Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Journal of Biopharmaceutical Statistics

Volumn 26, Issue 1, 2016, Pages 99-119

  Ondra, Thomas ^a   Dmitrienko, Alex ^b   Friede, Tim ^c   Graf, Alexandra ^a   Miller, Frank ^d   Stallard, Nigel ^e   Posch, Martin ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

^b IQVIA   (United States)

^c UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

^d STOCKHOLM UNIVERSITY   (Sweden)

^e UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Enrichment design; personalized medicine; precision medicine; predictive biomarkers; subgroup identification

Indexed keywords

BIOLOGICAL MARKER; COMPLEMENT; GEFITINIB; TRASTUZUMAB;

ALGORITHM; ANALYSIS; ARTICLE; BAYES THEOREM; CANCER CHEMOTHERAPY; CLINICAL TRIAL (TOPIC); DECISION MAKING; HUMAN; LITERATURE; PRIORITY JOURNAL; STOMACH CANCER; STUDY DESIGN; META ANALYSIS; METHODOLOGY; PERSONALIZED MEDICINE; STATISTICS AND NUMERICAL DATA;

BIOMARKERS; CLINICAL TRIALS AS TOPIC; HUMANS; PRECISION MEDICINE; RESEARCH DESIGN;

EID: 84955256935     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2015.1092034     Document Type: Article

References (87)

