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

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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)...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Description and Phylogenetic Position of <i>Thermocypris isabella</i> gen. et sp. nov. (Ostracoda: Cyprididae: Cyprinotinae) from a Hot Spring in Peru.

Zoological science·2025
Same author

Vocational training for unemployed youth in Latvia.

Journal of population economics·2021
Same author

Frucooligosaccharides purification: Complexing simple sugars with phenylboronic acid.

Food chemistry·2019
Same author

Barriers and Explanatory Mechanisms of Delays in the Patient and Diagnosis Intervals of Care for Breast Cancer in Mexico.

The oncologist·2017
Same author

Emergence of Plasmid-Borne dfrA14 Trimethoprim Resistance Gene in Shigella sonnei.

Frontiers in cellular and infection microbiology·2016
Same author

Molecular mechanisms of gastrointestinal protection by quercetin against indomethacin-induced damage: role of NF-κB and Nrf2.

The Journal of nutritional biochemistry·2015

Related Experiment Video

Updated: May 30, 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

Endogenous treatment effects for count data models with endogenous participation or sample selection.

Massimiliano Bratti1, Alfonso Miranda

  • 1Department of Economics, Business and Statistics, Università degli Studi di Milano, Milan, Italy.

Health Economics
|August 11, 2011
PubMed
Summary

This study introduces a new statistical method to analyze how a treatment affects outcomes when participation or selection is endogenous. Ignoring these factors leads to biased estimates of treatment effects, particularly in health economics research.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 30, 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

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Econometrics
  • Health Economics
  • Biostatistics

Background:

  • Endogeneity in treatment effects and outcomes is a common challenge in empirical research.
  • Sample selection and endogenous participation can bias estimates in statistical models.
  • Accurate estimation is crucial for understanding treatment impacts, especially in health-related studies.

Purpose of the Study:

  • To propose a novel estimator for models with endogenous dichotomous treatments affecting count outcomes.
  • To account for endogeneity in both treatment participation/selection and the main outcome.
  • To address limitations of existing methods in health economics and related fields.

Main Methods:

  • Development of a maximum simulated likelihood estimator.
  • Modeling the effect of treatment on participation/selection and the outcome simultaneously.
  • Application to health economics data, specifically physician advice on alcohol consumption.

Main Results:

  • Neglecting treatment endogeneity results in incorrect effect sizes for physician advice on drinking intensity.
  • Ignoring endogenous participation leads to upward-biased treatment effect estimates.
  • Physician advice influences the intensity of drinking but not its prevalence.

Conclusions:

  • The proposed estimator effectively addresses endogeneity in treatment and participation/selection.
  • Accurate modeling is essential to avoid biased conclusions in health economics.
  • Findings highlight the nuanced impact of physician advice on alcohol consumption patterns.