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

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
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Quantitative Analysis01:12

Quantitative Analysis

603
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
603
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

602
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
602
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

710
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
710
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.9K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures.

Biometrics·2026
Same author

Efficacy of a combined reverse and modified Valsalva maneuver for the cardioversion of paroxysmal supraventricular tachycardia: a retrospective cohort study.

Scientific reports·2026
Same author

How auditory development affects language acquisition: Influences of socioeconomic status and gestational age at birth.

PloS one·2026
Same author

Prenatal exposure to wildfire PM2.5 and pregnancy loss in Colorado, USA, 2007-2018.

International journal of epidemiology·2026
Same author

Association between prenatal ambient particulate matter and childhood asthma is modified by community safety and child sex.

Scientific reports·2025
Same author

Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree.

The R journal·2025
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K

Smooth and shape-constrained quantile distributed lag models.

Yisen Jin1, Aaron J Molstad1,2, Ander Wilson3

  • 1Department of Statistics, University of Florida, Gainesville, FL 32611, United States.

Biometrics
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

Identifying critical windows of pregnancy vulnerability to environmental pollutants is key for infant health. New quantile distributed lag models (QDLMs) improve analysis of pollutant exposure timing and its impact on birth weight.

Keywords:
distributed lag modelsenvironmental epidemiologyquantile regressionshape-constrained regression

More Related Videos

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Related Experiment Videos

Last Updated: Sep 10, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K
ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Area of Science:

  • Environmental Epidemiology
  • Reproductive Health
  • Biostatistics

Background:

  • Prenatal exposure to environmental pollutants impacts infant health outcomes, including birth weight and neurological development.
  • Identifying critical windows of susceptibility during pregnancy is crucial for targeted public health interventions.
  • Traditional distributed lag models (DLMs) primarily model the conditional mean, potentially missing important distributional effects.

Purpose of the Study:

  • To introduce novel quantile distributed lag model (QDLM) estimators for analyzing environmental exposure impacts on health outcomes.
  • To address limitations of traditional DLMs by incorporating shape constraints for enhanced interpretability and efficiency.
  • To identify critical windows of susceptibility to environmental pollutants during gestation.

Main Methods:

  • Development of two new quantile distributed lag model (QDLM) estimators.
  • Application of smoothness and shape constraints (unimodality, concavity) to QDLMs.
  • Analysis of the Colorado birth cohort data using the proposed QDLM estimators.

Main Results:

  • The new QDLM estimators effectively identified critical windows of susceptibility to environmental pollutants during pregnancy.
  • The models demonstrated improved interpretability and efficiency compared to traditional DLMs.
  • Analysis of the Colorado birth cohort data provided insights into pollutant exposure timing and birth weight.

Conclusions:

  • The developed QDLM estimators offer a powerful tool for environmental epidemiology, particularly for analyzing health outcome distributions.
  • These methods enhance the identification of critical exposure periods, informing the development of effective public health strategies.
  • The study highlights the importance of considering quantile-specific effects in understanding the impact of environmental exposures on gestational health.