ryuji Uozumi
Shinjo Yada
Atsushi Kawaguchi
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2019
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-019-0800-2

Other articles of this Issue 1/2019

BMC Medical Research Methodology 1/2019 Go to the issue