General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials

Journal of Biopharmaceutical Statistics

Volumn 26, Issue 1, 2016, Pages 71-98

  Dmitrienko, Alex ^a   Muysers, Christoph ^b   Fritsch, Arno ^b   Lipkovich, Ilya ^a  

^a Center for Statistics in Drug Development   (United States)

^b BAYER PHARMA AG   (Germany)

Author keywords

Biomarker evaluation; enrichment designs; regulatory guidance; subgroup analysis; subgroup identification

Indexed keywords

ARTICLE; CLINICAL TRIAL (TOPIC); COMPARATIVE STUDY; CONFIDENCE INTERVAL; CONFIRMATORY SUBGROUP ANALYSIS; DECISION TREE; DRUG EXPOSURE; EVALUATION STUDY; EXPERIMENTAL THERAPY; EXPLORATORY SUBGROUP ANALYSIS; FEEDBACK SYSTEM; FOOD AND DRUG ADMINISTRATION; HUMAN; PRACTICE GUIDELINE; PRIORITY JOURNAL; RISK ASSESSMENT; RISK FACTOR; SELECTION BIAS; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; METHODOLOGY; STATISTICS AND NUMERICAL DATA;

BIOLOGICAL MARKER;

BIOMARKERS; CLINICAL TRIALS AS TOPIC; DATA INTERPRETATION, STATISTICAL; HUMANS; RESEARCH DESIGN;

EID: 84955245549     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2015.1092033     Document Type: Article

References (66)

