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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

318
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...
318
Modeling with Differential Equations01:25

Modeling with Differential Equations

150
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
150
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

692
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
692
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

475
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
475

You might also read

Related Articles

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

Sort by
Same author

Assessing global factors associated with tropical cyclone-related mortality: A population-based longitudinal study.

Environment international·2026
Same author

Extreme heat and cause-specific risk of hospital admission in the adult population in England: a case time series analysis.

BMJ open·2026
Same author

Interaction Between Air Pollution and Genetic Predisposition to Blood Pressure and LDL-C on Cardiovascular Events.

European journal of preventive cardiology·2026
Same author

Minimum mortality temperature by cause of death and age group: A multi-country observational study (1990-2019).

Environmental research·2026
Same author

Temporal changes in mortality risk associated with PM<sub>10</sub> across 143 cities in 26 countries: a multicountry, multicity time-series study.

The Lancet. Planetary health·2026
Same author

Projecting climate change impacts on health: A tutorial integrating the latest climate and demographic scenarios.

Environmental epidemiology (Philadelphia, Pa.)·2026

Related Experiment Video

Updated: Mar 18, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Modelling Lagged Associations in Environmental Time Series Data: A Simulation Study.

Antonio Gasparrini1

  • 1From the Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Epidemiology (Cambridge, Mass.)
|July 12, 2016
PubMed
Summary
This summary is machine-generated.

This study compares moving average and distributed lag models for analyzing environmental time series. Distributed lag models offer more accurate results for complex, long-lagged associations, outperforming simple moving averages.

More Related Videos

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster
08:13

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster

Published on: July 17, 2017

9.6K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Related Experiment Videos

Last Updated: Mar 18, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster
08:13

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster

Published on: July 17, 2017

9.6K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Area of Science:

  • Environmental Science
  • Statistical Modeling

Background:

  • Environmental time series data often exhibit complex lagged associations.
  • Accurate modeling of these associations is crucial for understanding environmental processes.

Purpose of the Study:

  • To compare the performance of moving average (MA) methods with distributed lag linear and nonlinear models (DLNM) for analyzing linear and nonlinear lagged associations in environmental time series.
  • To evaluate bias and confidence interval accuracy under various lag structures and seasonal trends.

Main Methods:

  • Simulation studies were conducted to compare two approaches: simple moving average summaries and more flexible distributed lag models.
  • The models were assessed for their ability to estimate linear and nonlinear lagged associations in simulated environmental time series data.

Main Results:

  • Distributed lag models (DLMs) provided estimates with low bias and accurate confidence intervals, even for long lags and strong seasonality.
  • Moving average models were viable only for short lags when correctly specified; otherwise, they introduced substantial bias.
  • DLMs demonstrated significant advantages for complex and long-lagged associations.

Conclusions:

  • Distributed lag models are recommended for analyzing complex lagged associations in environmental time series data.
  • Simple moving average methods should be used cautiously, primarily for short and well-defined lag periods.
  • Flexible modeling approaches are essential for robust inference in environmental time series analysis.