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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
What are Estimates?01:06

What are Estimates?

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 as the mean,...

You might also read

Related Articles

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

Sort by
Same author

Data sharing in stepped-wedge cluster randomized trials: suboptimal data availability despite "data available upon request".

Journal of clinical epidemiology·2026
Same author

CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.

BMJ (Clinical research ed.)·2026
Same author

Factors affecting power in stepped wedge trials when the treatment effect varies with time.

Trials·2026
Same author

Investigating the HIV epidemic among Black gay and bisexual men in the Southern United States: Results of the HPTN 096 pilot cross-sectional assessment.

PloS one·2025
Same author

A results to action framework for community verification: A case study from a performance based financing program in Zimbabwe.

PLOS global public health·2025
Same author

Comparative analysis of HIV data completeness in Haiti's iSanté Plus Electronic Medical Record system across children, adolescents and adults: a cross-sectional evaluation of 2016-2022 data.

BMJ open·2025

Related Experiment Video

Updated: Jun 14, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Estimating duration in partnership studies: issues, methods and examples.

Bart Burington1, James P Hughes, William L H Whittington

  • 1Genentech, San Francisco, California, USA.

Sexually Transmitted Infections
|March 25, 2010
PubMed
Summary
This summary is machine-generated.

Estimating sexual partnership duration requires accounting for censoring and truncation. Ignoring these factors leads to biased results, with adjusted median duration significantly shorter than unadjusted estimates.

Related Experiment Videos

Last Updated: Jun 14, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Modeling

Background:

  • Understanding sexual partnership duration is crucial for sexual behavior analysis.
  • Accurate duration estimates inform sexually transmitted infection (STI) transmission risk assessments.
  • Time course data are vital for developing robust disease transmission models.

Purpose of the Study:

  • To identify and address biases in estimating sexual partnership duration.
  • To propose and illustrate improved study designs and analysis methods.
  • To correct for censoring, truncation, and sampling issues in duration estimation.

Main Methods:

  • Utilized data from a sexual behavior survey of individuals from health and STD clinics.
  • Collected detailed partnership information including start dates and encounter types.
  • Employed follow-up assessments every 4 months for up to 1 year.
  • Applied statistical adjustments for censoring and truncation.

Main Results:

  • Unadjusted median partnership duration was 9 months, but adjusted duration decreased to 1.6 months.
  • Adjustments for censoring and truncation significantly reduced bias in relative risk estimates.
  • Weighted estimation methods also demonstrated effectiveness in reducing bias for duration distributions.

Conclusions:

  • Effective methods exist for estimating partnership duration from censored and truncated data.
  • Failure to address sampling issues like censoring and truncation results in substantial estimation bias.
  • Accurate partnership duration is essential for reliable epidemiological studies and public health interventions.