1. Almirall, D., Compton, S.N., Gunlicks-Stoessel, M., Duan, N., Murphy, S.A. (2012). Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy. Statistics in Medicine 31: 1887-1902
2. Alosh, M., Huque, M.F. (2009). A flexible strategy for testing subgroups and overall population. Statistics in Medicine 28: 3-23
3. Alosh, M., Huque, M.F. (2013). Multiplicity considerations for subgroup analysis subject to consistency constraint: Multiplicity considerations for subgroup analysis. Biometrical Journal 55: 444-462
4. Altstein, L.L., Li, G., Elashoff, R.M. (2011). A method to estimate treatment efficacy among latent subgroups of a randomized clinical trial. Statistics in Medicine 30: 709-717
5. Bang, Y.J., Van Cutsem, E., Feyereislova, A., Chung, H.C., Shen, L., Sawaki, A., Lordick, F., Ohtsu, A., Omuro, Y., Satoh, T., Aprile, G., Kulikov, E., Hill, J., Lehle, M., Rüschoff, J., Kang, Y.K. (2010). Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of her2-positive advanced gastric or gastro-oesophageal junction cancer (toga): a phase 3, open-label, randomised controlled trial. The Lancet 367: 687-698
6. Bauer, P., Kieser, M. (1999). Combining different phases in the development of medical treatments within a single trial. Statistics in Medicine 18: 1833-1848
7. Bauer, P., Koehne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics 50: 1029-1041
8. Bauer, P., Posch, M. (2004). Letter to the editor. Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections by H. Schäfer and H.-H. Müller, Statistics in Medicine 2001; 20: 3741-3751. Statistics in Medicine 23: 1333-1334
9. Beckman, R.A., Clark, J., Chen, C. (2011). Integrating predictive biomarkers and classifiers into oncology clinical development programmes. Nature Reviews Drug Discovery 10: 735-748
10. Berger, J.O., Wang, X., Shen, L. (2014). A Bayesian approach to subgroup identification. Journal of Biopharmaceutical Statistics 24: 110-129
11. Berry, S.M., Broglio, K.R., Groshen, S., Berry, D.A. (2013). Bayesian hierarchical modeling of patient subpopulations: Efficient designs of phase II oncology clinical trials. Clinical Trials 10: 720-734
12. Boessen, R., van der Baan, F,., Groenwold, R., Egberts, A., Klungel, O., Grobbee, D., Knol, M., Roes, K. (2013). Optimizing trial design in pharmacogenetics research: comparing a fixed parallel group, group sequential, and adaptive selection design on sample size requirements. Pharmaceutical Statistics 12: 366-374
13. Bonetti, M. (2004). Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics 5: 465-481
14. Brannath, W., Bretz, F. (2010). Shortcuts for locally consonant closed test procedures. Journal of the American Statistical Association 105: 660-669
15. Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M., Racine-Poon, A. (2009). Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Statistics in Medicine 28: 1445-1463
16. Bretz, F., Maurer, W., Brannath, W., Posch, M. (2009). A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 28: 586-604
17. Bretz, F., Posch, M., Glimm, E., Klinglmueller, F., Maurer, W., Rohmeyer, K. (2011). Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests. Biometrical Journal 53: 894-913
18. Cai, T., Tian, L., Wong, P.H., Wei, L.J. (2011). Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 12: 270-282
19. Chen, W., Ghosh, D., Raghunathan, T.E., Norkin, M., Sargent, D.J., Bepler, G. (2012). On Bayesian methods of exploring qualitative interactions for targeted treatment. Statistics in Medicine 31: 3693-3707
20. Cobo, M., Isla, D., Massuti, B., Montes, A., Sanchez, J.M., Provencio, M., Viñolas, N., Paz-Ares, L., Lopez-Vivanco, G., Muñoz, M.A., Felip, E., Alberola, V., Camps, C., Domine, M., Sanchez, J.J., Sanchez-Ronco, M., Danenberg, K., Taron, M., Gandara, D., Rosell, R. (2007). Customizing cisplatin based on quantitative excision repair cross-complementing 1 mRNA expression: A phase III trial in non-small-cell lung cancer. Journal of clinical oncology 25: 2747-2754
21. Cree, I.A., Kurbacher, C.M., Lamont, A., Hindley, A.C., Love, S. (2007). A prospective randomized controlled trial of tumour chemosensitivity assay directed chemotherapy versus physicians choice in patients with recurrent platinum-resistant ovarian cancer. Anticancer drugs 18: 1093-1101
22. Dixon, D.O., Simon, R. (1991). Bayesian subset analysis. Biometrics 47: 871-881
23. Dixon, D.O., Simon, R. (1992). Bayesian subset analysis in a colorectal cancer clinical trial. Statistics in Medicine 11: 13-22
24. Dmitrienko, A., DAgostino, R.B. (2013). Tutorial in biostatistics: Traditional multiplicity adjustment methods in clinical trials. Statistics in Medicine 32: 5172-5218
25. Dusseldorp, E., Van Mechelen, I. (2014). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine 33: 219-237
26. Eickhoff, J.C., Kim, K., Beach, J., Kolesar, J.M., Gee, J.R. (2010). A Bayesian adaptive design with biomarkers for targeted therapies. Clinical Trials 7: 546-556
27. Eng, K.H. (2014). Randomized reverse marker strategy design for prospective biomarker validation. Statistics in Medicine 33: 3089-3099
