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Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural…

Applications · Statistics 2018-01-12 Dan Li , Samuel Iddi , Wesley K. Thompson , Michael C. Donohue

Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually…

Applications · Statistics 2016-01-26 Cécile Proust-Lima , Jean-François Dartigues , Hélène Jacqmin-Gadda

A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the likelihood function of a statistical model is computationally…

Methodology · Statistics 2024-12-10 Giuseppe Alfonzetti , Ruggero Bellio , Yunxiao Chen , Irini Moustaki

The entropic risk measure is widely used in high-stakes decision-making across economics, management science, finance, and safety-critical control systems because it captures tail risks associated with uncertain losses. However, when data…

Optimization and Control · Mathematics 2026-01-05 Utsav Sadana , Erick Delage , Angelos Georghiou

Huntington disease (HD) is a neurodegenerative disease with progressively worsening symptoms. Accurately modeling time to HD diagnosis is essential for clinical trial design. Langbehn's model, the CAG-Age Product (CAP) model, the Prognostic…

Disease mapping focuses on learning about areal units presenting high relative risk. Disease mapping models for disease counts specify Poisson regressions in relative risks compared with the expected counts. These models typically…

Methodology · Statistics 2016-07-26 Feifei Wang , Jian Wang , Alan E. Gelfand , Fan Li

Stacking regressions is an ensemble technique that forms linear combinations of different regression estimators to enhance predictive accuracy. The conventional approach uses cross-validation data to generate predictions from the…

Machine Learning · Statistics 2024-10-10 Xin Chen , Jason M. Klusowski , Yan Shuo Tan

We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with multiple exogenous covariates and a potentially endogenous treatment variable. Our approach integrates a Robinson transformation to handle…

Econometrics · Economics 2024-10-04 Xiaolin Sun

Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups…

Methodology · Statistics 2022-10-06 Margaret Gamalo , Yoonji Kim , Fan Zhang , Junjing Lin

We consider survival data in the presence of a cure fraction, meaning that some subjects will never experience the event of interest. We assume a mixture cure model consisting of two sub-models: one for the probability of being uncured…

Methodology · Statistics 2023-03-17 Eni Musta , Tsz Pang Yuen

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for…

Applications · Statistics 2024-04-26 Aaron J. Molstad , Yanwei Cai , Alexander P. Reiner , Charles Kooperberg , Wei Sun , Li Hsu

An individualized treatment rule (ITR) tailors treatments to a patient's specific characteristics. However, randomized controlled trials (RCTs) are often underpowered to detect the treatment effect heterogeneity needed for reliable ITR…

Methodology · Statistics 2026-04-14 Yuan Bian , Donglin Zeng , Hyun-Joon Yang , Leanne M. Williams , Yuanjia Wang

A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is…

Methodology · Statistics 2020-10-15 Olivier Bouaziz , Eva Lauridsen , Grégory Nuel

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost…

Optimization and Control · Mathematics 2014-10-17 Stefan Streif , Matthias Karl , Ali Mesbah

In this paper, we introduce some new notions of aging based on geometric, harmonic means of failure rate and aging intensity function. We define a generalized version of aging functions called specific interval-average geometric hazard…

Statistics Theory · Mathematics 2024-07-02 Subarna Bhattacharjee , Ananda Sen , Sabana Anwar , Aninda Kumar Nanda

In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart…

Quantitative Methods · Quantitative Biology 2019-10-09 Shihao Jin , Nicolò Savioli , Antonio de Marvao , Timothy JW Dawes , Axel Gandy , Daniel Rueckert , Declan P O'Regan

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte…

Computational Physics · Physics 2016-08-24 Daniel A. Charlebois , Jukka Intosalmi , Dawn Fraser , Mads Kaern

Current diagnosis and prognosis for Parkinson's disease (PD) face formidable challenges due to the heterogeneous nature of the disease course, including that (i) the impairment severity varies hugely between patients, (ii) whether a symptom…

Applications · Statistics 2024-10-03 Kai Kang , Yuqi Gu

Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastructure and diagnostic systems. Conventional diagnostic approaches often fall short in accurately…

Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a better understanding of the generalization properties of this…

Machine Learning · Computer Science 2020-03-16 Marco Loog , Tom Viering , Alexander Mey