Abstract
In this paper we enhance existing SIDES and SIDEScreen methods for biomarker discovery (Lipkovich et al., Stat. Med. 30:2601–2621, 2011; Lipkovich and Dmitrienko, J. Biopharm. Statist. 24:130–153, 2014; Lipkovich et al. Stat. Biopharm. Res. 9:368–378, 2017b) and apply it to a small Phase 2 clinical trial in patients with recurrent dysmenorrhea. We argue that incorporating stochastic elements in computing the variable importance, expected treatment effect and replicability index is particularly useful when dealing with relatively small data sets, so as to properly account for the uncertainty of the subgroup selection process. To demonstrate improved operating characteristics of the Stochastic SIDEScreen compared with the corresponding deterministic procedure, we conducted a small simulation study that mimics data from our Phase 2 trial. As analytical formulas for power calculations are not available for machine learning methods of biomarker/subgroup discovery, simulations utilizing existing early phase data should be conducted routinely for obtaining realistic estimates of power.
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This work is dedicated to the memory of James (Chip) Hackett.
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Lipkovich, I., Ratitch, B., Martell, B., Weiss, H., Dmitrienko, A. (2019). Evaluating Potential Subpopulations Using Stochastic SIDEScreen in a Cross-Over Trial. In: Zhang, L., Chen, DG., Jiang, H., Li, G., Quan, H. (eds) Contemporary Biostatistics with Biopharmaceutical Applications. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-15310-6_17
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