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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model
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Developing a predictive signature for two trial endpoints using the cross-validated risk scores method.

Svetlana Cherlin1, James M S Wason2

  • 1Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK.

Biostatistics (Oxford, England)
|June 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, cross-validated risk scores 2 (CVRS2), to analyze treatments with two outcomes. CVRS2 effectively identifies patient groups benefiting from treatments across multiple measures, outperforming older methods in complex trials.

Keywords:
Clinical trialsHigh-dimensional dataInnovative designMultiple outcomesPrecision medicineRisk scores

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Outcomes Research

Background:

  • The cross-validated risk scores (CVRS) design is used for treatment efficacy testing in high-efficacy patient groups with high-dimensional data.
  • Existing methods may not adequately address situations requiring the evaluation of tradeoffs between two outcomes, such as efficacy and toxicity.

Purpose of the Study:

  • To extend the CVRS design to accommodate bivariate risk scores for analyzing two outcomes simultaneously.
  • To introduce the CVRS2 design for identifying patient subgroups that benefit from a treatment across two distinct outcomes.

Main Methods:

  • Development of the CVRS2 design utilizing bivariate risk scores and nonparametric clustering.
  • Assessment of CVRS2 properties through simulated data analysis.
  • Application of CVRS2 to a randomized psychiatry trial with offender and substance use status as outcomes.

Main Results:

  • CVRS2 reliably identified the sensitive patient group benefiting from treatment on both outcomes in simulated data.
  • In a psychology clinical trial, CVRS2 demonstrated significant treatment effects for both offender and substance use outcomes.
  • The original CVRS approach only detected a significant effect for offender status after prefiltering covariates.

Conclusions:

  • The CVRS2 design offers a robust method for evaluating treatments with dual outcomes, particularly in complex clinical trials.
  • CVRS2 enhances the ability to detect treatment benefits across multiple patient outcomes compared to traditional CVRS methods.
  • This approach is valuable for personalized medicine and optimizing treatment strategies when considering multiple therapeutic goals.