Aug 2026· Bioinformatics Advances· Vol 6· 0 citations· 18 references
Medicine
TL;DR
The survkl software is introduced, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis.
Abstract
Abstract Summary Survival risk prediction often suffers from challenges such as rare event rates, small effective sample sizes, high-dimensional feature spaces, weak signals, population heterogeneity, and concerns over patient privacy. To overcome these obstacles and improve the precision of prognostic modeling, we introduce the survkl software, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis. Our method adaptively adjusts the weight given to external information, down-weighting heterogeneous information and highlighting more informative ones. The proposed tool accommodates both low-dimensional and high-dimensional data, offering unpenalized estimation and computationally efficient lasso, ridge, and elastic net penalties. The survkl software also provides auxiliary evaluation and plotting functions for model assessment. Availability and implementation survkl is freely available to the public at https://github.com/UM-KevinHe/survkl and published under General Public License version 3 license.
Survival analysis is a fundamental technique in biomedical research for modeling time-to-event data. It enables the identification of prognostic factors in disease, compares survival outcomes across treatment groups, and performs targeted treatment selection. A variety of machine learning (ML) approaches to survival analysis have emerged to complement classical statistical methods, especially for high-dimensional datasets with complex, nonlinear interactions between features. However, using survival ML methods requires addressing challenges such as censoring-unaware evaluation, overfitting, selecting performance metrics, and data leakage. To address these and other difficulties in using survival ML models, we developed the mlsurv software package. mlsurv is an open-source Python package built around three major design principles: 1) methodological rigor, including evidence-based model selection, leakage-free pipelines, and multi-metric evaluation, 2) multi-scale evaluation and interpretation, including population and subpopulation evaluation, patient-level explanations, and feature analysis, and 3) automated trust and transparency, including limitation flagging and TRIPOD+AI-aligned reporting. mlsurv bundles ten models spanning linear, ensemble, kernel, and deep learning families within a unified software package. We demonstrate mlsurv on the Chowell immunotherapy cohort (n=1,479). The survival-trained models achieve a test concordance index of 0.73 for overall survival prediction. Further, risk scores strongly correlate with the response-trained LORIS clinical score (|{rho}| up to 0.84), reflecting the overlap between prognostic and predictive signal. mlsurv enables biomedical researchers to conduct rigorous, multi-model survival analysis and benchmarking using minimal code with default best practices rather than implementing custom scripts and methodological safeguards from scratch.
A. Pybus, J. Qiu, P. C. Morais Lyra et al.· medRxiv· 0 citations
High-dimensional data and multicollinearity present major challenges in regression analysis, often causing overfitting and unstable coefficient estimates. The Elastic-net model, which combines (Lasso) and (Ridge) regularization, offers a robust solution by enhancing feature selection and handling multicollinearity. This study improves Elastic-net by integrating two optimization techniques: Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam). The SGD Elastic-net model accelerates convergence and boosts computational efficiency through mini-batch updates, while the Adam Elastic-net model incorporates adaptive learning rates and momentum to enhance stability and performance, especially with noisy or sparse data. Simulated data analysis showed that both Adam and SGD Elastic-net models produced more precise coefficient estimates with narrower confidence intervals, improving interpretability and robustness. A real-world application on a prostate cancer dataset further confirmed these advantages. Diagnostic plots validated the assumptions of linearity, normality, and homoscedasticity, supporting model reliability. Overall, the Adam and SGD Elastic-net approaches achieved faster convergence and lower residual errors, making them highly effective for high-dimensional datasets facing multicollinearity.
Biological research and clinical evidence suggest that treatment response may vary substantially along characteristics, such as comorbidities, genetic variants, environmental, or socio-economic factors. Future precision medicine requires accurate assessment of heterogeneous treatment effects (HTE) to guide optimal clinical decisions at the individual level. We introduce a functional score framework that extends the traditional estimating equations for survival data to nonparametric HTE and generalize the Neyman orthogonality accordingly, thus filling a methodological as well as theoretical gap. Under the Neyman orthogonal functional score framework, we developed the martingale R-learner based on a decomposition of the conditional martingale residuals into residuals of the risk-set propensity score and the marginal martingale, thereby reducing the impact of estimation bias in HTE from nuisance models including (1) marginal survival, and (2) risk-set propensity scores. This enables leveraging advances in machine learning and incorporates flexible estimators for the nuisance functions and attaining the standard optimal nonparametric estimation rate with the oracle property. Numerical experiments demonstrated empirical performance consistent with the theory. We applied the martingale R-learner to estimate the effect of alcohol on dementia using the Honolulu-Asia Aging Study data.
