A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
and Wei, Guanghui , title = "
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7roles
method 1polarities
use method 1representative citing papers
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
A one-step outcome imputation estimator is introduced as an alternative to multiple imputation for RCTs with missing data, constructing an efficient estimator via the influence function to achieve asymptotically valid inference.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
citing papers explorer
-
Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories
A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
-
Improved prediction of extreme random effects in joint models: WRaPs
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
-
One-step Outcome Imputation: An Alternative to Multiple Imputation
A one-step outcome imputation estimator is introduced as an alternative to multiple imputation for RCTs with missing data, constructing an efficient estimator via the influence function to achieve asymptotically valid inference.
-
Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
-
Principal Covariate Regression with Nuclear Norm Penalty
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
-
Estimating treatment duration effects via clone-censor-weight: a breast cancer case study
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
-
Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.