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2. A Cross-Validated Targeted Maximum Likelihood Estimator for Data-Adaptive Experiment Selection Applied to the Augmentation of RCT Control Arms with External Data

Author

Dang, Lauren Eyler, Tarp, Jens Magelund, Abrahamsen, Trine Julie, Kvist, Kajsa, Buse, John B, Petersen, Maya, and van der Laan, Mark

Subjects
Statistics - Methodology
Abstract

Augmenting the control arm of a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. Existing data fusion estimators generally rely on stringent assumptions or may have decreased coverage or power in the presence of bias. Framing the problem as one of data-adaptive experiment selection, potential experiments include the RCT only or the RCT combined with different candidate real-world datasets. To select and analyze the experiment with the optimal bias-variance tradeoff, we develop a novel experiment-selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE). The ES-CVTMLE uses two bias estimates: 1) a function of the difference in conditional mean outcome under control between the RCT and combined experiments and 2) an estimate of the average treatment effect on a negative control outcome (NCO). We define the asymptotic distribution of the ES-CVTMLE under varying magnitudes of bias and construct confidence intervals by Monte Carlo simulation. In simulations involving violations of identification assumptions, the ES-CVTMLE had better coverage than test-then-pool approaches and an NCO-based bias adjustment approach and higher power than one implementation of a Bayesian dynamic borrowing approach. We further demonstrate the ability of the ES-CVTMLE to distinguish biased from unbiased external controls through a re-analysis of the effect of liraglutide on glycemic control from the LEADER trial. The ES-CVTMLE has the potential to improve power while providing relatively robust inference for future hybrid RCT-RWD studies., Comment: 27 pages, 4 figures

Published
2022

5. Occurrence of Gastrointestinal Adverse Events Upon GLP-1 Receptor Agonist Initiation With Concomitant Metformin Use: A Post Hoc Analysis of LEADER, STEP 2, SUSTAIN-6, and PIONEER 6.

Author

Klein, Klara R., Clemmensen, Kim K.B., Fong, Edwin, Olsen, Søren, Abrahamsen, Trine, and Lingvay, Ildiko

Subjects
GLUCAGON-like peptide-1 agonists, GLUCAGON-like peptide 1, METFORMIN, TYPE 2 diabetes, PRODUCT elimination
Abstract

OBJECTIVE: To assess the impact of concomitant metformin use on gastrointestinal adverse events during the initiation and titration of a glucagon-like peptide 1 receptor agonist (GLP-1RA). RESEARCH DESIGN AND METHODS: Using data from four clinical trials of liraglutide and semaglutide (Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results [LEADER], Semaglutide Treatment Effect in People with Obesity [STEP 2], Trial to Evaluate Cardiovascular and Other Long-Term Outcomes With Semaglutide in Subjects With Type 2 Diabetes [SUSTAIN-6], and Peptide Innovation for Early Diabetes Treatment [PIONEER] 6), we compared the incidence of gastrointestinal adverse events during GLP-1RA initiation and titration in participants with and without concomitant metformin use. RESULTS: Of 16,996 participants, 12,928 (76%) were treated with metformin. Concomitant metformin use did not increase the percentage of participants who developed gastrointestinal adverse events or their severity during the observation window. Among participants experiencing gastrointestinal adverse events, metformin use did not increase study product discontinuation. Within treatment arms (GLP-1RA and placebo), a numerically higher percentage of metformin nonusers experienced gastrointestinal adverse events and discontinued the study product compared with metformin users. CONCLUSIONS: Concomitant metformin use does not increase occurrence of gastrointestinal symptoms during GLP-1RA initiation or impact GLP-1RA discontinuation. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data

Author

Abrahamsen, Trine Julie, Hansen, Lars Kai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Langs, Georg, editor, Rish, Irina, editor, Grosse-Wentrup, Moritz, editor, and Murphy, Brian, editor

Published
2012
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9. Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study.

Author

Bramante, Carolyn T., Johnson, Steven G., Garcia, Victor, Evans, Michael D., Harper, Jeremy, Wilkins, Kenneth J., Huling, Jared D., Mehta, Hemalkumar, Alexander, Caleb, Tronieri, Jena, Hong, Stephenie, Kahkoska, Anna, Alamgir, Joy, Koraishy, Farrukh, Hartman, Katrina, Yang, Kaifeng, Abrahamsen, Trine, Stürmer, Til, and Buse, John B.

Subjects
COVID-19 pandemic, VENTILATION, LIFE cycles (Biology), VACCINATION, DRUGS, COHORT analysis
Abstract

Background: While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size. Methods: Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome. Results: 6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32–1.44), and mortality (RR 0.82, 0.41–1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28–0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33–0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control. Conclusions: There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium–Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes

Author

Kahkoska, Anna R., primary, Abrahamsen, Trine Julie, primary, Alexander, G. Caleb, primary, Bennett, Tellen D., primary, Chute, Christopher G., primary, Haendel, Melissa A., primary, Klein, Klara R., primary, Mehta, Hemalkumar, primary, Miller, Joshua D., primary, Moffitt, Richard A., primary, Stürmer, Til, primary, Kvist, Kajsa, primary, and Buse, John B., primary

Published
2021
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13. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium-Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes.