28. FDA. (2012). Guidance for industry: Enrichment strategies for clinical trials to support approval of human drugs and biological products. http://www.fda.gov
29. Foster, J., Taylor, J., Ruberg, S. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine 30: 2867-2880
30. Freidlin, B. (2005). Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Research 11: 7872-7878
31. Freidlin, B., Jiang, W., Simon, R. (2010a). The cross-validated adaptive signature design. Clinical Cancer Research 16: 691-698
32. Freidlin, B., McShane, L.M., Korn, E.L. (2010b). Randomized clinical trials with biomarkers: Design issues. JNCI Journal of the National Cancer Institute 102: 152-160
33. Freidlin, B., McShane, L.M., Polley, M.Y.C., Korn, E.L. (2012). Randomized Phase II trial designs with biomarkers. Journal of Clinical Oncology 30: 3304-3309
34. Friede, T., Parsons, N., Stallard, N. (2012). A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine 31: 4309-4320
35. Graf, A.C., Posch, M., Koenig, F. (2015). Adaptive designs for subpopulation analysis optimizing utility functions. Biometrical Journal 57: 76-89
36. Gu, X., Chen, N., Wei, C., Liu, S., Papadimitrakopoulou, V.A., Herbst, R.S., Lee, J.J. (2014). Bayesian two-stage biomarker-based adaptive design for targeted therapy development. Statistics in Biosciences. Published online, doi:0.1007/s12561-014-9124-2
37. Götte, H., Donica, M., Mordenti, G. (2014). Improving probabilities of correct interim decision in population enrichment designs. Journal of Biopharmaceutical Statistics 25: 1020-1038
38. Hommel, G. (2001). Adaptive modifications of hypotheses after an interim analysis. Biometrical Journal 43: 581-589
39. Huang, X., Ning, J., Li, Y., Estey, E., Issa, J.P., Berry, D.A. (2009). Using short-term response information to facilitate adaptive randomization for survival clinical trials. Statistics in Medicine 28: 1680-1689
40. Irle, S., Schäfer, H. (2012). Interim design modifications in time-to-event studies. Journal of the American Statistical Association 107: 341-348
41. Jenkins, M., Stone, A., Jennison, C. (2011). An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival end-points†. Pharmaceutical Statistics 10: 347-356
42. Jones, H.E., Ohlssen, D.I., Neuenschwander, B., Racine, A., Branson, M. (2011). Bayesian models for subgroup analysis in clinical trials. Clinical Trials 8: 129-143
43. Kovalchik, S.A., Varadhan, R., Weiss, C.O. (2013). Assessing heterogeneity of treatment effect in a clinical trial with the proportional interactions model. Statistics in Medicine 32: 4906-4923
44. Krisam, J., Kieser, M. (2014). Decision rules for subgroup selection based on a predictive biomarker. Journal of Biopharmaceutical Statistics 24: 188-202
45. Lee, J.J., Gu, X., Liu, S. (2010). Bayesian adaptive randomization designs for targeted agent development. Clinical Trials 7: 584-596
46. Lipkovich, I., Dmitrienko, A. (2014a). Biomarker identification in clinical trials. In C. Carini, S. Menon, M. Chang., editors, Clinical and Statistical Considerations in Personalized Medicine. New York: Chapman and Hall/CRC Press
47. Lipkovich, I., Dmitrienko, A. (2014b). Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using sides. Journal of Biopharmaceutical Statistics 24: 130-153
48. Lipkovich, I., Dmitrienko, A., DAgostino, R.B. (2015). Tutorial in biostatistics: Exploratory subgroup analysis in clinical trials. Statistics in Medicine 34: To appear
49. Lipkovich, I., Dmitrienko, A., Denne, J., Enas, G. (2011). Subgroup identification based on differential effect search-A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine 30: 2601-2621
50. Magirr, D., Jaki, T., Koenig, F., Posch, M. (2014). Adaptive survival trials. arXiv:1405.1569 [stat.AP]. http://arxiv.org/abs/1405.1569
51. Magnusson, B.P., Turnbull, B.W. (2013). Group sequential enrichment design incorporating subgroup selection. Statistics in Medicine 32: 2695-2714
52. Mandrekar, S.J., An, M.W., Sargent, D.J. (2013). A review of phase II trial designs for initial marker validation. Contemporary Clinical Trials 36: 597-604
53. Mandrekar, S.J., Sargent, D.J. (2009). Clinical trial designs for predictive biomarker validation: One size does not fit all. Journal of Biopharmaceutical Statistics 19: 530-542
54. Mandrekar, S.J., Sargent, D.J. (2011a). All-comers versus enrichment design strategy in phase II trials. Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer 6: 658
55. Mandrekar, S.J., Sargent, D.J. (2011b). Design of clinical trials for biomarker research in oncology. Clinical Investigation 1: 1627-1636
56. Marcus, R., Peritz, E., Gabriel, K.R. (1976). On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63: 655-660
57. McQuay, H.J., Derry, S., Moore, R.A., Poulain, P., Legout, V. (2008). Enriched enrolment with randomised withdrawal (eerw): time for a new look at clinical trial design in chronic pain. Pain 135: 217-220
58. Mehta, C., Schäfer, H., Daniel, H., Irle, S. (2014). Biomarker driven population enrichment for adaptive oncology trials with time-to-event endpoints. Statistics in Medicine 33: 4515-4531
