Multistate models are widely used to characterize transitions between discrete states in progressive disease processes or biological systems. In many practical settings, exact transition times are not observed; instead, current status data, where each subject is observed only once at a random inspection time, are collected. This leads to a severely interval-censored (case I) multivariate survival problem. In this talk, I will present nonparametric methods for both marginal and conditional estimation of transition and occupation probabilities in multistate systems under current status observation. I will also discuss regression models for evaluating the effects of covariates on these temporal functions. Special attention will be given to estimation in the presence of cluster-correlated data and strategies for adjusting bias due to informative clustering. Applications will include analyses of periodontal disease progression in an understudied population and breast cancer progression in a European cohort.

Molecular mechanisms of complex diseases such as cancer often vary across patients, leading to differences in risk, progression, and treatment response. The central premise of precision medicine is to tailor preventative and therapeutic strategies to individual patients, by accounting for this heterogeneity in disease mechanisms. Recently, gene regulatory networks, which capture interactions among genes and their molecular regulators, have proven to be valuable tools for uncovering disease mechanisms, paving the way for network medicine. However, conventional approaches for biological network inference typically estimate population-level networks that average out individual heterogeneity. This limitation arises because individual-level data are scarce, and most statistical methods require reasonably sized samples to extract meaningful signals. In this talk, I will introduce an Empirical Bayes framework for estimating individual-specific networks that reflect person-specific disease biology. By integrating population-level prior information with individual-specific omics data, our framework recovers both shared and unique regulatory patterns across individuals. Our methods are highly scalable to large scale multi-omics datasets. I will describe two such methods: one for individual-specific co- expression networks, and another for individual-specific multi-omic Gaussian graphical models. Applications to simulated and human cancer datasets demonstrate that these methods not only recover accurate interactions between omics data types, but they also reveal patient-level network differences linked to clinical outcomes. This work highlights the potential for Empirical Bayes as a principled and practical strategy for individualized network inference, with broad implications for precision medicine.

In fish species abundance studies, a major challenge in deriving an absolute abundance estimate across a continental-scale spatial domain lies in the fact that regional survey teams possess and deploy different gear types, each with its unique field of view that produces gear-specific relative abundance observations. In a continental-scale study, the dataset from any regional survey must be converted from the gear-specific relative abundance scale to an absolute abundance scale, so that regional observations can be combined to estimate a continental scale absolute abundance. In this paper, we develop an operational conversion tool that takes regional gear-based data as input, and produces as output the required conversion, with associated uncertainty. Methodologically, the conversion tool is operationalized from a Bayesian hierarchical model which we develop in an inferential context that is akin to the change-of-support problem often encountered in large-scale spatial studies; the actual context here is to reconcile abundance data observed at various gear-specific scales, some being relative, and others, absolute. To this end, we consider data from a small-scale calibration experiment in which 2 to 4 different underwater video camera types were simultaneously deployed on each of 21 boat trips. Alongside each suite of deployed cameras was also an acoustic echosounder that recorded fish signals along a set of surrounding transects. While the echosounder records data on the absolute scale, it is subject to confounding from acoustically similar species, thus requiring an externally derived correction factor. Conversely, a camera allows visual distinction between species but records data on a relative scale specific to the camera type. Our statistical modeling framework reflects the relationship among all five gear types across the 21 boat trips, and the resulting model is used to derive calibration formulae that translate camera-specific relative abundance data to the corrected absolute abundance scale whenever a camera is deployed alone. Cross-validation is conducted using mark-recapture abundance estimates (only available for 10 trips, all observed at the same type of habitat). We also briefly discuss the case when one camera type is deployed alongside the echosounder.