Two‐stage enrichment clinical trial design with adjustment for misclassification in predictive biomarkers

Abstract

A two‐stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis. In this paper, we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker‐positive cohort. We discuss the power analysis with the control of the type I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two‐stage enrichment over the one‐stage design and illustrate with examples of the recent successful development of immunotherapy in non–small‐cell lung cancer.

Two‐stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers

“…Misclassification of biomarker is common and needs to be properly adjusted in the clinical study designs. It is shown in Lin et al 18 that biomarker misclassification may have a big impact on type I error and power in the design of clinical studies. As shown in Shih and Lin, 23 stratified design is more efficient than precision medicine design, which is a family of complex biomarker-based-strategy designs discussed in, for instance, Mandrekar and Sargent [24][25][26] and Young et al 27 However, to the best of our knowledge, no stratified study design for survival outcomes, based on predictive biomarkers with an adjustment of misclassification, has been proposed in the statistical literature.…”

“…Zang and Guo 17 proposed a two‐stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. Lin et al 18 reported a two‐stage adaptive enrichment design for continuous endpoints, with an adjustment for the misclassification of predictive biomarkers. Those reports focused on the misclassification adjustment for continuous and/or binary endpoints.…”

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

“…This can be achieved through risk algorithms, 23,25,28,29 and imaging or circulating biomarkers with prognostic value. 22,[30][31][32] This approach, however, may affect the generalizability of a trial's findings and does not evaluate whether treatment allocation to specific patient phenotypes aligns with those most likely to benefit. To address this issue, predictive enrichment focuses on the individualized effects of the intervention within the context of a patient's unique phenotypic profile.…”

“…This can be achieved through risk algorithms, 23,25,28,29 and imaging or circulating biomarkers with prognostic value. 22,30,31 A representative example in the cardiovascular field is the use of coronary artery calcium scoring to enrich for individuals at high risk of adverse cardiovascular outcomes. 32 This approach, however, may affect the generalizability of a trial's findings and does not evaluate whether treatment allocation to specific patient phenotypes aligns with those most likely to benefit.…”