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

Conjugate Addition (1,4-Addition) vs Direct Addition (1,2-Addition)01:27

Conjugate Addition (1,4-Addition) vs Direct Addition (1,2-Addition)

4.3K
α,β-Unsaturated carbonyl compounds with two electrophilic sites, the carbonyl carbon, and the β carbon, are susceptible to nucleophilic attack via two modes: conjugate or 1,4-addition and direct or 1,2-addition.
Conjugate addition results in a thermodynamically stable product. The reaction retains the stronger C=O bond at the expense of the weaker C=C π bond. The process is slow as the β carbon is less electrophilic than the carbonyl carbon.
Direct addition products are...
4.3K
Relative Risk01:12

Relative Risk

2.1K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.1K
What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
Conjugate Addition of Enolates: Michael Addition01:08

Conjugate Addition of Enolates: Michael Addition

3.6K
The attack of a nucleophile at the β carbon of an α,β-unsaturated carbonyl compound is called conjugate addition. Conjugate addition reactions of active methylene compounds, such as β-diketones, β-keto esters, β-keto nitriles, and α-nitro ketones, are called Michael addition reactions.
3.6K
Electric Potential and Potential Difference01:16

Electric Potential and Potential Difference

5.7K
Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
When a test charge moves from the initial to the final position, the electric potential difference between those positions is defined as the ratio of the change in the potential energy to the charge on the...
5.7K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.3K

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

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 author

Leveraging precision medicine analytics to optimize inflammation reduction and enhance physical function in older adults.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Related Experiment Video

Updated: Jan 30, 2026

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

2.6K

Semiparametric Additive Model for Estimating Risk Difference in Multicenter Studies.

Donglin Zeng1, Noorie Hyun2, Jianwen Cai1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599.

Biostatistics & Epidemiology
|January 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for multi-center cancer studies to address data variations. The semiparametric additive risk model accurately estimates risk effects, accounting for center-specific differences.

Keywords:
Additive risk modelsEstimating equationmulti-center studyone-to-one matched designproportional hazards modelrecurrent event

More Related Videos

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
10:11

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies

Published on: October 22, 2014

19.7K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Related Experiment Videos

Last Updated: Jan 30, 2026

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

2.6K
Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
10:11

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies

Published on: October 22, 2014

19.7K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Multi-center studies offer larger patient populations but face challenges with center-to-center heterogeneity.
  • Ignoring such heterogeneity can lead to biased results in cancer research.
  • Robust statistical methods are needed to handle variations in multi-center data.

Purpose of the Study:

  • To propose semiparametric additive risk models with a general link function for analyzing multi-center studies.
  • To estimate risk effects while accounting for center-specific baseline functions.
  • To provide a statistically sound method for handling heterogeneity in cancer research.

Main Methods:

  • Development of semiparametric additive risk models with a general link function.
  • Formulation of an estimating equation for statistical inference.
  • Demonstration of consistency and asymptotic normality of the proposed estimators.

Main Results:

  • The proposed method effectively estimates risk effects in the presence of center-specific baseline functions.
  • Simulation studies confirmed good small-sample performance of the statistical method.
  • The method was successfully applied to analyze data from the Study of Left Ventricular Dysfunction (SOLVD).

Conclusions:

  • The proposed semiparametric additive risk models provide a reliable approach for analyzing complex multi-center cancer data.
  • The method accounts for critical center-specific variations, enhancing the accuracy of risk effect estimation.
  • The approach is applicable to various study designs, including matched cohort studies.