Prognostic factor evaluation and prediction model development are central to precision oncology, enabling patient risk stratification and individualized treatment selection. Unified predictions that synthesize information from existing models are valuable for comprehensive and consistent risk assessment. Many studies also seek to evaluate the incremental value of new biomarkers beyond established prognostic factors. However, such efforts are often constrained by small-to-moderate sample sizes. Motivated by these challenges, we consider Cox regression analysis in settings where individualized risk predictions from existing models are externally available without a transparent or interpretable structure, for example, through online calculators. We develop a Bayesian discretized survival time inference framework in which individualized predictions from potentially multiple external sources are integrated through a formulation based on Kullback-Leibler divergence, yielding informative priors. The divergence-based formulation serves as a surrogate for the external information likelihood, enabling principled incorporation of individualized predictions without requiring knowledge of the underlying external prediction models. Theoretical results show that the resulting posterior mean estimators are asymptotically more efficient than their internal-only maximum likelihood counterparts. However, using the divergence-based surrogate in place of the unavailable external likelihood renders posterior variance-based inference conservative. We propose a correction to address this overcoverage. We demonstrate the performance of the proposed approach through simulations and an application to prostate cancer trial data.
Yena Jeon, Yunxiang Huang, H. Kim et al.· 0 citations
As data accumulation continues to expand and information technologies evolve, machine learning methods have become widely adopted, making the effectiveness of learning algorithms crucial. Among the most popular machine learning models is Lasso regression, renowned for its feature selection capabilities and ability to address multicollinearity. This paper introduces novel algorithms for estimating Lasso regression parameters by reformulating the problem as an inverse single-point optimization task. Two algorithms are proposed: Lasso-I, which implements coordinate descent with L1 regularization, and Lasso-H, a hybrid approach that combines Lasso-I with wrapper techniques for feature selection using information criteria. The iterative algorithms involve calculating partial derivatives and selecting arguments for adjustment based on residual sum of squares or information criteria. Algorithm evaluation was performed using linear and logistic regression models across diverse datasets from KEEL and UCI repositories, alongside various metrics including the AIC, MSE, and
R
2
. The experimental results demonstrate that the algorithms effectively address parameter estimation problems, with Lasso-H achieving optimal AIC values in 90% of logistic regression cases. The proposed methods eliminate the need for explicit regularization parameter specification while maintaining robust feature selection capabilities and effective multicollinearity mitigation, demonstrating high accuracy and reliability across high-dimensional datasets.
Neural survival models are increasingly asked to support counterfactual claims---how much a treatment would change survival in a population---rather than only prognostic risk scores. Answering such questions from observational data requires valid inference for treatment-specific survival contrasts under confounding and covariate-dependent censoring, targets for which standard deep survival estimators are biased and provide no honest uncertainty. We propose Targeted Deep Survival Contrasts (TDSC), which extends Targeted Deep Architectures (TDA)---targeted maximum likelihood estimation embedded in a network's weight space---to the full vector of treatment-specific survival curves over a time grid, and hence to the benefit curve and the restricted mean survival time (RMST) difference. A single universal targeting path, one ridge projection of the stacked efficient influence functions onto closed-form last-layer gradients per iteration, simultaneously solves the projected estimating equations for all coordinates; a one-step residual top-up converts the plug-in into a doubly robust estimator of the unrestricted target; and a multiplier bootstrap yields simultaneous confidence bands for the benefit curve. We prove joint asymptotic linearity, band validity, and double robustness of the top-up for a cross-fitted variant requiring no Donsker conditions. Across seven Monte Carlo banks with confounded treatment, sign-varying effect heterogeneity, and dependent censoring, the TDSC plug-in attains nominal pointwise and simultaneous coverage with 35% lower MSE than a per-timepoint one-step (AIPCW) built from the same nuisance fits. Under a badly wrong outcome model the plug-in tracks its working parameter and its intervals fail (44% coverage), while the top-up restores nominal inference for the unrestricted causal target (94-95%)---and in-sample diagnostics separate the two regimes.
David McCoy, Yi Li· 0 citations
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