Skip to main content
SpringerLink
Log in

Data-Driven and Confirmatory Subgroup Analysis in Clinical Trials

  • Chapter
  • First Online:
Design and Analysis of Subgroups with Biopharmaceutical Applications

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

Abstract

In this chapter we provide an overview of the principles and practice of subgroup analysis in late-stage clinical trials. For convenience, we classify different subgroup analyses into two broad categories: data-driven and confirmatory. The two settings are different from each other primarily by the scope and extent of pre-specification of patient subgroups. First, we review key considerations in confirmatory subgroup analysis based on one or more pre-specified patient populations. This includes a survey of multiplicity adjustment methods recommended in multi-population Phase III clinical trials and decision-making considerations that ensure clinically meaningful inferences across the pre-defined populations. Secondly, we consider key principles for data-driven subgroup analysis and contrast it with that for a guideline-driven approach. Methods that emerged in the area of principled data-driven subgroup analysis in the last 10 years as a result of cross-pollination of machine learning, causal inference and multiple testing are reviewed. We provide examples of recommended approaches to data-driven and confirmatory subgroup analysis illustrated with data from Phase III clinical trials. We also illustrate common errors, pitfalls and misuse of subgroup analysis approaches in clinical trials often resulting from employing overly simplistic or naive methods. Overview of available statistical software and extensive bibliographical references are provided.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alosh M, Huque MF (2013) Multiplicity considerations for subgroup analysis subject to consistency constraint. Biom J 55:444–462

    Article  MathSciNet  MATH  Google Scholar 

  • Alosh M, Fritsch K, Huque M, Mahjoob K, Pennello G, Rothmann M, Russek-Cohen E, Smith F, Wilson S, Yue L (2015) Statistical considerations on subgroup analysis in clinical trials. Stat Biopharm Res 7(4):286–303

    Article  Google Scholar 

  • Alosh M, Huque MF, Bretz F, D’Agostino RB (2016) Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials. Stat Med 36:1334–1360

    Article  MathSciNet  Google Scholar 

  • Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci 113:7353–7360

    Article  MathSciNet  MATH  Google Scholar 

  • Ballarini NM, Rosenkranz GK, Jaki T, König F, Posch M (2018) Subgroup identification in clinical trials via the predicted individual treatment effect. PLoS One 13:e0205971

    Article  Google Scholar 

  • Battioui C, Denton B, Shen L (2018) TSDT: treatment-specific subgroup detection tool. R package version 1.0.0. https://CRAN.R-project.org/package=TSDT

  • Benda N, Branson M, Maurer W, Friede T (2010) Aspects of modernizing drug development using clinical scenario planning and evaluation. Drug Inf J 44:299–315

    Article  Google Scholar 

  • Berger J, Wang X, Shen L (2014) A Bayesian approach to subgroup identification. J Biopharm Stat 24:110–129

    Article  MathSciNet  Google Scholar 

  • Bornkamp B, Ohlssen D, Magnusson BP, Schmidli H (2017) Model averaging for treatment effect estimation in subgroups. Pharm Stat 16:133–142

    Article  Google Scholar 

  • Bonetti M, Gelber R (2000) A graphical method to assess treatment–covariate interactions using the cox model on subsets of the data. Stat Med 19:2595–2609

    Article  Google Scholar 

  • 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. Stat Med 28:1445–1463

    Article  MathSciNet  Google Scholar 

  • Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–231

    Article  MathSciNet  MATH  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont

    MATH  Google Scholar 

  • Bretz F, Schmidli H, Koenig F, Racine A, Maurer W (2006) Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: general concepts. Biom J 48:623–634

    Article  MathSciNet  Google Scholar 

  • Brookes ST, Whitley E, Peters TJ, Mulheran PA, Egger M, Davey Smith G (2001) Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives. Health Technol Assess 5:1–56

    Article  Google Scholar 

  • Cappuzzo F et al (2010) Erlotinib as maintenance treatment in advanced non-small-cell lung cancer: a multicentre, randomised, placebo-controlled Phase 3 study. Lancet Oncol 11:521–529

    Article  Google Scholar 

  • Carroll KJ, Fleming TR (2013) Statistical evaluation and analysis of regional interactions: the PLATO trial case study. Stat Biopharm Res 5(2):91–101

