SESSION B

TITLE: Subgroup identification: a comparative review
SPEAKER: Wei-Yin Loh University of Wisconsin-Madison

 

Abstract:

Identification of subgroups with differential treatment effects is increasingly important nowadays when effective therapies for modern diseases (such as cancer) are hard to find while the amount of data grows in leaps and bounds. The good news is that several statistical methods for subgroup identification are available. The bad news, for the practitioner, is that there is little guidance to choose among them. As a result, many in the biopharma industry are doing their own in-house evaluation of the methods.

The aims of the tutorial are (1) to review the motivation, objectives, and limitations of the existing algorithms and (2) to present the results of a comprehensive head-to-head comparison of the methods. The algorithms include:

  1. BLASSO: Bayesian Lasso (Gu et al.,   2013)
  2. FindIt: Finding heterogeneous treatment effects (Imai and Ratkovic, 2013; Egami et al., 2017)
  3. GUIDE: Generalized unbiased interaction detection and estimation (Loh et al., 2015, 2016, 2018)
  4. IT: Interaction trees (Su et al., 2008, 2009, 2011)
  5. MOB: Model-based recursive partitioning (Zeileis et al., 2008; Seibold et al., 2016, 2017)
  6. QUINT: Qualitative interaction trees (Dusseldorp and Van Mechelen, 2014; Dusseldorp et al., 2016a, b)
  7. ROWSi: Regularized outcome weighted subgroup identification (Xu et al., 2015)
  8. SIDES: Subgroup identification based on differential effect search (Lipkovich et al., 2011; Lipkovich and Dmitrienko, 2014)
  9. SubgrpID: Patient subgroup identification for clinical drug development (Chen et al., 2015; Huang et al., 2017)
  10. VT: Virtual twins (Foster et al., 2011; Vieille, 2016)

Real and simulated data are used to compare the algorithms in terms of their probability of false positives (Type I error), power, bias in variable selection (probability of selecting the wrong biomarkers and hence wrong subgroups), bias in estimates of treatment effects, and computational speed. Included in the discussion are conceptual and inferential questions such as: “When there are multiple subgroups, which is the right one?” “Is the sample size large enough?” “Is the subgroup statistically significant?” “Are the treatment effects statistically significant?”

 

References

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