Program
Speaker 1: Mengjiao Peng (East China Normal University, China)

Title: Joint mean variance screening for ultrahigh dimensional categorical predictors
with multiple survival outcomes
Abstract: Medical research often involves collecting ultrahigh-dimensional gene features, where thenumber of gene features is much larger than the sample size, creating challenges in survival
prediction and disease mechanism research in precision medicine. The focus of this article is to
extract important categorical covariate features from vast amount of genomic data for further
survival outcome prediction. The complexity of the problem is heightened when studying two typesof survival endpoints, one being possibly censored by the other, known as semi-competing risks
outcomes. We propose the Joint Mean Variance Index (JMV) as a measure of the correlation between
outcomes and a categorical covariate, aiming in identifying important gene features in ultrahighdimensional multiple survival outcomes data. The proposed screening procedure is model-free,robust to model misspecification, heavy tails, and outliers. Additionally, we show that the screening
procedure exhibits sure independence and ranking consistency. Extensive numerical simulations areconducted to confirm the theoretical properties.
Speaker 2: Zili Zhang (Huaqiao University, China)

Other authors: Christiana Charalambous, Peter Foster
Title: A Gaussian copula joint model for longitudinal and time-to-event data with random effects
Abstract: Longitudinal and survival sub-models are two building blocks for joint modelling of
longitudinal and time-to-event data. Extensive research indicates separate analysis of these twoprocesses could result in biased outputs due to their associations. Conditional independence between
measurements of biomarkers and event time process given latent classes or random effects is aconventional approach for characterising the association between the two sub-models while taking
the heterogeneity among the population into account. However, this assumption is difficult tovalidate because of the unobservable latent variables. Thus, a Gaussian copula joint model withrandom effects is proposed to accommodate the scenarios where the conditional independence
assumption is questionable. The conventional joint model assuming conditional independence is a
special case of the proposed model when the association parameters in the Gaussian copula shrink tozero. Simulation studies and real data application are carried out to evaluate the performance of the
proposed model with different correlation structures. In addition, personalised dynamic predictions

of survival probabilities are obtained based on the proposed model and comparisons are made to thepredictions obtained under the conventional joint model.
Speaker 3: Takeshi Emura (the ISM, Japan)

Other authors: Marc Ditzhaus, Dennis Dobler, Kenta Murotani
Title: Factorial survival analysis for treatment effects under dependent censoring
Abstract: Factorial analyses offer a powerful nonparametric means to detect main or interaction
effects among multiple treatments. For survival outcomes, e.g. from clinical trials, such techniques
can be adopted for comparing reasonable quantifications of treatment effects. The key difficulty tosolve in survival analysis concerns the proper handling of censoring. So far, all existing factorial
analyses for survival data were developed under the independent censoring assumption, which is toostrong for many applications. As a solution, the central aim of this article is to develop new methods
in factorial survival analyses under quite general dependent censoring regimes. This will beaccomplished by combining existing results for factorial survival analyses with techniques developed
for survival copula models. As a result, we will present an appealing F-test that exhibits soundperformance in our simulation study. The new methods are illustrated in real data analysis. We
implement the proposed method in an R function surv.factorial(.) in the R package compound.Cox.
Speaker 4: Dongdong Li (Harvard Pilgrim Health Care Institute, USA)

Title: Proportional hazards regression models for interval-censored outcome with interval-censoredcovariates
Abstract: Identifying predictors for features of viral rebound trajectories after antiretroviral therapy(ART) interruption is central to HIV cure research. Motivated by the need to assess whether the time
to viral suppression after ART initiation is predictive of the time to viral rebound after ART
interruption, we investigate modeling approaches that relate an interval-censored outcome (e.g., timeto viral rebound) and an interval-censored covariate (e.g., time to viral suppression). We develop
estimation and inference procedures for fitting a proportional hazards regression model when boththe outcome and a covariate can be interval-censored, without making parametric distributional
assumptions about baseline hazard functions, through the use of an Expectation-Maximization
algorithm. As some participants experienced multiple episodes of ART initiation and interruption, we
further extend the proposed method to accommodate the clustering effect of multiple observations
from the same participant. We evaluate the finite-sample performance of the proposed method forboth independent and clustered data settings through simulation studies. To illustrate, we assess theeffect of time to viral suppression after ART initiation on time to viral rebound after ART interruption
using data from the Zurich Primary HIV Infection cohort.
Speaker 5: IL DO HA (Dept. of Statistics, Pukyong National University, South Korea)

