다음과 같이 초청특강을 실시하오니 많은 참여바랍니다.

1. 일시 : 2023년 4월 21일 (금), 오후 2시 -

2. 장소 : 자연대연구실험동 222호

3. 연사 : 송성호 교수 (Division of Statistics and Data Science, Univ. of Cincinnati)

4. 연제 : Univariate and Multivariate Joint Models with Flexible Covariance Structures

            for Dynamic Prediction of Longitudinal and Time-to-event Data

Abstract

  Joint modeling of noisily measured biomarkers alongside time-to-event outcomes, such as those used for evaluating disease progression over time and dynamic prediction of survival, has revolutionized statistical science. It is also becoming increasingly clear with the advent of non-stationary Gaussian processes applied to medical monitoring studies that typical random effects within a longitudinal sub-model do not properly reflect complex fluctuations in biological processes. In this body of work, we propose a novel, flexible five-component longitudinal sub-model for univariate and multivariate joint modeling frameworks. The most noteworthy development is our introduction in these modeling frameworks of scaled integrated fractional Brownian motion (IFBM), a more generalized version of the integrated Brownian motion stochastic process that has been demonstrated to accurately represent biological processes observed with uncertainty. As the event sub-model, the Cox proportional hazards model is used, which comprises a time-dependent true longitudinal trajectory and a set of baseline variables. Using data from national patient registries, we assess lung function trajectories and mortality in two distinct rare lung diseases-lymphangioleiomyomatosis and cystic fibrosis.

We investigate clinically important target functions with the goal of predicting rapid lung function decline in each disease, including environmental exposures and community characteristics in the application of cystic fibrosis. Univariate joint modeling enables dynamic risk prediction for an event of interest while accounting for measurement error in a single marker. On the other hand, cystic fibrosis is a multisystem condition. As a result, we present a multivariate joint model that employs a flexible covariance structure to effectively capture the highly variable nature of these longitudinal processes while modeling lung function, and growth simultaneously. Our suggested model is assessed using simulations and is applied to the US Cystic Fibrosis Foundation?Patient Registry (CFF-PR) data set.