Presents a neural network model with IPW pseudo-observations for dynamic prediction of alternating recurrent events, with simulation and medical resident mood application results.
Evaluating the yield of medical tests
5 Pith papers cite this work, alongside 3,442 external citations. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
FORSS is a new formula-based super-sample framework for power and sample size calculations with win statistics on hierarchical endpoints that incorporates marginal effects and a flexible joint distribution.
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.
The paper introduces neural networks by framing them as approximations to linear regression with common customizations for statisticians.
citing papers explorer
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Dynamic Prediction of Alternating Recurrent Events via Neural Network
Presents a neural network model with IPW pseudo-observations for dynamic prediction of alternating recurrent events, with simulation and medical resident mood application results.
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The FORSS Framework for Sample Size and Power Calculations With Win Statistics for Hierarchical Endpoints
FORSS is a new formula-based super-sample framework for power and sample size calculations with win statistics on hierarchical endpoints that incorporates marginal effects and a flexible joint distribution.
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A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
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fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R
fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.
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Neural Networks as Linear Regression: An Introduction for Statisticians
The paper introduces neural networks by framing them as approximations to linear regression with common customizations for statisticians.