Other authors: Hangbin Lee (Seoul National University), Youngjo Lee (Seoul National University)
Title: Deep Neural Network for Semi-parametric Frailty Models via H-likelihood
Abstract: Recently, deep neural network (DNN) has provided a major breakthrough to enhance
prediction in various areas. For prediction of clustered time-to-event data, we propose a new DNNbased frailty model (DNN-FM). An advantage of the proposed model is that the joint maximizationof the new h-likelihood provides maximum likelihood estimators for fixed parameters and best
unbiased predictors for frailties. Thus, the proposed DNN-FM is trained by using a negative profiled
h-likelihood, constructed by profiling out the non-parametric baseline hazard, as a loss function.
Numerical studies show that the proposed method improves the prediction performance of theexisting methods (e.g. DNN-Cox model) in terms of Brier score and C-index. A real data analysis
shows that the inclusion of subject-specific frailties to the DNN-Cox model helps to improve the risk
prediction of the DNN-Cox model. We also present an online learning framework of the proposed
DNN model for large data set.
Keywords: Deep neural network, Frailty models, H-likelihood, Random effect.
Speaker 6: Xinyuan Song (The Chinese University of Hong Kong, HK)

Title: A tree-based Bayesian accelerated failure time cure model for estimating heterogeneous
treatment effect
Abstract: Estimating heterogeneous treatment effects has drawn increasing attention in medical
studies, considering that patients with divergent features can undergo a different disease progression
even with identical treatment. Such heterogeneity can co-occur with a cured fraction for biomedical
studies with a time-to-event outcome and further complicates the quantification of treatment effects.
This study considers a joint framework of Bayesian causal forest and accelerated failure time curemodel to capture the cured proportion and treatment effect heterogeneity through three separate
Bayesian additive regression trees. Under the potential outcomes framework, conditional and sample
average treatment effects within the uncured subgroup are derived on the scale of log survival time
subject to right-censoring, and treatment effects on the scale of survival probability are derived foreach individual. Bayesian backfitting Markov chain Monte Carlo algorithm with the Gibbs sampler
is conducted to estimate the causal effects. Simulation studies show the satisfactory performance ofthe proposed method. The proposed model is then applied to a breast cancer dataset extracted fromthe SEER database to demonstrate its usage in detecting heterogeneous treatment effects and cured

subgroups.
Speaker 7: Liming Xiang (Nanyang Technological University, Singapore)

Title: Multiple imputation for competing risks analysis with covariates subject to detection limits
Other authors: Yilin Wu, Rui Huang, Huixia Judy Wang.
Abstract: Competing risks occur commonly in survival analysis when subjects may experience
multiple types of events and the occurrence of the primary event of interest can be precluded by acompeting event. Challenges arise for the analysis of competing risks data with covariates subject tocensoring due to detection limits. We propose a semiparametric multiple imputation method for
inference under the subdistribution hazard model. Our proposal automatically utilizes theinformation from the exactly observed covariate values and the outcome data to efficiently imputethe censored covariates based on rejection sampling. We establish consistency and asymptotic
normality of the resulting estimator and demonstrate its promising finite sample performance via
extensive simulations. Finally, we illustrate the application of the method with the data from a studyof community acquired pneumonia.
Speaker 8: Feng-Chang Lin (University of North Carolina at Chapel Hill, USA)

Title: A more efficient estimator for competing risks data with missing cause of failure
Abstract: When a competing risk observation misses the cause of failure, one common approach,
other than a complete-case analysis, is to utilize the cause-specific hazards to impute the missing
indicator with a probability of being the type. However, in addition to the baseline markers used ascovariates for the cause-specific hazards, the same markers are likely collected when the event
happens. Unfortunately, such information is usually observed but seldom used. In this presentation,
we will demonstrate how we can utilize the transition information of markers to provide a moreaccurate probability estimation of the missing indicator and, therefore, provide a more efficientestimator for the parameter estimation. To demonstrate our method’s feasibility, we will apply thenewly developed approach to two P. vivax malaria recurrent infection data and Pseudomonas
aeruginosa (Pa) infections in young cystic fibrosis (CF) patients.