Author

Kahkoska, Anna R., Abrahamsen, Trine Julie, Alexander, G. Caleb, Bennett, Tellen D., Chute, Christopher G., Haendel, Melissa A., Klein, Klara R., Mehta, Hemalkumar, Miller, Joshua D., Moffitt, Richard A., Stürmer, Til, Kvist, Kajsa, Buse, John B., Duong, Tim Q., and N3C Consortium

Subjects
*SODIUM-glucose cotransporter 2 inhibitors, *GLUCAGON-like peptide 1, *PEPTIDE receptors, *COVID-19, *GLUCAGON-like peptide-1 receptor, *THROMBOPOIETIN receptors
Abstract

Objective: To determine the respective associations of premorbid glucagon-like peptide-1 receptor agonist (GLP1-RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) use, compared with premorbid dipeptidyl peptidase 4 inhibitor (DPP4i) use, with severity of outcomes in the setting of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.Research Design and Methods: We analyzed observational data from SARS-CoV-2-positive adults in the National COVID Cohort Collaborative (N3C), a multicenter, longitudinal U.S. cohort (January 2018-February 2021), with a prescription for GLP1-RA, SGLT2i, or DPP4i within 24 months of positive SARS-CoV-2 PCR test. The primary outcome was 60-day mortality, measured from positive SARS-CoV-2 test date. Secondary outcomes were total mortality during the observation period and emergency room visits, hospitalization, and mechanical ventilation within 14 days. Associations were quantified with odds ratios (ORs) estimated with targeted maximum likelihood estimation using a super learner approach, accounting for baseline characteristics.Results: The study included 12,446 individuals (53.4% female, 62.5% White, mean ± SD age 58.6 ± 13.1 years). The 60-day mortality was 3.11% (387 of 12,446), with 2.06% (138 of 6,692) for GLP1-RA use, 2.32% (85 of 3,665) for SGLT2i use, and 5.67% (199 of 3,511) for DPP4i use. Both GLP1-RA and SGLT2i use were associated with lower 60-day mortality compared with DPP4i use (OR 0.54 [95% CI 0.37-0.80] and 0.66 [0.50-0.86], respectively). Use of both medications was also associated with decreased total mortality, emergency room visits, and hospitalizations.Conclusions: Among SARS-CoV-2-positive adults, premorbid GLP1-RA and SGLT2i use, compared with DPP4i use, was associated with lower odds of mortality and other adverse outcomes, although DPP4i users were older and generally sicker. [ABSTRACT FROM AUTHOR]

Published
2021
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16. Impact de l’IMC sur la réduction de l’HbA 1c sous IDegLira chez des diabétiques de type 2 insuffisamment contrôlés sous sulfamides, agonistes des récepteurs au GLP-1 ou insuline Glargine U100 : analyses d’études de phase IIIb

Author

Cariou, Bertrand, primary, Belhatem, Narimène, additional, Jodar, Esteban, additional, Harris, Stewart, additional, Jaeckel, Elmar, additional, Lingvay, Ildiko, additional, Chandrana, keval, additional, Abrahamsen, Trine, additional, and Sorli, Christopher, additional

Published
2017
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19. Kernel Methods for Machine Learning with Life Science Applications