    Article  Google Scholar 

  • Carroll KJ, Le Maulf F (2011) Japanese guideline on global clinical trials: statistical implications and alternative criteria for assessing consistency. Drug Inf J 45:657–667

    Article  Google Scholar 

  • CFDA (China Food and Drug Administration) (2007) Provisions for drug registration. State Food and Drug Administration Order No. 28

    Google Scholar 

  • Chen G, Zhong H, Belousov A, Viswanath D (2015) PRIM approach to predictive-signature development for patient stratification. Stat Med 34:317–342

    Article  MathSciNet  Google Scholar 

  • Chipman HA, George EI, McCulloch RE (2010) BART: Bayesian additive regression trees. Ann Appl Stat 4:266–298

    Article  MathSciNet  MATH  Google Scholar 

  • Cohen AT et al (2016) Extended thromboprophylaxis with betrixaban in acutely ill medical patients. N Engl J Med 375:534–544

    Article  Google Scholar 

  • Dane A, Spencer A, Rosenkranz G, Lipkovich I, Parke T on behalf of the PSI/EFSPI Working Group on Subgroup Analysis (2019) Subgroup analysis and interpretation for phase 3 confirmatory trials: white paper of the EFSPI/PSI working group on subgroup analysis. Pharm Stat 18:126–139. https://doi.org/10.1002/pst.1919

  • Dixon DO, Simon R (1991) Bayesian subset analysis. Biometrics 47:871–882

    Article  MATH  Google Scholar 

  • Dmitrienko A, D’Agostino RB (2013) Tutorial in biostatistics: traditional multiplicity adjustment methods in clinical trials. Stat Med 32:5172–5218

    Article  MathSciNet  Google Scholar 

  • Dmitrienko A, D’Agostino RB (2018) Multiplicity considerations in clinical trials. N Engl J Med 378:2115–2122

    Article  Google Scholar 

  • Dmitrienko A, Paux G (2017) Subgroup analysis in clinical trials. In: Dmitrienko A, Pulkstenis E (eds) Clinical trial optimization using R. Chapman and Hall/CRC Press, New York

    Chapter  MATH  Google Scholar 

  • Dmitrienko A, Tamhane AC (2011) Mixtures of multiple testing procedures for gatekeeping applications in clinical trials. Stat Med 30:1473–1488

    Article  MathSciNet  Google Scholar 

  • Dmitrienko A, Tamhane AC (2013) General theory of mixture procedures for gatekeeping. Biom J 55:402–419

    Article  MathSciNet  MATH  Google Scholar 

  • Dmitrienko A, Soulakova JN, Millen B (2011) Three methods for constructing parallel gatekeeping procedures in clinical trials. J Biopharm Stat 53:768–786

    Article  MathSciNet  Google Scholar 

  • Dmitrienko A, Muysers C, Fritsch A, Lipkovich I (2016) General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials. J Biopharm Stat 26:71–98

    Article  Google Scholar 

  • Douillard JY et al (2014) Final results from PRIME: randomized phase 3 study of panitumumab with FOLFOX4 for first-line treatment of metastatic colorectal cancer. Ann Oncol 25:1346–1355

    Article  Google Scholar 

  • Dusseldorp E, Van Mechelen I (2014) Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Stat Med 33:219–237

    Article  MathSciNet  Google Scholar 

  • Dusseldorp E, Conversano C, Van Os BJ (2010) Combining an additive and tree-based regression model simultaneously: STIMA. J Comput Graph Stat 19:514–530

    Article  MathSciNet  Google Scholar 

  • EMA (European Medicines Agency) (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

    Google Scholar 

  • EMA (European Medicines Agency) (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

    Google Scholar 

  • EMA (European Medicines Agency) (2015) Guideline on adjustment for baseline covariates in clinical trials. European Medicines Agency/Committee for Medicinal Products for Human Use. EMA/CHMP/295050/2013

    Google Scholar 

  • EMA (European Medicines Agency) (2017) Guideline on multiplicity issues in clinical trials. Draft. European Medicines Agency/Committee for Medicinal Products for Human Use. EMA/CHMP/44762/2017

    Google Scholar 

  • FDA (U.S. Food and Drug Administration) (2014) Guidance: evaluation of sex-specific data in medical device clinical studies. https://www.fda.gov/media/82005/download

