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

270
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...
270
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

96
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...
96
Hazard Rate01:11

Hazard Rate

202
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
202
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K
Binomial Probability Distribution01:15

Binomial Probability Distribution

12.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
12.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

190
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
190

You might also read

Related Articles

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

Sort by
Same author

Rare Coding Variants Reveal Distinct Genetic Architectures Across Multidimensional Sleep Phenotypes.

medRxiv : the preprint server for health sciences·2026
Same author

Disentangling direct and indirect genetic pathways to neurodevelopmental risk: brain structure and behavior in a population-based parent-offspring trio study.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same author

Temperature exposure and time adolescents spend in physical activity across intensity levels.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

Associations of ambient temperature exposure with embryonic and early fetal development.

International journal of epidemiology·2026
Same author

Examining bidirectional associations between overprotective parenting and adolescent problem behaviors: a multiverse analysis.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same author

Evaluation of the Centers for Disease Control and Prevention-Harvard T.H. Chan School of Public Health Program Evaluation Practicum.

Journal of public health management and practice : JPHMP·2026
Same journal

BAYESIAN MIXED MULTIDIMENSIONAL SCALING FOR AUDITORY PROCESSING.

Psychometrika·2026
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Oct 4, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K

A Censored Mixture Model for Modeling Risk Taking.

Nienke F S Dijkstra1, Henning Tiemeier1,2, Bernd Figner3

  • 1Erasmus University Rotterdam, Rotterdam, The Netherlands.

Psychometrika
|February 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new censored mixture model to better understand risk-taking behavior. The model accounts for censored data, outcome attractiveness, and individual risk preferences in experimental settings.

Keywords:
Columbia Card TaskGeneration R Studycensoringfinite mixturesmultiple inflated model

More Related Videos

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

2.2K
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

10.4K

Related Experiment Videos

Last Updated: Oct 4, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
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

2.2K
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

10.4K

Area of Science:

  • Behavioral economics
  • Psychological modeling
  • Quantitative psychology

Background:

  • Risk behavior significantly impacts health, well-being, and overall conduct.
  • Existing research documents links between real-world and experimental risk-taking, but modeling remains complex.
  • Challenges include censored observations, outcome desirability, and unobserved participant heterogeneity.

Purpose of the Study:

  • To develop a novel statistical model for analyzing risk-taking behavior in experimental tasks.
  • To address limitations in current models, specifically censoring, outcome attractiveness, and individual differences.
  • To provide a more accurate representation of risk-taking propensity.

Main Methods:

  • Proposal of a censored mixture model.
  • The model incorporates parameters for censoring, outcome attractiveness, and unobserved individual risk preferences.
  • It also accounts for experimental conditions influencing risk-taking.

Main Results:

  • The censored mixture model effectively handles censored data common in risk tasks.
  • It quantifies the influence of outcome attractiveness on risk-taking decisions.
  • The model differentiates between unobserved participant groups with varying risk preferences.

Conclusions:

  • The censored mixture model offers a robust framework for studying experimental risk behavior.
  • It improves upon existing methods by integrating key factors influencing risk-taking.
  • This approach enhances our understanding of individual differences in risk preferences and decision-making.