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.
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,...
Measurement of Bioavailability: Pharmacokinetic Methods01:30

Measurement of Bioavailability: Pharmacokinetic Methods

Pharmacokinetics is a vital branch of pharmacology that examines how drugs are absorbed, distributed, metabolized, and excreted by the body. Two key methodologies in pharmacokinetics are plasma drug concentration studies and urinary drug excretion analyses, both of which provide critical insights into a drug's therapeutic efficacy and bioavailability.Plasma Drug Concentration-Time StudiesPlasma drug concentration-time studies involve analyzing blood samples at specific intervals to quantify...
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...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

You might also read

Related Articles

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

Sort by
Same author

Efficacy and Safety of Dual Antiplatelet Therapy in Acute Ischemic Stroke With Recurrent Symptoms Post-thrombolysis: A Retrospective Cohort Study From the TriNetX US Collaborative Network.

Cureus·2026
Same author

Estimating the hazard ratio of excess mortality using a microsimulation framework with golden section search calibration: a methodological primer.

BMC medical research methodology·2026
Same author

Outcomes of cannulation strategies under antegrade cerebral perfusion in type a aortic dissection surgery: a systematic review and meta-analysis.

Journal of cardiothoracic surgery·2026
Same author

Aortic Growth After Isolated Aortic Valve Repair in Patients With Aortic Regurgitation Without Primary Aneurysm Indication.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery·2026
Same author

Reply to Nezic.

Interdisciplinary cardiovascular and thoracic surgery·2026
Same author

Risk of Neuropsychiatric Disorders After Pediatric Delirium in Children Under 12: A Cohort Study.

Psychiatry investigation·2026

Related Experiment Videos

Measuring follow-up completeness.

YingXing Wu1, Johanna J M Takkenberg, Gary L Grunkemeier

  • 1Medical Data Research Center, Providence Health System, Portland, Oregon, USA. yingxing.wu@providence.org

The Annals of Thoracic Surgery
|March 22, 2008
PubMed
Summary

A new method, C*, improves the accuracy of measuring patient follow-up completeness by accounting for unobserved deaths. This enhanced metric better reflects true follow-up rates in clinical studies.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Follow-up completeness is a critical quality indicator in longitudinal studies.
  • Current methods often rely on patient counts, which can be misleading.
  • Existing measures may underestimate true follow-up due to unobserved events.

Purpose of the Study:

  • To introduce a modified measure (C*) for follow-up completeness.
  • To address the underestimation inherent in previous metrics like Clark's C.
  • To provide a more accurate assessment of achieved follow-up years.

Main Methods:

  • Proposed a modified completeness measure (C*) accounting for unobserved deaths.
  • Calculated C and C* for coronary artery bypass graft patients.

Related Experiment Videos

  • Validated C and C* against true completeness data from the National Death Index.

    • Main Results:

    • Clark's C measure indicated 80.4% follow-up completeness.
    • The proposed C* measure showed 84.5% follow-up completeness.
    • National Death Index data confirmed true completeness at 85.0%.

    Conclusions:

    • The C* measure offers a more realistic and accurate estimation of follow-up completeness.
    • Accounting for unobserved deaths significantly improves the quality assessment of longitudinal data.
    • This refined methodology is crucial for evaluating the integrity of long-term patient follow-up.