1. Alosh, M., Huque, M. F. (2013). Multiplicity considerations for subgroup analysis subject to consistency constraint. Biometrical Journal 55: 444-462
2. Alosh, M., Huque, M. F., Koch, G. (2015). Statistical perspectives on subgroup analysis: Testing for heterogeneity and evaluating error rate for the complementary subgroup. Journal of Biopharmaceutical Statistics 25: 1161-1178
3. Berger, J., Wang, X., Shen, L. (2014). A Bayesian approach to subgroup identification. Journal of Biopharmaceutical Statistics 24: 110-129
4. Bonetti, M., Gelber, R. D. (2004). Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics 5: 465-481
5. Borenstein, M., Hedges, L. V., Higgins, J. P. T., Rothstein, H. R. (2009). Introduction to Meta-Analysis. Chichester, UK: Wiley
6. 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 on oncology. Statistics in Medicine 28: 1445-1463
7. Burzykowski, T., Molenberghs, G., Buyse, M. (2005). The Evaluation of Surrogate Endpoints. New York, NY: Springer
8. Carroll, K. J., Le Maulf, F. (2011). Japanese guideline on global clinical trials: Statistical implications and alternative criteria for assessing consistency. Drug Information Journal 45: 657-667
9. CFDA. (2007). Provisions for drug registration. State Food and Drug Administration (SFDA) Order No. 28
10. CFDA. (2015). Guidance for international multicenter clinical trials (IMCT). China Food and Drug Administration Announcement No. 2 [2015] trial implementation
11. Dmitrienko, A., DAgostino, R. B., Huque, M. F. (2013). Key multiplicity issues in clinical drug development. Statistics in Medicine 32: 1079-1111
12. Dmitrienko, A., DAgostino, R. B. (2013). Tutorial in biostatistics: Traditional multiplicity adjustment methods in clinical trials. Statistics in Medicine 32: 5172-5218
13. Dmitrienko, A., Lipkovich, I., Hopkins, A., Li, Y. P., Wang, W. (2015). Biomarker evaluation and subgroup identification in a pneumonia development program using SIDES. In Applied Statistics in Biomedicine and Clinical Trials Design, Chen, Z., Liu, A., Qu, Y., Tang, L., Ting, N., and Tsong, Y., eds, pp. 427-468. New York, NY: Springer
14. Dmitrienko, A., Millen, B., Lipkovich, I. (2015). Statistical and regulatory considerations in subgroup analysis. Statistics in Medicine. To appear
15. EMA. (2002). Points to consider on multiplicity issues in clinical trials. European Medicines Agency/Committee for Proprietary Medicinal Products. CHMP/EWP/908/99
16. EMA. (2003). Points to consider on adjustment for baseline covariates. European Medicines Agency/Committee for Proprietary Medicinal Products. EMA/CPMP/EWP/2863/99
17. EMA. (2007). Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. European Medicines Agency/Committee for Medicinal Products for Human Use. CHMP/EWP/2459/02
18. EMA. (2014). Guideline on the investigation of subgroups in confirmatory clinical trials. Draft. European Medicines Agency/Committee for Medicinal Products for Human Use. EMA/CHMP/539146/2013
19. EMA. (2015). Guideline on adjustment for baseline covariates in clinical trials. European Medicines Agency/Committee for Medicinal Products for Human Use. EMA/CHMP/295050/2013
20. FDA. (2012). Guidance for Industry: Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. U.S. Food and Drug Administration
21. Foster, J. C., Taylor, J. M. C., Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine 30: 2867-2880
22. Freidlin, B., McShane, L. M., Polley, M. Y., Korn, E. L. (2012). Randomized Phase II trial designs with biomarkers. Journal of Clinical Oncology 30: 3304-3309
23. 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
24. Friedman, J. H., Fisher, N. I. (1999). Bump hunting in high-dimensional data. Statistics and Computing 9: 123-143
25. Grouin, J. M., Coste, M., Lewis, J. (2005). Subgroup analyses in randomized clinical trials: Statistical and regulatory issues. Journal of Biopharmaceutical Statistics 15: 869-882
26. Gunter, L., Zhu, J., Murphy, S. (2011). Variable selection for qualitative interactions in personalized medicine while controlling the familywise error rate. Journal of Biopharmaceutical Statistics 21: 1063-1078
27. Hemmings, R. (2014). An overview of statistical and regulatory issues in the planning, analysis, and interpretation of subgroup analyses in confirmatory clinical trials. Journal of Biopharmaceutical Statistics 24: 4-18
28. Hothorn, T., Hornik, K., Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15: 651-674
29. Hothorn, T., Zeileis, A. (2008). Generalized maximally selected statistics. Biometrics 64: 1263-1269
30. Huque, M., Röhmel, J. (2009). Multiplicity problems in clinical trials: A regulatory perspective. In Multiple Testing Problems in Pharmaceutical Statistics, Dmitrienko, A., Tamhane, A. C., and Bretz, F., eds. New York, NY:. Chapman and Hall/CRC Press
31. ICH. (1998). Ethnic factor in the acceptability of foreign data. ICH E5 Expert Working Group. The US Federal Register 83: 31790-31796
32. ICH. (1999). Statistical principles for clinical trials: ICH harmonized tripartite guideline. ICH E9 Expert Working Group. CPMP/ICH/363/96
33. ICH. (2014). Final Concept Paper E9 (R1): Addendum to Statistical Principles for Clinical Trials on Choosing Appropriate Estimands and Defining Sensitivity Analyses in Clinical Trials. ICH Steering Committee
