Startseite Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies
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Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies

  • Andrea S. Foulkes EMAIL logo , Livio Azzoni und Luis J. Montaner
Veröffentlicht/Copyright: 11. Dezember 2020
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Statistical Communications in Infectious Diseases
Aus der Zeitschrift Statistical Communications in Infectious Diseases Band 12 Heft s1

Abstract

Objectives

Exploratory studies that aim to evaluate novel therapeutic strategies in human cohorts often involve the collection of hundreds of variables measured over time on a small sample of individuals. Stringent error control for testing hypotheses in this setting renders it difficult to identify statistically signification associations. The objective of this study is to demonstrate how leveraging prior information about the biological relationships among variables can increase power for novel discovery.

Methods

We apply the class level association score statistic for longitudinal data (CLASS-LD) as an analysis strategy that complements single variable tests. An example is presented that aims to evaluate the relationships among 14 T-cell and monocyte activation variables measured with CD4 T-cell count over three time points after antiretroviral therapy (n=62).

Results

CLASS-LD using three classes with emphasis on T-cell activation with either classical vs. intermediate/inflammatory monocyte subsets detected associations in two of three classes, while single variable testing detected only one out of the 14 variables considered.

Conclusions

Application of a class-level testing strategy provides an alternative to single immune variables by defining hypotheses based on a collection of variables that share a known underlying biological relationship. Broader use of class-level analysis is expected to increase the available information that can be derived from limited sample clinical studies.

Keywords: biomarker analysis; CD4 recovery; clustered data methods; longitudinal data; multiple testing adjustments
Corresponding author: Andrea S. Foulkes, Biostatistics Center, Massachusetts General Hospital, Boston, USA; and Department of Medicine, Harvard Medical School, Boston, USA, E-mail:

Funding source: National Institutes of Health

Award Identifier / Grant number: UM1AI126620

Funding source: National Institute of Diabetes and Digestive and Kidney Diseases

Award Identifier / Grant number: DK103225

Funding source: National Institute of General Medical Sciences

Award Identifier / Grant number: GM127862

Funding source: National Institute of Allergy and Infectious Diseases

Award Identifier / Grant number: P30AI045008

Acknowledgement

This study was supported by grants to L.J.M.: NIH-funded BEAT-HIV Martin Delaney Collaboratory to cure HIV-1 infection (UM1AI126620, co-funded by NIAID, NIMH, NINDS, and NIDA), UPenn CFAR (P30AI045008), Kean Family Professorship, and the Roberts I. Jacobs of the Philadelphia Foundation and to A.S.F: NIH R01 GM127862 and the CKD Biomarkers Consortium Pilot and Feasibility Studies Program funded by the NIDDK U01 DK103225. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

  1. Research funding: This research was funded by National Institutes of Health, UM1AI126620; Kean Family Professorship, and the Philadelphia Foundation, (Roberts I. Jacobs Fund); National Institute of Diabetes and Digestive and Kidney Diseases, DK10322; National Institute of General Medical Sciences, GM127862; and National Institute of Allergy and Infectious Diseases, P30AI045008.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

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Received: 2019-10-22
Accepted: 2020-11-02
Published Online: 2020-12-11

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Editorial
  2. Forward
  3. Research Articles
  4. Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies
  5. Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials
  6. Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption
  7. Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data
  8. A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment
  9. Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities
  10. Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health
  11. Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations
https://doi.org/10.1515/scid-2019-0018
Schlagwörter für diesen Artikel
biomarker analysis; CD4 recovery; clustered data methods; longitudinal data; multiple testing adjustments

Artikel in diesem Heft

  1. Editorial
  2. Forward
  3. Research Articles
  4. Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies
  5. Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials
  6. Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption
  7. Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data
  8. A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment
  9. Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities
  10. Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health
  11. Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations
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