kaplan-meier estimation

Poor interpretability of survival data impedes clinical decision-making and patient communication. Method: A pretest–posttest crossover survey assessed 32 medical students and clinicians on comprehension accuracy, learning gains, and contextual suitability of Kaplan–Meier (KM) curves, mean residual life (MRL) plots, and their difference plots. Contribution/Results: This study provides the first systematic validation of MRL plots’ acceptability and educational potential in clinical settings. Overall interpretation accuracy significantly improved from 50.0% to 81.2% post-training (p = 0.002), with MRL plots yielding the highest learning gain (+37.5 percentage points). Although KM curves remain the preferred visualization for clinical use (59% preference), MRL plots demonstrated unique utility in patient communication (9% preference). Notably, individuals with low baseline knowledge achieved substantial comprehension improvements after brief training, underscoring MRL plots’ accessibility and pedagogical value for diverse clinical audiences.

This study systematically compares classical statistical (Cox proportional hazards, Weibull accelerated failure time), machine learning (random survival forests, DeepSurv), and Bayesian survival models—including a novel Bayesian frailty Weibull AFT model—in breast cancer prognosis prediction. Performance is evaluated using concordance index (C-index) and root mean squared error (RMSE). While DeepSurv achieves the highest predictive accuracy, it lacks interpretability. In contrast, random survival forests and the proposed Bayesian frailty Weibull AFT model attain comparable performance while offering parameter interpretability, principled uncertainty quantification, and clinical deployability. To our knowledge, this work presents the first implementation of a Bayesian frailty survival model within standard probabilistic programming libraries. It demonstrates that, with appropriate modeling, interpretable models can match the predictive performance of “black-box” methods—establishing a new paradigm for clinical decision support that jointly ensures accuracy, transparency, and reliability.

Continuous-time survival models yield biased estimates when applied to discretized or interval-censored event times—common in clinical and administrative data—and fail to properly account for competing risks. Method: We propose the first Python-based, regularized (LASSO/elastic net) semiparametric discrete-time competing risks regression framework, built upon a discrete-time Cox-type formulation. It integrates synthetic data generation, model fitting, and comprehensive evaluation modules, enabling robust high-dimensional modeling and variable selection. Contribution/Results: Evaluated on MIMIC-IV clinical data, the method accurately predicts length of hospital stay. Monte Carlo simulations confirm unbiased parameter estimation and significantly superior predictive performance over mainstream continuous-time approaches—including the Fine–Gray model. This work fills a critical gap in the Python ecosystem for discrete competing risks modeling, combining theoretical rigor with practical scalability and reproducibility.

Existing methods for analyzing recurrent event data often rely on unverifiable modeling assumptions and neglect the competing risks posed by informative terminal events (e.g., death, premature treatment discontinuation), leading to biased, non-robust, and poorly interpretable treatment effect estimates. To address this, we propose the Area Under the Mean Cumulative Function (AUMCF), the first nonparametric extension of restricted mean survival time to recurrent event settings, directly quantifying the overall treatment effect. We further develop a doubly robust covariate-adjusted augmented estimator that integrates inverse probability weighting with augmented inverse probability weighting (AIPW). The proposed methodology is implemented in the open-source R package *MCC*. Simulation studies and application to the BEST heart failure trial demonstrate AUMCF’s unbiasedness, stability, and clinical interpretability, substantially improving reliability, transparency, and competing-risk adjustment in multi-endpoint efficacy evaluation.

This paper addresses individualized survival time prediction for right-censored data. We propose the first conformal prediction-based method for constructing prediction intervals that provides rigorous finite-sample coverage guarantees under the sole assumption of independent and identically distributed (i.i.d.) observations. The method adaptively distinguishes individual similarity: it outputs two-sided intervals for highly similar individuals and degenerates to one-sided lower bounds for dissimilar ones. Coverage accuracy is achieved via similarity-weighted truncation and nonparametric calibration. Unlike Cox regression or random forest quantile methods, our approach imposes no distributional assumptions. Empirical evaluation on both synthetic and real-world datasets demonstrates that it consistently attains nominal coverage levels (e.g., ≥89.7% observed at the 90% target), substantially improving statistical reliability and practical utility.

This study addresses the inefficiency and bias introduced by right-censored covariates in survival analysis, which commonly undermine conventional approaches such as complete-case analysis. Within the Cox proportional hazards framework, the authors propose a novel method that incorporates a weighted averaging strategy into the partial likelihood function: for observations with censored covariates, the corresponding relative risk is replaced by a weighted average derived from fully observed cases. By directly leveraging the censoring information rather than discarding incomplete cases or imputing constant values, the approach mitigates estimation bias. Extensive simulations and analyses of two oncology clinical trials demonstrate that the proposed method substantially improves estimation efficiency and data utilization, outperforming existing strategies for handling censored covariates.

This work addresses the challenge of survival analysis with right-censored data by proposing a training-free, end-to-end approach that introduces tabular foundation models (TFMs) into survival analysis for the first time. The method integrates the accelerated failure time (AFT) model with the Buckley–James estimator to perform nonparametric, iterative imputation of censored times within context. Requiring only the estimation of a single scalar parameter and no task-specific training, it effectively handles right-censoring while maintaining computational simplicity. On standard benchmarks, the proposed approach matches or even surpasses the performance of trained Cox regression and parametric AFT models, substantially enhancing the practicality and competitiveness of zero-shot survival regression.

This study addresses the limitations of traditional joint models in clinical longitudinal studies, where repeatedly measured biomarkers or quality-of-life outcomes are often associated with event times but constrained by the proportional hazards assumption, hindering interpretability on the time scale. The authors propose a class of Bayesian semiparametric accelerated failure time joint models that integrate linear mixed-effects models for the longitudinal process and employ Bernstein polynomials to flexibly model the baseline hazard. A time-warping rescaling strategy is introduced to enhance numerical stability and parameter identifiability. By relaxing the proportional hazards assumption, the proposed approach offers more intuitive time-scale interpretations. Simulation studies demonstrate that, when event risk depends on underlying longitudinal trajectories, the method yields more accurate estimates of treatment effects compared to separate modeling approaches and exhibits excellent finite-sample performance.

This study addresses the modeling challenges posed by population heterogeneity in high-dimensional clinical data by systematically reviewing and categorizing methods that integrate patient covariate clustering with outcome modeling. It explicitly distinguishes, for the first time, between “informed clustering” (which leverages outcome information) and “agnostic clustering” (based solely on covariates). Through a comprehensive analysis of 55 studies—including PPMx models, finite mixture regression, cluster-aware supervised learning, and two-stage approaches—the work clarifies the respective strengths and appropriate use cases of each framework in risk stratification, subgroup treatment effect estimation, and rare disease research. The review provides a clear methodological guide and practical reference for integrated modeling of heterogeneous clinical data.

This study addresses the low power and poor stability of conventional Wald-type tests for interval-censored data in small-sample settings by proposing the first likelihood ratio test framework based on spline sieves. The method integrates spline sieve modeling with likelihood ratio testing and rigorously derives its asymptotic distribution, ensuring both theoretical validity and practical robustness. Simulation studies demonstrate that the proposed approach substantially improves statistical power while maintaining proper control of Type I error rates. Its applicability and advantages are further confirmed through analysis of real clinical data.