Abstract
There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure.We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group.We illustrate the application of the CVRS method on the psychiatry trial.
Highlights
It is increasingly common in clinical trials to collect a large amount of potentially high-dimensional data about patients such as genomic, imaging, and data from wearable technologies
We have presented a modification of the cross-validated ASD method (CVASD) method.[3]
By contrast to the existing CVASD method that considers a prespecified number or covariates reaching a prespecified significance level and a prespecified odds ratio, the cross-validated risk scores (CVRS) method allows all of the covariates to contribute to the construction of a signature, eliminating a need for the prior assumptions about the number of the true causal covariates, significance level and odds ratio
Summary
It is increasingly common in clinical trials to collect a large amount of potentially high-dimensional data about patients such as genomic, imaging, and data from wearable technologies. Genetic signatures that are constructed based on a combination of multiple variables such as gene expression profiling, have been used to determine a subpopulation in which the novel treatment is efficacious.[1,2,3,4]. The adaptive signature design (ASD)[2] allows a trial to develop and test efficacy of a treatment in a high-efficacy group of patients (the sensitive group) using two stages: the first stage is used to build a genetic signature, and in the second stage, the signature is applied to select the sensitive group.
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