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Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records

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BioData Mining 2025年第1期18卷 1-27页

作者： Hunsdieck, Berit Bender, Christian Ickstadt, Katja Mielke, Johanna Computational Biology Bayer AG Wuppertal Germany Department of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

Background: Over the past decade an increase in usage of electronic health data (EHR) by office-based physicians and hospitals has been reported. However, these data types come with challenge regarding completeness and data quality and it is, especially for more complex models, unclear how these characteristics influence the performance. Methods: In this paper, we focus on joint models which combines longitudinal modelling with survival modelling to incorporate all available information. The aim of this paper is to establish simulation-based guidelines for the necessary quality of longitudinal EHR data so that joint models perform better than cox models. We conducted an extensive simulation study by systematically and transparently varying different characteristics of data quality, e.g., measurement frequency, noise, and heterogeneity between patients. We apply the joint models and evaluate their performance relative to traditional Cox survival modelling techniques. Results: Key findings suggest that biomarker changes before disease onset must be consistent within similar patient groups. With increasing noise and a higher measurement density, the joint model surpasses the traditional Cox regression model in terms of model performance. We illustrate the usefulness and limitations of the guidelines with two real-world examples, namely the influence of serum bilirubin on primary biliary liver cirrhosis and the influence of the estimated glomerular filtration rate on chronic kidney disease. © The Author(s) 2025.

关键词： Chronic kidney disease Joint modelling Longitudinal data application Primary care data Simulation study

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Active multiple testing with proxy p-values and e-values

arXiv

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arXiv 2025年

作者： Xu, Ziyu Wang, Catherine Wasserman, Larry Roeder, Kathryn Ramdas, Aaditya Department of Statistics and Data Science United States Machine Learning Department Germany Computational Biology Department Carnegie Mellon University United States

Researchers often lack the resources to test every hypothesis of interest directly or compute test statistics comprehensively, but often possess auxiliary data from which we can compute an estimate of the experimental outcome. We introduce a novel approach for selecting which hypotheses to query a statistic (i.e., run an experiment, perform expensive computation, etc.) in a hypothesis testing setup by leveraging estimates (e.g., from experts, machine learning models, previous experiments, etc.) to compute proxy statistics. Our framework allows a scientist to propose a a proxy statistic, and then query the true statistic with some probability based on the value of the proxy. We make no assumptions about how the proxy is derived and it can be arbitrarily dependent with the true statistic. If the true statistic is not queried, the proxy is used in its place. We characterize "active" methods that produce valid p-values and e-values in this setting and utilize this framework in the multiple testing setting to create procedures with false discovery rate (FDR) control. Through simulations and real data analysis of causal effects in scCRISPR screen experiments, we empirically demonstrate that our proxy framework has both high power and low resource usage when our proxies are accurate estimates of the respective true statistics. Copyright © 2025, The Authors. All rights reserved.

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Tensor parametric Hamiltonian operator inference

arXiv

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arXiv 2025年

作者： Vijaywargiya, Arjun McQuarrie, Shane A. Gruber, Anthony Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Computational Mathematics Center for Computing Research Sandia National Laboratories Scientific Machine Learning Center for Computing Research Sandia National Laboratories

This work presents a tensor-based approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian structure. By expressing parameter-varying operators with affine dependence as contractions of a generalized parameter vector against a constant tensor, this method leverages the operator inference framework to capture parametric dependence in the learned reduced-order model via the solution to a convex, least-squares optimization problem. This leads to a concise and straightforward implementation which compactifies previous parametric operator inference approaches and directly extends to learning parametric operators with symmetry constraints—a key feature required for constructing structure-preserving surrogates of Hamiltonian systems. The proposed approach is demonstrated on both a (non-Hamiltonian) heat equation with variable diffusion coefficient as well as a Hamiltonian wave equation with variable wave *** Codes 15A69, 35B30, 65P10 © 2025, CC BY.

关键词： Tensors

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The optical and infrared are connected

arXiv

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arXiv 2025年

作者： Jespersen, Christian Kragh Melchior, Peter Spergel, David N. Goulding, Andy D. Hahn, ChangHoon Iyer, Kartheik G. Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Center for Statistics and Machine Learning Princeton University PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia University Columbia Astrophysics Lab 550 W 120th St New YorkNY10010 United States

Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of χ2N ≈ 1 for all photometric bands in WISE, as well as good colors. We are also able to tightly constrain typically IR-derived properties, e.g. the bolometric luminosities of AGN and dust parameters such as qPAH. We find that current SED-fitting methods are incapable of making comparable predictions, and that model misspecification often leads to correlated biases in star-formation rates and AGN luminosities. To help improve SED models, we determine what features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and Hα), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled. © 2025, CC BY.

