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

State Space Representation01:27

State Space Representation

208
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
208
Transfer Function to State Space01:23

Transfer Function to State Space

257
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
257
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

41
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...
41
Epistasis Analysis01:09

Epistasis Analysis

5.0K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.0K
State Space to Transfer Function01:21

State Space to Transfer Function

205
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
205
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
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...
54

You might also read

Related Articles

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

Sort by
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

An enhanced exact permutation rank-based inferential seamless phase 2/3 design.

Journal of biopharmaceutical statistics·2026
Same author

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.

The annals of applied statistics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same author

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Journal of machine learning research : JMLR·2026
Same author

Data fusion methods for the heterogeneity of treatment effect and confounding function.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.0K

Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes.

Tianchen Xu1, Yuan Chen2, Donglin Zeng3

  • 1Department of Biostatistics Mailman School of Public Health, Columbia University, NY 10032, USA.

Journal of the American Statistical Association
|February 26, 2024
PubMed
Summary
This summary is machine-generated.

We developed a novel statistical model to analyze digital phenotype data from mobile health studies. This approach effectively captures patient health status and treatment effects, overcoming challenges of data variability and noise for Parkinson's disease research.

Keywords:
Parkinson’s diseaseheterogeneous treatment effectslatent state-space modelmHealthobservational studiestime series

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K

Related Experiment Videos

Last Updated: Jul 2, 2025

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K

Area of Science:

  • Digital Health
  • Biostatistics
  • Wearable Technology

Background:

  • Digital technologies offer objective, real-world data collection for health monitoring.
  • Modeling digital phenotype data is challenging due to confounding, variability, and measurement noise.
  • Parkinson's disease (PD) research can benefit from advanced methods to interpret complex digital data.

Purpose of the Study:

  • To develop a statistical model for jointly analyzing multi-dimensional, multi-modal digital phenotypes.
  • To capture latent health states and time-varying treatment effects from mobile health data.
  • To address the inherent variabilities and noise in digital phenotype measurements.

Main Methods:

  • Developed a mixed-response state-space (MRSS) model to represent latent health states.
  • Utilized the Kalman filter for Gaussian phenotypes and importance sampling with Laplace approximation for non-Gaussian phenotypes.
  • Applied the model to a mobile health study involving remote data collection from PD patients.

Main Results:

  • The MRSS model successfully integrated multi-modal digital phenotypes, reflecting dynamic health status.
  • Latent states effectively captured personalized, time-varying treatment effects.
  • The model demonstrated advantages in handling between- and within-patient variability and measurement noise.

Conclusions:

  • The MRSS model provides a robust framework for analyzing complex digital phenotype data in mobile health.
  • This approach enhances the understanding of disease progression and treatment efficacy in conditions like Parkinson's disease.
  • The developed methods facilitate more accurate and personalized remote patient monitoring.