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Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints

Author

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  • Ying Huang

    (Fred Hutchinson Cancer Research Center)

  • Aliasghar Tarkhan

    (University of Washington)

Abstract

In clinical trials, it is often of interest to compare and order several candidate regimens based on multiple endpoints. For example, in HIV vaccine development, immune response profiles induced by vaccination are key for selecting vaccine regimens to advance to efficacy evaluation. Motivated by the need to rank and choose a few vaccine regimens based on their immunogenicity in phase I trials, Huang et al. (Biostatistics 18(2):230–243, 2017) proposed a ranking/filtering/selection algorithm that down-selects vaccine regimens to satisfy the superiority and non-redundancy criteria, based on multiple immune response endpoints. In practice, many candidate immune response endpoints can be correlated with each other. An important question that remains to be addressed is how to choose a parsimonious set of the available immune response endpoints to effectively compare regimens. In this paper, we propose novel algorithms for selecting immune response endpoints to be used in regimen down-selection, based on importance weights assigned to individual endpoints and their correlation structure. We show through extensive simulation studies that pre-selection of endpoints can substantially improve performance of the subsequent regimen down-selection process. The application of the proposed method is demonstrated using a real example in HIV vaccine research, although the methods are also applicable in general to clinical research for dimension reduction when comparing regimens based on multiple candidate endpoints.

Suggested Citation

  • Ying Huang & Aliasghar Tarkhan, 2020. "Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 353-375, December.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:3:d:10.1007_s12561-020-09275-2
    DOI: 10.1007/s12561-020-09275-2
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    References listed on IDEAS

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    1. Ying Huang & Peter B. Gilbert, 2011. "Comparing Biomarkers as Principal Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 67(4), pages 1442-1451, December.
    2. Peter B. Gilbert & Michael G. Hudgens, 2008. "Evaluating Candidate Principal Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 64(4), pages 1146-1154, December.
    3. Ajit C. Tamhane, 2004. "A superiority-equivalence approach to one-sided tests on multiple endpoints in clinical trials," Biometrika, Biometrika Trust, vol. 91(3), pages 715-727, September.
    4. Daniel A. Bloch & Tze Leung Lai & Pascale Tubert-Bitter, 2001. "One-Sided Tests in Clinical Trials with Multiple Endpoints," Biometrics, The International Biometric Society, vol. 57(4), pages 1039-1047, December.
    5. Michael D. Perlman & Lang Wu, 2004. "A Note on One-Sided Tests with Multiple Endpoints," Biometrics, The International Biometric Society, vol. 60(1), pages 276-280, March.
    6. Ying Huang & Peter B. Gilbert & Julian Wolfson, 2013. "Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 301-309, June.
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