59. Mehta, C.R., Gao, P. (2011). Population enrichment designs: case study of a large multinational trial. Journal of Biopharmaceutical Statistics 21: 831-845
60. Millen, B.A., Dmitrienko, A. (2011). Chain procedures: A class of flexible closed testing procedures with clinical trial applications. Statistics in Biopharmaceutical Research 3: 14-30
61. Millen, B.A., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework for decision making in confirmatory multipopulation tailoring clinical trials. Therapeutic Innovation & Regulatory Science 46: 647-656
62. Millen, B.A., Dmitrienko, A., Song, G. (2014). Bayesian assessment of the influence and interaction conditions in multipopulation tailoring clinical trials. Journal of Biopharmaceutical Statistics 24: 94-109
63. Moineddin, R., Butt, D.A., Tomlinson, G., Beyene, J. (2008). Identifying subpopulations for subgroup analysis in a longitudinal clinical trial. Contemporary Clinical Trials 29: 817-822
64. Moore, R.A., Wiffen, P.J., Eccleston, C., Dery, S.,., Baron, R., Bell, R.F., Furlan, A.D,., Gilron, I., Haroutounian, S., Katz, N.P., Lipman, A.G., Morley, S., Peloso, P.M., Quessy, S.N., Seers, K., Strassels, S.A., Straube, S. (2015). Systematic review of enriched-enrolment randomised-withdrawal trial designs in chronic pain: A new framework for design and reporting. Pain 156: 1382-1395
65. Morita, S., Yamamoto, H., Sugitani, Y. (2014). Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation. Statistics in Medicine 33: 4008-4016
66. Müller, H.H., Schäfer, H. (2004). A general statistical principle for changing a design any time during the course of a trial. Statistics in Medicine 23: 2497-2508
67. Proschan, M.A., Hunsberger, S.A. (1995). Designed extension of studies based on conditional power. Biometrics 51: 1315-1324
68. Simon, N., Simon, R. (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14: 613-625
69. Simon, R., Dixon, D.O., Freidlin, B. (1996). Bayesian subset analysis of a clinical trial for the treatment of hiv infections. In D. A. Berry, D. K. Stangl., editors, Bayesian Biostatistics. New York: CRC Press
70. Sivaganesan, S., Laud, P.W., Müller, P. (2011). A Bayesian subgroup analysis with a zero-enriched Polya urn scheme. Statistics in Medicine 30: 312-323
71. Song, J.X. (2014). A two-stage patient enrichment adaptive design in phase II oncology trials. Contemporary Clinical Trials 37: 148-154
72. Song, Y., Chi, G.Y.H. (2007). A method for testing a prespecified subgroup in clinical trials. Statistics in Medicine 26: 3535-3549
73. Spiessens, B., Debois, M. (2010). Adjusted significance levels for subgroup analyses in clinical trials. Contemporary Clinical Trials 31: 647-656
74. Stallard, N., Hamborg, T., Parsons, N., Friede, T. (2014). Adaptive designs for confirmatory clinical trials with subgroup selection. Journal of Biopharmaceutical Statistics 24: 168-187
75. Straube, S., Derry, S., McQuay, H.J., Moore, A.M. (2008). Enriched enrolment: definition of effects of enrichment and dose in trials of pregabalin and gabapentin in neuropathic pain. a systematic review. British Journal of Clinical Pharmacology 66: 266-275
76. Varadhan, R., Wang, S.J. (2014). Standardization for subgroup analysis in randomized controlled trials. Journal of Biopharmaceutical Statistics 24: 154-167
77. Wang, S.J., James Hung, H.M., ONeill, R.T. (2009). Adaptive patient enrichment designs in therapeutic trials. Biometrical Journal 51: 358-374
78. Wang, S.J., ONeill, R.T., Hung, H.M.J. (2007). Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics 6: 227-244
79. Wassmer, G., Dragalin, V. (2014). Designing issues in confirmatory adaptive population enrichment trials. Journal of Biopharmaceutical Statistics 25: 651-669
80. Xu, Y., Trippa, L., Müller, P., Ji, Y. (2014). Subgroup-based adaptive (suba) designs for multi-Arm biomarker trials. Statistics in Biosciences, 1-2
81. Zhang, B., Tsiatis, A.A., Davidian, M., Zhang, M., Laber, E.B. (2012). Estimating optimal treatment regimes from a classification perspective. Statistics 1: 103-114
82. Zhao, L., Tian, L., Cai, T., Claggett, B., Wei, L.J. (2013). Effectively selecting a target population for a future comparative study. Journal of the American Statistical Association 108: 527-539
83. Zhao, Y., Zeng, D., Rush, A.J., Kosorok, M.R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association 107: 1106-1118
84. Zhao, Y.D., Dmitrienko, A., Tamura, R. (2010). Design and analysis considerations in clinical trials with a sensitive subpopulation. Statistics in Biopharmaceutical Research 2: 72-83
85. Zhong, W., Koopmeiners, J.S., Carlin, B.P. (2013). A two-stage Bayesian design with sample size reestimation and subgroup analysis for phase II binary response trials. Contemporary Clinical Trials 36: 587-596
86. Zhou, X., Liu, S., Kim, E.S., Herbst, R.S., Lee, J.J. (2008). Bayesian adaptive design for targeted therapy development in lung cancer-A step toward personalized medicine. Clinical Trials 5: 181-193
87. Ziegler, A., Koch, A., Krockenberger, K., Großhennig, A. (2012). Personalized medicine using DNA biomarkers: A review. Human Genetics 131: 1627-1638.