Subgroup identification for treatment selection in biomarker adaptive design

Abstract

BackgroundAdvances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects.MethodsThe diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets.ResultsThe DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error.ConclusionAccuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

“…Based on these data sets, three different classification algorithms (diagonal linear discriminant analysis (dLDA) [ 19 , 20 ], k-nearest neighbors (K-NN) [ 20 , 21 ] and support vector machines (SVM) with linear kernel [ 22 , 23 ] were generated and tested. In order to formulate and select the best model for classification of LB, the misclassification rate for each classifier was estimated using leave-one-out cross-validation (LOOCV), during which we applied uni-variate t-tests with a grit of p-values (p < 0.001, p < 0.005, p < 0.01, p < 0.05) for selection of the optimal number of probe sets to include in each model.…”

A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease

“…5,8,9 Subgroup analyses represent a valuable source of information, but implementation in (R)CTs may lead to challenges as well, such as the pre-specification of relevant patient characteristics and respective cut-off values for the definition of subgroups. [10][11][12] Previous knowledge Subgroup analyses were often criticised for spurious, meaningless or potentially misleading results. 2,5,[13][14][15] Previous reviews showed that reported information regarding the implementation of subgroup analyses in (R)CTs was not consistent or complete.…”

“… 5 , 8 , 9 Subgroup analyses represent a valuable source of information, but implementation in (R)CTs may lead to challenges as well, such as the pre-specification of relevant patient characteristics and respective cut-off values for the definition of subgroups. 10 – 12 …”

Triel Bonds with Au Atoms as Electron Donors

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