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1. Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces

   https://doi.org/10.1007/s10985-021-09539-z  

   Zhang, Song; Qu, Yang; Cheng, Yu; Lopez, Oscar L; Wahed, Abdus S (January 2022, Lifetime Data Analysis)

   Many medical conditions are marked by a sequence of events in association with continuous changes in biomarkers. Few works have evaluated the overall accuracy of a biomarker in predicting disease progression. We thus extend the concept of receiver operating characteristic (ROC) surface and the volume under the surface (VUS) from multi-category outcomes to ordinal competing-risk outcomes that are also subject to noninformative censoring. Two VUS estimators are considered. One is based on the definition of the ROC surface and obtained by integrating the estimated ROC surface. The other is an inverse probability weighted U estimator that is built upon the equivalence of the VUS to the concordance probability between the marker and sequential outcomes. Both estimators have nice asymptotic results that can be derived using counting process techniques and U-statistics theory.We illustrate their good practical performances through simulations and applications to two studies of cognition and a transplant dataset. 

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2. Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks

   Shen, Xiaobin; Elmer, Jonathan; Chen, George H (August 2023, Proceedings of the 8th Machine Learning for Healthcare Conference)

   Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (i.e., the task of neurological prognostication) could help with treatment decisions: which patients are likely to awaken from their coma and should be kept on life-sustaining therapies, and which are so ill that they would unlikely benefit from treatment? In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients’ available EEG time series could vary in length. Predictions themselves are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient’s probability of awakening or of dying across multiple time horizons (e.g., within the next 24, 48, or 72 hours). Our framework is based on using any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). For some patients, we do not know which of these happened first since they were still in a coma when data collection stopped (i.e., their outcome is censored). Competing risks models readily accommodate such patients. We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 post-cardiac-arrest coma patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient’s static features and summary statistics from the patient’s latest hour’s worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient’s EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks). 

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3. Simplicity and Probability Weighting in Choice under Risk

   https://doi.org/10.1257/pandp.20221091  

   Fudenberg, Drew; Puri, Indira (May 2022, AEA Papers and Proceedings)

   We present a speculative application of model estimates from Fudenberg and Puri (2021) to prize-linked savings in South Africa. The models used include one combining simplicity theory (Puri 2018, 2022), a preference for lotteries with fewer possible outcomes, with cumulative prospect theory. The results and those of prior literature indicate that both simplicity and probability weighting have a role to play in understanding behavior in choice under risk. We discuss the properties of these models and their implications for behavior. 

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4. Estimation of the Cumulative Incidence Function under multiple dependent and independent censoring mechanisms

   Lok J.J.; Yang S.; Sharkey B.; Hughes M.D. (April 2018, Lifetime data analysis)

   Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of two anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095. 

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5. Predicting the Pathway Involvement of Metabolites Based on Combined Metabolite and Pathway Features

   https://doi.org/10.3390/metabo14050266  

   Huckvale, Erik D; Moseley, Hunter_N B (May 2024, Metabolites)

   A major limitation of most metabolomics datasets is the sparsity of pathway annotations for detected metabolites. It is common for less than half of the identified metabolites in these datasets to have a known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a “pathway category”, as defined by a metabolic knowledge base like KEGG. Past models were implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating the predictions for multiple pathway categories. This past approach multiplied the computational resources necessary for training while diluting the positive entries in the gold standard datasets needed for training. To address these limitations, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts the features both representing a metabolite and representing a pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite–pathway features pair approach not only outperforms the combined performance of training separate binary classifiers but demonstrates an order of magnitude improvement in robustness: a Matthews correlation coefficient of 0.784 ± 0.013 versus 0.768 ± 0.154. 

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