  • FDA (U.S. Food and Drug Administration) (2017a) Guidance for industry: evaluation and reporting of age-, race-, and ethnicity-specific data in medical device clinical studies; doc number 1500626, pp 1–36. https://www.fda.gov/media/98686/download

  • FDA (U.S. Food and Drug Administration) (2017b) Guidance for industry: multiple endpoints in clinical trials. https://www.fda.gov/media/102657/download

  • FDA (U.S. Food and Drug Administration) (2018) Guidance for industry: adaptive design clinical trials for drugs and biologics. https://www.fda.gov/media/78495/download

  • FDA (U.S. Food and Drug Administration) (2019) Guidance for industry: enrichment strategies for clinical trials to support determination of effectiveness of human drugs and biological products. https://www.fda.gov/media/121320/download

  • Foster JC, Taylor JMC, Ruberg SJ (2011) Subgroup identification from randomized clinical trial data. Stat Med 30:2867–2880

    Article  MathSciNet  Google Scholar 

  • Freidlin B, Simon R (2005) Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res 21:7872–7878

    Article  Google Scholar 

  • Freidlin B, McShane LM, Korn EL (2010) Randomized clinical trials with biomarkers: design issues. J Natl Cancer Inst 102:152–160

    Article  Google Scholar 

  • Freidlin B, Korn EL, Gray R (2014) Marker sequential test (MaST) design. Clin Trials 11:19–27

    Article  Google Scholar 

  • Friede T, Parsons N, Stallard N (2012) A conditional error function approach for subgroup selection in adaptive clinical trials. Stat Med 31:4309–4320

    Article  MathSciNet  Google Scholar 

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378

    Article  MathSciNet  MATH  Google Scholar 

  • Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9:123–143

    Article  Google Scholar 

  • Fu H (2018) Individualized treatment recommendation (ITR) for survival outcomes. Presentation at conference on statistical learning and data science/nonparametric statistics. Columbia University, New York. https://publish.illinois.edu/sldsc2018/2018/05/20/session-35-machine-learning-and-precision-medicine/

    Google Scholar 

  • Fu H, Zhou J, Faries DE (2016) Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies. Stat Med 35:3285–3302

    Article  MathSciNet  Google Scholar 

  • Graf AC, Wassmer G, Friede T, Gera RG, Posch M (2019) Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker. Stat Methods Med Res 28:1879–1892

    Article  MathSciNet  Google Scholar 

  • Graf AC, Magirr D, Dmitrienko A, Posch M (2020) Optimized multiple testing procedures for nested subpopulations based on a continuous biomarker. Stat Med. To appear

    Google Scholar 

  • Gunter L, Zhu J, Murphy S (2011) Variable selection for qualitative interactions in personalized medicine while controlling the familywise error rate. J Biopharm Stat 21:1063–1078

    Article  Google Scholar 

  • Hemmings R (2014) An overview of statistical and regulatory issues in the planning, analysis, and interpretation of subgroup analyses in confirmatory clinical trials. J Biopharm Stat 24:4–18

    Article  MathSciNet  Google Scholar 

  • Hemmings R (2015) Comment. Stat Biopharm Res 7:305–308

    Article  Google Scholar 

  • Henderson NC, Louis TA, Rosner G, Varadhan R (2020) Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models. Biostatistics 21:50–68. https://doi.org/10.1093/biostatistics/kxy02

    Article  MathSciNet  Google Scholar 

  • Herbst RS et al (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387:1540–1550

    Article  Google Scholar 

  • Hill JL (2016) Bayesian nonparametric modeling for causal inference. J Comput Graph Stat 20:217–240

    Article  MathSciNet  Google Scholar 

  • Hirakawa A, Kinoshita F (2017) An analysis of Japanese patients enrolled in multiregional clinical trials in oncology. Ther Innov Regul Sci 51:207–211

    Article  Google Scholar 

  • Hodges JS, Cui Y, Sargent DJ, Carlin BP (2007) Smoothing balanced single-error-term analysis of variance. Technometrics 49:12–25

    Article  MathSciNet  Google Scholar 

  • Huang Y, Fong Y (2014) Identifying optimal biomarker combinations for treatment selection via a robust kernel method. Biometrics 70:891–901

    Article  MathSciNet  MATH  Google Scholar 

  • Huang X, Sun Y, Trow P, Chatterjee S, Chakravatty A, Tian L, Devanarayan V (2017) Patient subgroup identification for clinical drug development. Stat Med 36:1414–1428

