An assessment of methods for adaptive patient selection based on perfect or imperfect biomarkers

Meinhard Kieser and Johannes Krisam - Institute of Medical Biometry and Informatics, University of Heidelberg

In the planning stage of a clinical trial investigating a potentially targeted therapy there is commonly a high degree of uncertainty whether the treatment is more efficient (or efficient only) in a subgroup as compared to the whole population. Recently developed adaptive designs enable to plan an efficacy assessment both for the whole population and a subgroup and to select the target population mid-course based on interim results (see, e.g., Brannath et al., 2009; Jenkins et al., 2011; Friede et al., 2012). Frequently, predictive biomarkers are used in these trials for identifying patients more likely to benefit from a drug. The performance of the applied subset selection rule is crucial for the overall characteristics of the design. We investigate the features of subgroup selection rules to be applied in adaptive two-stage designs. Both perfect and imperfect (e.g. sensitivity and/or specificity smaller than 100%) biomarkers are considered. The results are demonstrated by examples, and the consequences for planning clinical trials including biomarker-based patient selection are illustrated.

References

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Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharmaceutical Statistics 2011; 10: 347–356.

Friede T, Parsons N, Stallard N. A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine 2012. Epublished ahead of print (DOI: 10.1002/sim.5541).