Development of predictive signatures for treatment selection in precision medicine with survival outcomes
Journal
Pharmaceutical Statistics
Journal Volume
17
Journal Issue
2
Pages
105-116
Date Issued
2018
Author(s)
Abstract
For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker-positive and biomarker-negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood-based change-point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change-point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially. Copyright ? 2018 John Wiley & Sons, Ltd.
Subjects
adaptive power; composite model; Cox proportional hazards model; likelihood ratio test; precision medicine; subgroup selection
SDGs
Other Subjects
algorithm; Article; controlled study; human; lung cancer; major clinical study; measurement accuracy; measurement error; overall survival; patient identification; personalized medicine; proportional hazards model; sample size; scoring system; sensitivity and specificity; statistical distribution; statistical model; survival prediction; survival time; univariate analysis; factual database; lung tumor; mortality; patient selection; personalized medicine; procedures; survival rate; treatment outcome; trends; Databases, Factual; Humans; Likelihood Functions; Lung Neoplasms; Patient Selection; Precision Medicine; Survival Rate; Treatment Outcome
Type
journal article