    Article  MathSciNet  Google Scholar 

  • ICH (1998) Ethnic factor in the acceptability of foreign data. ICH E5 Expert Working Group. The US Federal Register, vol 83, pp 31790–31796

    Google Scholar 

  • ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) (1999) Topic E9 Statistical principles for clinical trials. CPMP/ICH/363/96

    Google Scholar 

  • 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

    Google Scholar 

  • ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) (2017) Guideline E17 on general principles for planning and design of multi-regional clinical trials. EMA/CHMP/ICH/453276/2016

    Google Scholar 

  • Ikeda K, Bretz F (2010) Sample size and proportion of Japanese patients in multi-regional trials. Pharm Stat 9:207–216

    Article  Google Scholar 

  • Imai K, Ratkovic M (2013) Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat 7:443–470

    Article  MathSciNet  MATH  Google Scholar 

  • Johnston S et al (2009) Lapatinib combined with letrozole versus letrozole and placebo as first-line therapy for postmenopausal hormone receptor-positive metastatic breast cancer. J Clin Oncol 33:5538–5546

    Article  Google Scholar 

  • Kehl V, Ulm K (2006) Responder identification in clinical trials with censored data. Comput Stat Data An 50:1338–1355

    Article  MathSciNet  MATH  Google Scholar 

  • Koch A, Framke T (2014) Reliably basing conclusions on subgroups of randomized clinical trials. J Biopharm Stat 24:42–57

    Article  MathSciNet  Google Scholar 

  • Koch G, Schwartz TA (2014) An overview of statistical planning to address subgroups in confirmatory clinical trials. J Biopharm Stat 24:72–93

    Article  MathSciNet  Google Scholar 

  • Laber EB, Zhao YQ (2015) Tree-based methods for individualized treatment regimes. Biometrika 102:501–514

    Article  MathSciNet  MATH  Google Scholar 

  • Lamont AE, Lyons M, Jaki TF, Stuart E, Feaster D, Ishwaran H, Tharmaratnam K, Van Horn ML (2018) Identification of predicted individual treatment effects in randomized clinical trials. Stat Methods Med Res 27:142–157

    Article  MathSciNet  Google Scholar 

  • Leblanc M, Crowley J (1993) Survival trees by goodness of split. J Am Stat Assoc 88:457–467

    Article  MathSciNet  MATH  Google Scholar 

  • Lee JD, Sun DL, Sun Y, Taylor JE (2016) Exact post-selection inference, with application to the lasso. Ann Stat 44:907–927

    Article  MathSciNet  MATH  Google Scholar 

  • Lipkovich I, Dmitrienko A (2014a) Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect clinical trials using SIDES. J Biopharm Stat 24:130–153

    Article  MathSciNet  Google Scholar 

  • Lipkovich I, Dmitrienko A (2014b) Biomarker identification in clinical trials. In: Carini C, Menon S, Chang M (eds) Clinical and statistical considerations in personalized medicine. Chapman and Hall/CRC Press, New York

    Google Scholar 

  • 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 patient subpopulations. Stat Med 30:2601–2621

    MathSciNet  Google Scholar 

  • Lipkovich I, Dmitrienko A, D’Agostino BR (2017a) Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials. Stat Med 36:136–196

    Article  MathSciNet  Google Scholar 

  • Lipkovich I, Dmitrienko A, Patra K, Ratitch B, Pulkstenis E (2017b) Subgroup identification in clinical trials by Stochastic SIDEScreen methods. Stat Biopharm Res 9:368–378

    Article  Google Scholar 

  • Lipkovich I, Dmitrienko A, Muysers C, Ratitch B (2018) Multiplicity issues in exploratory subgroup analysis. J Biopharm Stat 28:63–81

    Article  Google Scholar 

  • Liu JT, Tsou HH, Gordon Lan KK et al (2016) Assessing the consistency of the treatment effect under the discrete random effects model in multiregional clinical trials. Stat Med 35:2301–2314

    Article  MathSciNet  Google Scholar 

  • Loh WY, He X, Man M (2015) A regression tree approach to identifying subgroups with differential treatment effects. Stat Med 34:1818–1833

    Article  MathSciNet  Google Scholar 

  • Loh WY, Fu H, Man M, Champion V, Yu M (2016) Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables. Stat Med 35:4837–4855