Author

Abrahamsen, Trine Julie

Abstract

Denne afhandling omhandler kernelmetoder til ikke-lineær dataanalyse. Kernelmetoder er en fællesbetegnelse for algoritmer, der benytter det såkaldte kerneltrick til at formulere ikke-lineære udvidelser af klassiske lineære algoritmer. Dette kan gøres så længe at data kun indgår som indreprodukter i den lineære model. Overordnet har denne afhandling to hovedmål. Den første del omhandler stabil støjreduktion ved kernel Principal Komponent Analyse hvorefter variansinflations problemet undersøges i relation til kernellæring.Når kernel Principal Component Analysis anvendes til støjreduktion, er løsning af det inverse ill-posed pre-image problem essentielt. Stabil pre-image estimering udgør i denne forbindelse den største udfordring. Denne afhandling præsenterer nye pre-image algoritmer til forbedret støjreduktion ved at introducere henholdsvis `1- og `2-norm regularisering. Eksperimenter på håndskrevne tal samt biomedicinske datasæt illustrerer effekten af de nye estimatorer. Derudover introduceres metoder til at forbedre støjreduktionen, når klasse information er tilgængelig for en del af dataen.Den anden del af denne afhandling omhandler varians-inflation i kernelmetoder. Varians-inflation kan forekomme i høj-dimensionale problemer, når mængden af træningsdata er utilstrækkelig til at repræsentere signalmanifolden. Dette medfører et muligt mismatch mellem underrummet udspændt af henholdsvis trænings- og testdata. I denne afhandling vises det, hvordan varians problemet forefindes i kernelalgoritmer, og metoder til at korrigere for det forøget variansestimat i både kernel Principal Komponent Analyse og Support Vektor Maskiner præsenteres. Standard machine learning datasæt anvendes til at illustrere, hvordan de foreslåede algoritmer gendanner generaliserbarheden. Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear as innerproducts in the model formulation. This dissertation presents research on improving the performance of standard kernel methods like kernel Principal Component Analysis and the Support Vector Machine. Moreover, the goal of the thesis has been two-fold.The first part focuses on the use of kernel Principal Component Analysis for nonlinear denoising. In this context stable solution of the inverse and inherently ill-posed pre-image problem constitutes the main challenge. It is proposed to stabilize the estimation by augmenting the cost function with either an `1-or `2-norm penalty, and solution schemes are derived for both approaches. The methods are experimentally validated on several biomedical data sets. Furthermore, frameworks for exploiting label information for improved denoising in the semisupervised case are proposed.The second part of the thesis examines the effect of variance inflation in kernel methods. Variance inflation occurs in high-dimensional problems when the training data are insufficient to describe the entire signal manifold. Thereby leading to a potential mismatch between the subspaces spanned by the training and test data, respectively. It is shown how this effect extends from linear models to kernel learning, and means for restoring the generalizability in both kernel Principal Component Analysis and the Support Vector Machine are proposed. Viability is proved on a wide range of benchmark machine learning data sets.

Published
2013

24. A Randomized Heuristic for Kernel Parameter Selection with Large-scale Multi-class Data

Author

Hansen, Toke Jansen, Abrahamsen, Trine Julie, Hansen, Lars Kai, Hansen, Toke Jansen, Abrahamsen, Trine Julie, and Hansen, Lars Kai

Abstract

Over the past few years kernel methods have gained a tremendous amount of attention as existing linear algorithms can easily be extended to account for highly non-linear data in a computationally efficient manner. Unfortunately most kernels require careful tuning of intrinsic parameters to correctly model the distribution of the underlying data. For large-scale problems the multiplicative scaling in time complexity imposed by introducing free parameters in a crossvalidation setup will prove computationally infeasible, often leaving pure ad-hoc estimates as the only option. In this contribution we investigate a novel randomized approach for kernel parameter selection in large-scale multi-class data. We fit a minimum enclosing ball to the class means in Reproducing Kernel Hilbert Spaces (RKHS), and use the radius as a quality measure of the space, defined by the kernel parameter. We apply the developed algorithm to a computer vision paradigm where the objective is to recognize 72:000 objects among 1:000 classes. Compared to other distance metrics in the RKHS we find that our randomized approach provides better results together with a highly competitive time complexity.

Published
2011

25. Regularized Pre-image Estimation for Kernel PCA De-noising:Input Space Regularization and Sparse Reconstruction

Author

Abrahamsen, Trine Julie, Hansen, Lars Kai, Abrahamsen, Trine Julie, and Hansen, Lars Kai

Abstract

The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed. As a consequence the most widely used estimation schemes lack stability. A common way to stabilize such estimates is by augmenting the cost function by a suitable constraint on the solution values. For de-noising applications we here propose Tikhonov input space distance regularization as a stabilizer for pre-image estimation, or sparse reconstruction by Lasso regularization in cases where the main objective is to improve the visual simplicity. We perform extensive experiments on the USPS digit modeling problem to evaluate the stability of three widely used pre-image estimators. We show that the previous methods lack stability in the is non-linear regime, however, by applying our proposed input space distance regularizer the estimates are stabilized with a limited sacrifice in terms of de-noising efficiency. Furthermore, we show how sparse reconstruction can lead to improved visual quality of the estimated pre-image.

Published
2011

26. A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

Author

Abrahamsen, Trine Julie, Hansen, Lars Kai, Abrahamsen, Trine Julie, and Hansen, Lars Kai

Abstract

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA. As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen.

Published
2011

27. Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising

Author

Abrahamsen, Trine Julie, Hansen, Lars Kai, Abrahamsen, Trine Julie, and Hansen, Lars Kai

Abstract

Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de-noising applications we propose input space distance regularization as a stabilizer for pre-image estimation. We perform extensive experiments on the USPS digit modeling problem to evaluate the stability of three widely used pre-image estimators. We show that the previous methods lack stability when the feature mapping is non-linear, however, by applying a simple input space distance regularizer we can reduce variability with very limited sacrifice in terms of de-noising efficiency.

Published
2009

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