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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

637
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...
637
Censoring Survival Data01:09

Censoring Survival Data

599
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...
599
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

866
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...
866
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

449
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
449
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

656
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
656
Naturalistic Observations02:30

Naturalistic Observations

17.7K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
17.7K

You might also read

Related Articles

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

Sort by
Same author

Who Is Paying the Extinction Debt? Phylogenetic and Functional Structuring on Greek Islands Is Shaped by Sea-Level Rise Since the Last Glacial Maximum.

Ecology and evolution·2026
Same author

Waterfowl Move Less in Heterogeneous and Human-Populated Landscapes, With Implications for Spread of Avian Influenza Viruses.

Ecology letters·2026
Same author

Correction: 'Revisiting Perdeck's massive avian migration experiments debunks alternative social interpretations' (2024), by Pot et al.

Biology letters·2026
Same author

Correction: A multi-species model for goose management: Competition and facilitation drive space use of foraging geese.

Ambio·2025
Same author

Arctic geese in newly colonised, colder breeding areas have higher spring body mass and breed earlier relative to the onset of spring.

The Journal of animal ecology·2025
Same author

Adult survival has a stronger role than productivity in the annual population change of European songbirds.

Oecologia·2025

Related Experiment Video

Updated: Feb 22, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Analyzing time-ordered event data with missed observations.

Adriaan M Dokter1,2,3, E Emiel van Loon3, Wimke Fokkema4

  • 1Dutch Centre for Avian Migration and Demography Netherlands Institute of Ecology Wageningen The Netherlands.

Ecology and Evolution
|September 26, 2017
PubMed
Summary

This study introduces a statistical method to correct for missed events in observational data, improving accuracy in time series analysis. The approach accurately estimates true event rates, crucial for ecological and biological research.

Keywords:
fecal outputinterval time seriesmissing datamixture modelobservation protocolprobability of detection

More Related Videos

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Feb 22, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Ecology
  • Statistics
  • Behavioral Science

Background:

  • Observational studies often suffer from missed events, leading to biased data.
  • This is common in wildlife observation, such as tracking animal defecation rates.
  • Undetected events distort time series analysis and inferences about natural processes.

Purpose of the Study:

  • To develop a statistical method to identify and correct for missed detections in event time series.
  • To provide a theoretical framework and practical tools for analyzing event data with potential observation bias.
  • To demonstrate the effectiveness of the methodology using simulations and a real-world case study.

Main Methods:

  • Derived the theoretical probability density function for observed intervals, incorporating missed detection probability.
  • Developed methodology and software tools for analyzing and correcting event data.
  • Applied the method to simulated data and a case study on goose defecation rates.

Main Results:

  • Simulations showed uncorrected data biased by up to a factor of 3, while corrected values were within 1% of true values.
  • The goose defecation case study revealed significant underestimates of true rates when missed observations were not accounted for.
  • The methodology effectively removed observational biases in time-ordered event data.

Conclusions:

  • The developed statistical methodology accurately corrects for missed events in observational time series.
  • Failure to account for missed observations can lead to substantial biases in ecological rate estimations.
  • This approach enhances the reliability of data from observational studies across various scientific fields.