    Article  MathSciNet  Google Scholar 

  • Lu M, Sadiq S, Feaster DJ, Ishwaran H (2018) Estimating individual treatment effect in observational data using random Forest methods. J Comput Graph Stat 27:209–219

    Article  MathSciNet  Google Scholar 

  • Mahaffey KW, Wojdyla DM, Carroll K, Becker RC, Storey RF, Angiolillo DJ, Held C, Cannon CP, James S, Pieper KS, Horrow J, Harrington RA, Wallentin L (2011) Ticagrelor compared with clopidogrel by geographic region in the Platelet Inhibition and Patient Outcomes (PLATO) Trial. Circulation 124:544–554

    Article  Google Scholar 

  • Mayer C, Lipkovich I, Dmitrienko A (2015) Survey results on industry practices and challenges in subgroup analysis in clinical trials. Stat Biopharm Res 7:272–282

    Article  Google Scholar 

  • Millen B, Dmitrienko A, Ruberg S, Shen L (2012) A statistical framework for decision making in confirmatory multi-population tailoring clinical trials. Drug Inf J 46:647–656

    Article  Google Scholar 

  • Millen B, Dmitrienko A, Song G (2014) Bayesian assessment of the influence and interaction conditions in multi-population tailoring clinical trials. J Biopharm Stat 24:94–109

    Article  Google Scholar 

  • NMPA (China National Medical Product Administration) (2015) Guidance for international multicenter clinical trials (IMCT). Issued final trial implementation, as of March 2015

    Google Scholar 

  • Paux G, Dmitrienko A (2018) Penalty-based approaches to evaluating multiplicity adjustments in clinical trials: traditional multiplicity problems. J Biopharm Stat 28:146–168

    Article  Google Scholar 

  • Piccart-Gebhart MJ et al (2005) Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 353:1659–1672

    Article  Google Scholar 

  • PMDA (Pharmaceuticals and Medical Devices Agency) (2007) Ministry of Health, Labour and Welfare. Basic principles on global clinical trials. https://www.pmda.go.jp/files/000153265.pdf

  • PMDA (Pharmaceuticals and Medical Devices Agency) (2012) Ministry of Health, Labour and Welfare. Basic principles on global clinical trials (Reference cases). https://www.pmda.go.jp/files/000157520.pdf

  • Qian M, Murphy SA (2011) Performance guarantees for individualized treatment rules. Ann Stat 39:1180–1210

    Article  MathSciNet  MATH  Google Scholar 

  • Ridgeway G (1999) The state of boosting. Comput Sci Stat 31:172–181

    Google Scholar 

  • Rosenkranz GK (2016) Exploratory subgroup analysis in clinical trials by model selection. Biom J 58:1217–1228

    Article  MathSciNet  MATH  Google Scholar 

  • Rothmann MD, Zhang JJ, Lu L, Fleming TR (2012) Testing in a pre-specified subgroup and the intent-to-treat population. Drug Inf J 46:175–179

    Article  Google Scholar 

  • Rothwell PM (2005) Subgroup analysis in randomized controlled trials: importance, indications, and interpretation. Lancet 365:176–86

    Article  Google Scholar 

  • Royston P, Sauerbrei W (2004) A new approach to modelling interaction between treatment and continuous covariates in clinical trials by using fractional polynomials. Stat Med 23:2509–2525

    Article  Google Scholar 

  • Royston P, Sauerbrei W (2013) Interaction of treatment with a continuous variable: simulation study of power for several methods of analysis. Stat Med 32:3788–3803

    Article  MathSciNet  Google Scholar 

  • Russek-Cohen E (2014) EMA workshop on the investigation of subgroups in confirmatory clinical trials. Presentation available online: https://www.ema.europa.eu/documents/presentation/presentation-comments-us-food-drug-administration-fdaworking-group-subgroup-analyses-estelle-russek_en.pdf

  • Seibold H, Zeileis A, Hothorn T (2016) Model-based recursive partitioning for subgroup analyses. Int J Biostat 12:45–63

    Article  MathSciNet  Google Scholar 

  • Seibold H, Zeileis A, Hothorn T (2018) Individual treatment effect prediction for ALS patients. Stat Methods Med Res 10:3104–3125

    Article  Google Scholar 

  • Su X, Zhou T, Yan X, Fan J, Yang S (2008) Interaction trees with censored survival data. Int J Biostat 4(1):Article 2

