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

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 squares (OLS)...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

You might also read

Related Articles

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

Sort by
Same author

Variable selection-combined causal mediation analysis for continuous treatments with application to large-dimensional biomedical data.

PLoS computational biology·2026
Same author

Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.

One health (Amsterdam, Netherlands)·2026
Same author

Relevance of Kidney-Metabolic Multimorbidity Pattern to Metabolic Health and Mortality Among Elderly Inpatients in China.

Food science & nutrition·2026
Same author

Angiography-derived fractional flow reserve versus coronary angiography to guide coronary artery bypass grafting in patients undergoing surgical valve procedures with concomitant coronary artery disease in China (FAVOR IV-QVAS): a multicentre, triple-blind, randomised trial.

Lancet (London, England)·2026
Same author

Efficacy and safety of ruxolitinib for graft-versus-host disease prophylaxis in patients with aplastic anemia undergoing PBSC-only allogeneic stem cell transplantation: a prospective phase II study.

Experimental hematology & oncology·2026
Same author

Association between cholesterol and liver injury risk in solid cancer patients treated with PD-1 inhibitors: evidence from two cohort studies.

BMC cancer·2026
Same journal

Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Adaptive Fisher's method using weakly geometric grid for combining <i>p</i>-values with application to COVID-19 surveillance.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Robust domain selection for functional data via interval-wise testing and effect size mapping.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

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

A Partially Linear Regression Model for Data from an Outcome-Dependent Sampling Design.

Haibo Zhou1, Jinhong You, Guoyou Qin

  • 1University of North Carolina at Chapel Hill, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

Outcome dependent sampling enhances study power and cost-effectiveness. This research introduces a penalized spline maximum likelihood estimation (PSMLE) for semi-parametric models, improving analysis of complex relationships in epidemiological data.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Related Experiment Videos

Last Updated: May 28, 2026

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

  • Statistics
  • Epidemiology
  • Environmental Health

Background:

  • Outcome dependent sampling is increasingly used in research for efficiency.
  • Nonlinear confounding variables pose challenges in epidemiological studies.
  • Previous methods may not fully address semi-parametric models with this sampling scheme.

Purpose of the Study:

  • To develop and evaluate a novel estimation method for semi-parametric regression models under outcome dependent sampling.
  • To address nonlinear confounding variables in the context of in utero exposure and children's IQ.
  • To provide robust statistical inference for both parametric and nonparametric components.

Main Methods:

  • Proposed a penalized spline maximum likelihood estimation (PSMLE) approach.
  • Developed asymptotic properties for the proposed estimator.
  • Conducted simulation studies to assess performance.
  • Applied the method to a real-world IQ study data.

Main Results:

  • The PSMLE approach demonstrated effectiveness in handling semi-parametric models with outcome dependent sampling.
  • The method provided accurate inference for both linear and nonlinear effects.
  • Simulations showed the proposed estimator outperformed competing methods in certain scenarios.
  • Analysis of the IQ study yielded insights into the effects of in utero exposure.

Conclusions:

  • Penalized spline maximum likelihood estimation is a valuable tool for analyzing data from outcome dependent sampling schemes, especially with nonlinear confounders.
  • The proposed method offers a statistically sound and practical approach for epidemiological and environmental research.
  • This work advances the application of statistical methods in complex health studies.