Subgroup Identification for Tailored Therapies: Methods and Consistent Evaluation

  • Lei ShenEmail author
  • Hollins Showalter
  • Chakib Battioui
  • Brian Denton
Part of the Emerging Topics in Statistics and Biostatistics book series (ETSB)


In contrast to the “one-size-fits-all” approach of traditional drug development, the paradigm of tailored therapeutics seeks to identify subjects with an enhanced treatment effect. In this chapter, we describe a statistical approach (TSDT) to subgroup identification that utilizes ensemble trees and resampling. For each potential subgroup identified, TSDT produces a multiplicity-adjusted strength of the subgroup finding as well as a bias-adjusted estimate of the treatment effect in the identified subgroup, both of which are important for decision-making in the development of tailored therapeutics. We describe a careful examination of simulation studies in a number of related publications, in order to determine the ideal framework to compare subgroup identification methods. A simulation study is performed to evaluate the performance of TSDT. The method has been implemented in a publicly available R package.


  1. Breiman L (1996) Bagging markers. Mach Learn 24:123–140zbMATHGoogle Scholar
  2. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  3. Breiman L, Stone CJ (1984) Classification and regression trees. Chapman & Hall, New YorkzbMATHGoogle Scholar
  4. 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(33):1–56CrossRefGoogle Scholar
  5. Dusseldorp E, Van Mechelen I (2014) Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Stat Med 33:219–237MathSciNetCrossRefGoogle Scholar
  6. Foster JC, Taylor JM, Ruberg SJ (2011) Subgroup identification from randomized clinical trial data. Stat Med 30(24):2867–2880MathSciNetCrossRefGoogle Scholar
  7. Friedman J (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232MathSciNetCrossRefGoogle Scholar
  8. Lipkovich I, Dmitrienko A (2014) Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES. J Biopharm Stat 24:130–153MathSciNetCrossRefGoogle Scholar
  9. 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. Stat Med 30(21):2601–2621MathSciNetGoogle Scholar
  10. Loh W-Y, He X, Man M (2015) A regression tree approach to subgroup identification for censored data. Stat Med 34(11):1818–1833MathSciNetCrossRefGoogle Scholar
  11. Negassa A, Ciampi A, Abrahamowicz M, Shapiro S, Boivin J-F (2005) Tree-structured subgroup analysis for censored survival data: validation of computationally inexpensive model selection criteria. Stat Comput 15:231–239MathSciNetCrossRefGoogle Scholar
  12. Rothwell PM (2005) Subgroup analysis in randomized controlled trials: importance, indications, and interpretation. Lancet 365:176–186CrossRefGoogle Scholar
  13. Ruberg SJ, Chen L, Wang Y (2010) The mean does not mean as much anymore: finding subgroups for tailored therapeutics. Clin Trials 7:574–583CrossRefGoogle Scholar
  14. Shen L, Ding Y, Battioui C (2015) A framework of statistical methods for identification of subgroups with differential treatment effects in randomized trials. In: Applied statistics in biomedicine and clinical trials design: selected papers from 2013 ICSA/ISBS Joint Statistical Meetings, Chapter 25. Springer, Cham, pp 411–425Google Scholar
  15. Su XG, Zhou T, Yan X, Fan J, Yang S (2008) Interaction trees with censored survival data. Int J Biostat 4(1):2MathSciNetCrossRefGoogle Scholar
  16. Su X, Tsai CL, Wang H, Nickerson DM, Li B (2009) Subgroup analysis via recursive partitioning. J Mach Learn Res 10:141–158Google Scholar
  17. Wang R, Lagakos SW, Ware JH et al (2007) Statistics in medicine – reporting of subgroup analyses in clinical trials. New Eng J Med 357:2189–2194CrossRefGoogle Scholar
  18. Wong CH, Siah KW, Lo AW (2019) Estimating clinical trial success rates and related parameters in oncology.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lei Shen
    • 1
    Email author
  • Hollins Showalter
    • 1
  • Chakib Battioui
    • 1
  • Brian Denton
    • 1
  1. 1.Eli Lilly and CompanyIndianapolisUSA

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