    Google Scholar 

  • Su X, Tsai CL, Wang H, Nickerson DM, Li B (2009) Subgroup analysis via recursive partitioning. J Mach Learn Res 10:141–158

    Google Scholar 

  • Su X, Peña AT, Liu L, Levine RA (2018) Random forests of interaction trees for estimating individualized treatment effects in randomized trials. Stat Med 37:2547–2560

    Article  MathSciNet  Google Scholar 

  • Sun X, Briel M, Walter SD, Guyatt GH (2010) Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ 340:c117

    Article  Google Scholar 

  • Taylor J, Tibshirani R (2017) Post-selection inference for l1-penalized likelihood models. Can J Stat 1:1–21

    Google Scholar 

  • Tian L, Alizaden AA, Gentles AJ, Tibshirani R (2014) A simple method for detecting interactions between a treatment and a large number of covariates. J Am Stat Assoc 109:1517–1532

    Article  MATH  Google Scholar 

  • Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 113:1228–1242

    Article  MathSciNet  MATH  Google Scholar 

  • Wager S, Hastie T, Efron B (2014) Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. J Mach Learn Res 15:1625–1651

    MathSciNet  MATH  Google Scholar 

  • Wang SJ, Dmitrienko A (2014) Guest Editors’ Note: Special issue on subgroup analysis in clinical trials. J Biopharm Stat 24:1–3

    Article  MathSciNet  Google Scholar 

  • Wang SJ, Hung HMJ (2014) A regulatory perspective on essential considerations in design and analysis of subgroups when correctly classified. J Biopharm Stat 24:19–41

    Article  MathSciNet  Google Scholar 

  • Wang SJ, O’Neill RT, Hung HMJ (2007) Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 6:227–244

    Article  Google Scholar 

  • Wassmer G, Brannath W (2016) Group sequential and confirmatory adaptive designs in clinical trials. Springer, New York

    Book  MATH  Google Scholar 

  • Xu Y, Yu M, Zhao YQ, Li Q, Wang S, Shao J (2015) Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics 71:645–653

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang B, Tsiatis AA, Davidian M, Zhang M, Laber EB (2012) Estimating optimal treatment regimes from a classification perspective. Statistics 1:103–114

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang Y, Laber EB, Tsiatis A, Davidian M (2016) Interpretable dynamic treatment regimes. Preprint. arXiv:1606.01472

    Google Scholar 

  • Zhao Y, Zheng D, Rush AJ, Kosorok MR (2012) Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc 107:1106–1118

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao Y, Zheng W, Zhuo DY, Lu Y, Ma X, Liu H, Zeng Z, Laird G (2018) Bayesian additive decision trees of biomarker-by treatment interactions for predictive biomarkers detection and subgroup identification. J Biopharm Stat 28:534–549

    Article  Google Scholar 

Download references

Acknowledgement

We are grateful to Lei Xu, Anthony Zagar, Lanju Zhang and the book’s editors for their insightful comments.

Author information

Authors and Affiliations

  1. Mediana Inc., Overland Park, KS, USA

    Alex Dmitrienko

  2. Eli Lilly and Company, Indianapolis, IN, USA

    Ilya Lipkovich

  3. Dane Statistics, Manchester, UK

    Aaron Dane

  4. Bayer AG Pharmaceuticals, Berlin, Germany

    Christoph Muysers

Authors
  1. Alex Dmitrienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilya Lipkovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron Dane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christoph Muysers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Dmitrienko .

Editor information

Editors and Affiliations

  1. Biostatistics & Data Sciences, Boehringer Ingelheim Corporation, Ridgefield, CT, USA

    Naitee Ting

  2. Pfizer Inc, Groton, CT, USA

    Joseph C. Cappelleri

  3. UCB Biosciences Inc., Raleigh, NC, USA

    Shuyen Ho

  4. School of Social Work, University of North Carolina, Chapel Hill, NC, USA

    (Din) Ding-Geng Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dmitrienko, A., Lipkovich, I., Dane, A., Muysers, C. (2020). Data-Driven and Confirmatory Subgroup Analysis in Clinical Trials. In: Ting, N., Cappelleri, J., Ho, S., Chen, (G. (eds) Design and Analysis of Subgroups with Biopharmaceutical Applications. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-40105-4_3

Download citation

Publish with us

Policies and ethics

Search

Navigation