IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v11y2019i2d10.1007_s12561-019-09232-8.html
   My bibliography  Save this article

Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials

Author

Listed:
  • Yifan Zhu
  • Chongzhi Di
  • Ying Qing Chen

Abstract

Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper, we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches, are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network 069 study for identifying subgroups with distinct adherence behavior over the study period.

Suggested Citation

  • Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09232-8
    DOI: 10.1007/s12561-019-09232-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-019-09232-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-019-09232-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeng‐Min Chiou & Pai‐Ling Li, 2007. "Functional clustering and identifying substructures of longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 679-699, September.
    2. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    3. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    4. Hui Huang & Yehua Li & Yongtao Guan, 2014. "Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories With Application to Cocaine Abuse Treatment Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1412-1424, December.
    5. Peter Hall & Hans‐Georg Müller & Fang Yao, 2008. "Modelling sparse generalized longitudinal observations with latent Gaussian processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 703-723, September.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Holly Janes & Yifan Zhu & Elizabeth R. Brown, 2020. "Designing HIV Vaccine Efficacy Trials in the Context of Highly Effective Non-vaccine Prevention Modalities," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 468-494, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ja‐Yoon Jang & Hee‐Seok Oh & Yaeji Lim & Ying Kuen Cheung, 2021. "Ensemble clustering for step data via binning," Biometrics, The International Biometric Society, vol. 77(1), pages 293-304, March.
    2. Kim, Joonpyo & Oh, Hee-Seok, 2020. "Pseudo-quantile functional data clustering," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    3. Qingzhi Zhong & Huazhen Lin & Yi Li, 2021. "Cluster non‐Gaussian functional data," Biometrics, The International Biometric Society, vol. 77(3), pages 852-865, September.
    4. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    5. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    6. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    7. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    8. Gertheiss, Jan & Goldsmith, Jeff & Staicu, Ana-Maria, 2017. "A note on modeling sparse exponential-family functional response curves," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 46-52.
    9. Fang, Kuangnan & Chen, Yuanxing & Ma, Shuangge & Zhang, Qingzhao, 2022. "Biclustering analysis of functionals via penalized fusion," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.
    11. Juhyun Park & Jeongyoun Ahn, 2017. "Clustering multivariate functional data with phase variation," Biometrics, The International Biometric Society, vol. 73(1), pages 324-333, March.
    12. Snježana Majstorović & Kristian Sabo & Johannes Jung & Matija Klarić, 2018. "Spectral methods for growth curve clustering," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 715-737, September.
    13. C. Denis & E. Lebarbier & C. Lévy‐Leduc & O. Martin & L. Sansonnet, 2020. "A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 623-640, June.
    14. Julia Wrobel & Vadim Zipunnikov & Jennifer Schrack & Jeff Goldsmith, 2019. "Registration for exponential family functional data," Biometrics, The International Biometric Society, vol. 75(1), pages 48-57, March.
    15. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    16. Christina Yassouridis & Friedrich Leisch, 2017. "Benchmarking different clustering algorithms on functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 467-492, September.
    17. Elvira Romano & Jorge Mateu & Ramon Giraldo, 2015. "On the performance of two clustering methods for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 467-492, October.
    18. Cristina Anton & Iain Smith, 2024. "Model-based clustering of functional data via mixtures of t distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 563-595, September.
    19. Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    20. Javier Albert-Smet & Aurora Torrente & Juan Romo, 2023. "Band depth based initialization of K-means for functional data clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 463-484, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-019-09232-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.