Wentao (Winston) Li

Our primary objective is to provide the clinical informatics community with an introductory tutorial on calibration measurements and calibration models for predictive models using existing R packages and custom implemented code in R on real and simulated data. Clinical predictive model performance is commonly published based on discrimination measures, but use of models for individualized predictions requires adequate model calibration. This tutorial is intended for clinical researchers who want to evaluate predictive models in terms of their applicability to a particular population. It is also for informaticians and for software engineers who want to understand the role that calibration plays in the evaluation of a clinical predictive model, and to provide them with a solid starting point to consider incorporating calibration evaluation and calibration models in their work.Covered topics include (1) an introduction to the importance of calibration in the clinical setting, (2) an illustration of the distinct roles that discrimination and calibration play in the assessment of clinical predictive models, (3) a tutorial and demonstration of selected calibration measurements, (4) a tutorial and demonstration of selected calibration models, and (5) a brief discussion of limitations of these methods and practical suggestions on how to use them in practice.

This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package (‘lme4’) as the baseline model.

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to correct for population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of dMEGA are demonstrated through simulated and real datasets. dMEGA is publicly available at https://github.com/Li-Wentao/dMEGA.

Federated learning of data from multiple participating parties is getting more attention and has many healthcare applications. We have previously developed VERTIGO, a distributed logistic regression model for vertically partitioned data. The model takes advantage of the linear separation property of kernel matrices of a dual space model to harmonize information in a privacy-preserving manner. However, this method does not handle the variance estimation and only provides point estimates: it cannot report test statistics and associated P-values. In this work, we extend VERTIGO by introducing a novel ring-structure protocol to pass on intermediary statistics among clients and successfully reconstructed the covariance matrix in the dual space. This extension, VERTIGO-CI, is a complete protocol to construct a logistic regression model from vertically partitioned datasets as if it is trained on combined data in a centralized setting. We evaluated our results on synthetic and real data, showing the equivalent accuracy and tolerable performance overhead compared to the centralized version. This novel extension can be applied to other types of generalized linear models that have dual objectives.

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935.