Publications

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Optimizing Dynamic Predictions from Joint Models using Super Learning

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Sample size calculation for clinical trials analyzed with the meta-analytic-predictive approach

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Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model

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Shared decision making of burdensome surveillance tests using personalized schedules and their burden and benefit

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Incorporating historical controls in clinical trials with longitudinal outcomes using the modified power prior

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JointAI: Joint analysis and imputation of incomplete data in R

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Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model

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An alternative characterization of Missing at Random in Shared Parameter Models for incomplete longitudinal data and its utilization for sensitivity analysis

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A Bayesian joint model for zero-inflated integers and left-truncated event times with a time-varying association: Applications to senior health care

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A marginal estimate for the overall treatment effect on a survival outcome within the joint modeling framework

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Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes

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Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

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Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach

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Personalized decision making for biopsies in prostate cancer active surveillance programs

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Bayesian imputation of time-varying covariates in linear mixed models

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Joint models for longitudinal and time-to-event data in a case-cohort design

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Personalized schedules for surveillance of low risk prostate cancer patients

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Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines

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Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature

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Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking

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Extension of the association structure in joint models to include weighted cumulative effects

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Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures

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The R package JMbayes for fitting joint Models for longitudinal and time-to-event data using MCMC

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Dealing with missing covariates in epidemiologic studies: A comparison between multiple imputation and a full Bayesian approach

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Personalized screening intervals for biomarkers using joint models for longitudinal and survival data

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Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging

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