关键词： Stars

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Bayesian conditional cointegration

Bayesian conditional cointegration

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29th International Conference on machine learning, ICML 2012

作者： Bracegirdle, Chris Barber, David Centre for Computational Statistics and Machine Learning University College London Gower Street London United Kingdom

ISBN: (纸本)9781450312851

Cointegration is an important topic for time-series, and describes a relationship between two series in which a linear combination is stationary. Classically, the test for cointegration is based on a two stage process in which first the linear relation between the series is estimated by Ordinary Least Squares. Subsequently a unit root test is performed on the residuals. A well-known deficiency of this classical approach is that it can lead to erroneous conclusions about the presence of cointegration. As an alternative, we present a framework for estimating whether cointegration exists using Bayesian inference which is empirically superior to the classical approach. Finally, we apply our technique to model segmented cointegration in which cointegration may exist only for limited time. In contrast to previous approaches our model makes no restriction on the number of possible cointegration segments. Copyright 2012 by the author(s)/owner(s).

关键词： Bayesian networks

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Improved loss bounds for multiple kernel learning

Improved loss bounds for multiple kernel learning

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14th International Conference on Artificial Intelligence and statistics, AISTATS 2011

作者： Hussain, Zakria Shawe-Taylor, John Centre for Computational Statistics and Machine Learning Department of Computer Science University College London United Kingdom

We propose two new generalization error bounds for multiple kernel learning (MKL). First, using the bound of Srebro and Ben-David (2006) as a starting point, we derive a new version which uses a simple counting argument for the choice of kernels in order to generate a tighter bound when 1-norm regularization (sparsity) is imposed in the kernel learning problem. The second bound is a Rademacher complexity bound which is additive in the (logarithmic) kernel complexity and margin term. This dependence is superior to all previously published Rademacher bounds for learning a convex combination of kernels, including the recent bound of Cortes et al. (2010), which exhibits a multiplicative interaction. We illustrate the tightness of our bounds with simulations. Copyright 2011 by the authors.

关键词： Artificial intelligence

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Observational-interventional priors for dose-response learning 30

Observational-interventional priors for dose-response learni...

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30th Annual Conference on Neural Information Processing Systems, NIPS 2016

作者： Silva, Ricardo Department of Statistical Science Centre for Computational Statistics and Machine Learning University College London United Kingdom

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the dose-response curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Affine transforms

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Matching pursuit kernel fisher discriminant analysis

Matching pursuit kernel fisher discriminant analysis

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12th International Conference on Artificial Intelligence and statistics, AISTATS 2009

作者： Diethe, Tom Hussain, Zakria Hardoon, David R. Shawe-Taylor, John Department of Computer Science Centre for Computational Statistics and Machine Learning University College London WC1E 6BT United Kingdom

We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant Analysis (MPKFDA). We provide generalisation error bounds analogous to those constructed for the Robust Minimax algorithm together with a sample compression bounding technique. We present experimental results on real world datasets, which show that MPKFDA is competitive with the KFDA and the SVM on UCI datasets, and additional experiments that show that the MPKFDA on average outperforms KFDA and SVM in extremely high dimensional settings. © 2009 by the authors.

关键词： Discriminant analysis

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PAC-Bayes analysis of maximum entropy learning

PAC-Bayes analysis of maximum entropy learning

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12th International Conference on Artificial Intelligence and statistics, AISTATS 2009

作者： Shawe-Taylor, John Hardoon, David R. Department of Computer Science Centre for Computational Statistics and Machine Learning University College London WC1E 6BT United Kingdom

We extend and apply the PAC-Bayes theorem to the analysis of maximum entropy learning by considering maximum entropy classification. The theory introduces a multiple sampling technique that controls an effective margin of the bound. We further develop a dual implementation of the convex optimisation that optimises the bound. This algorithm is tested on some simple datasets and the value of the bound compared with the test error.© 2009 by the authors.

关键词： Convex optimization

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Bilevel optimization with a lower-level contraction: optimal sample complexity without warm-start

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 7995-8031页

作者： Riccardo Grazzi Massimiliano Pontil Saverio Salzo Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy and University College of London UK Universitá la Sapienza di Roma Italy and Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy

We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map. This type of problems include instances of meta-learning, equilibrium models, hyperparameter optimization and data poisoning adversarial attacks. Several recent works have proposed algorithms which warm-start the lower-level problem, i.e. they use the previous lower-level approximate solution as a staring point for the lower-level solver. This warm-start procedure allows one to improve the sample complexity in both the stochastic and deterministic settings, achieving in some cases the order-wise optimal sample complexity. However, there are situations, e.g., meta learning and equilibrium models, in which the warm-start procedure is not well-suited or ineffective. In this work we show that without warm-start, it is still possible to achieve order-wise (near) optimal sample complexity. In particular, we propose a simple method which uses (stochastic) fixed point iterations at the lower-level and projected inexact gradient descent at the upper-level, that reaches an ε-stationary point using O(ε-2) and Õ(ε-1) samples for the stochastic and the deterministic setting, respectively. Finally, compared to methods using warm-start, our approach yields a simpler analysis that does not need to study the coupled interactions between the upper-level and lower-level iterates.

关键词： bilevel optimization warm-start non-convex optimization implicit differentiation hypergradient sample complexity

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