34. Imai, K., Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. The Annals of Applied Statistics 7: 443-470
35. Jenkins, M., Stone, A., Jennison, C. (2011). An adaptive seamless Phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharmaceutical Statistics 10: 347-356
36. 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
37. Koch, A., Framke, T. (2014). Reliably basing conclusions on subgroups of randomized clinical trials. Journal of Biopharmaceutical Statistics 24: 42-57
38. Koch, G., Schwartz, T. A. (2014). An overview of statistical planning to address subgroups in confirmatory clinical trials. Journal of Biopharmaceutical Statistics 24: 72-93
39. Lipkovich, I., Dmitrienko, A., Denne, J., Enas, G. (2011). Subgroup identification based on differential effect search (SIDES): A recursive partitioning method for establishing response to treatment in subject subpopulations. Statistics in Medicine 30: 2601-2621
40. Lipkovich, I., Dmitrienko, A. (2014). Biomarker identification in clinical trials. In Clinical and Statistical Considerations in Personalized Medicine, Carini, C., Menon, S., and Chang, M., eds. New York, NY: Chapman and Hall/CRC Press
41. Lipkovich, I., Dmitrienko, A., DAgostino, R. B. (2015). Tutorial in biostatistics: Exploratory subgroup analysis in clinical trials. Statistics in Medicine. To appear
42. Loh, W.-Y., Shih, Y.-S. (1997). Split selection methods for classification trees. Statistica Sinica 7: 815-840
43. Loh, W.-Y., He, X., Man, M. (2015). A regression tree approach to identifying subgroups with differential treatment effects. Statistics in Medicine 34: 1818-1833
44. Meinshausen, N., Bühlmann, P. (2010). Stability selection. Journal of Royal Statistical Society. Series B 72: 417-473
45. Millen, B., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework for decision making in confirmatory multi-population tailoring clinical trials. Drug Information Journal 46: 647-656
46. Millen, B., Dmitrienko, A., Song, G. (2014). Bayesian assessment of the influence and interaction conditions in multi-population tailoring clinical trials. Journal of Biopharmaceutical Statistics 24: 94-109
47. Millen, B., Dmitrienko, A., Mandrekar, S., Zhang, Z., Williams, D. (2014). Multi-population tailoring clinical trials: Design, analysis and inference considerations. Therapeutic Innovation and Regulatory Science 48: 453-462
48. Ondra, T., Dmitrienko, A., Friede, T., Graf, A., Miller, F., Stallard, N., Posch, M. (2015). Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. Journal of Biopharmaceutical Statistics. To appear
49. PMDA. (2012). Basic principles on global clinical trials (Reference cases). Japanese Ministry of Health, Pharmaceuticals and Medical Devices Agency
50. Royston, P., Altman, D. G. (1994). Regression using factional polynomials of continuous covariates: Parsimonious parametric modeling (with discussion). Applied Statistics 43: 429-467
51. Royston, P., Sauerbrei, W. (2004). A new approach to modelling interaction between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine 23: 2509-2525
52. Royston, P., Sauerbrei, W. (2013). Interaction of treatment with a continuous variable: Simulation study of significance level for several methods of analysis. Statistics in Medicine 32: 3788-3803
53. Ruberg, S. J., Shen. L. (2015). Personalized medicine: Four perspectives for clinical drug development. Statistics in Biopharmaceutical Research. To appear
54. Senn, S. (2007). Statistical issues in drug development (Second Edition). Chichester, UK: Wiley
55. Senn, S. J. (1994). Testing for baseline imbalance in clinical trials. Statistics in Medicine 13: 1715-1726
56. Simon, R. (2008). Subgroup Analysis. Wiley Encyclopedia of Clinical Trials. New York: John Wiley and Sons, Inc
57. 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
58. Su, X., Zhou, T., Yan, X., Fan, J., Yang, S. (2008). Interaction trees with censored survival data. The International Journal of Biostatistics 4 (1),. Article 2
59. Tian, L., Alizaden, A. A., Gentles, A. J., Tibshirani, R. (2012). A simple method for detecting interactions between a treatment and a large number of covariates. Available at http://arxiv.org/abs/1212.2995
60. Wang, S. J., Cohen, N., Katz, D. A., Ruano, G., Shaw, P.M, Spear, B. (2006). Retrospective validation of genomic biomarkers-what are the questions, challenges and strategies for developing useful relationships to clinical outcomes-Workshop summary. Pharmacogenomics Journal 6: 82-88
61. Wang, S. J., Hung, H. M. J. (2014). A regulatory perspective on essential considerations in design and analysis of subgroups when correctly classified. Journal of Biopharmaceutical Statistics 24: 19-41
62. White, I. R., Pocock, S. J., Wang, D. (2005): Eliciting and using expert opinions about influence of patient characteristics on treatment effects: A Bayesian analysis of the CHARM trials. Statistics in Medicine 25: 3805-3821
63. Yusuf, S., Wittes, J., Probstfield, J., Tyroler, H. A. (1991). Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. Journal of the American Medical Association 266: 93-98
64. Zhang, B., Tsiatis, A. A., Laber, E. B., Davidian, M. (2012) A robust method for estimating optimal treatment regimes. Biometrics 68: 1010-1018
65. 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
66. Zhao, Y., Zheng, 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.