Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
89
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

568
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
568
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

405
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
405
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Federated feature selection with false discovery rate control.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same author

Creating change through short-term pilots and rapid cycle testing: a case of cardiovascular risk reduction.

BMJ open quality·2026
Same authorSame journal

PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

The Blue Coats Program to Improve and Sustain Workforce Well-Being.

NEJM catalyst innovations in care delivery·2026
Same author

Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.

NPJ digital medicine·2026
Same author

Dynamic response and pore evolution mechanism of composite improved loess using an eco-friendly curing agent and cement: a macroscopic and microscopic experimental study.

Scientific reports·2026

Related Experiment Video

Updated: Sep 23, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K

dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving

Chongliang Luo1,2, Md Nazmul Islam3, Natalie E Sheils3

  • 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Journal of the American Medical Informatics Association : JAMIA
|May 17, 2022
PubMed
Summary

A new lossless distributed algorithm for generalized linear mixed models (GLMMs) enables privacy-preserving hospital profiling using aggregated data. This method accurately calculates mortality rates without sharing individual patient data, ensuring data security and efficient analysis.

Keywords:
distributed penalized quasi-likelihood algorithmfederated learninggeneralized linear mixed modelhospital profilingprivacy-preserving

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Related Experiment Videos

Last Updated: Sep 23, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Area of Science:

  • Biostatistics
  • Health Informatics
  • Computational Epidemiology

Background:

  • Generalized linear mixed models (GLMMs) are crucial for hospital profiling using clinical data.
  • Individual patient data (IPD) privacy regulations and computational complexity hinder traditional GLMM analysis.
  • A distributed approach is necessary for privacy-preserving and efficient hospital profiling.

Purpose of the Study:

  • To develop a lossless distributed algorithm for GLMM fitting.
  • To enable privacy-preserving hospital profiling without sharing IPD.
  • To demonstrate the calculation of standardized mortality rates distributively.

Main Methods:

  • Developed a novel distributed penalized quasi-likelihood (dPQL) algorithm.
  • Applied the dPQL algorithm to fit GLMM using aggregated data across hospitals.
  • Validated the method on COVID-19 mortality data for 929 hospitals.

Main Results:

  • The dPQL algorithm is mathematically proven to be lossless, yielding identical results to pooled IPD.
  • Achieved convergence in only 5 iterations for COVID-19 mortality profiling.
  • Distributed calculation of fixed effects, random effects, and mortality rates matched pooled data results.

Conclusions:

  • The dPQL algorithm offers a lossless, privacy-preserving, and fast-converging solution for GLMM fitting.
  • This distributed approach is highly suitable and convenient for hospital profiling.
  • Enables secure and efficient analysis of sensitive health data for comparative studies.