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

Actuarial Approach01:20

Actuarial Approach

133
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,...
133
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

395
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...
395
Life Tables01:22

Life Tables

194
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
194
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
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.
196
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

285
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...
285
Censoring Survival Data01:09

Censoring Survival Data

230
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...
230

You might also read

Related Articles

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

Sort by
Same author

Hypertension and diabetes prevalence, associated factors, care cascade, and quality of life in older adults: A cross-sectional population-based study in The Gambia, South Africa, and Zimbabwe.

PLoS medicine·2026
Same author

The Economic Imperative of Living Donor Kidney Transplantation: A Single-Center Micro-Cost Analysis for Sustainable ESRD Care in Saudi Arabia.

Transplantation proceedings·2026
Same author

Remnant kidney volume-to-weight ratio and point score predict post-donor kidney function: the hypertrophy paradox and structure-demand model.

Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica·2026
Same author

Barriers to and enablers of childhood immunization uptake in Ethiopia's Amhara, Oromia, and Somali Regions: A multi-perspective qualitative study.

PLOS global public health·2026
Same author

Risk Factors and Outcomes of Bowel Complications in Single-Site Donor Nephrectomy: A Retrospective Analysis of 350 Consecutive Cases.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation·2026
Same author

Sex-specific bone architectural deficits among older adults living with HIV: a cross-sectional study from Zimbabwe.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026

Related Experiment Video

Updated: Sep 10, 2025

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

14.6K

War-time mortality in Sudan: a multiple systems estimation analysis.

Maysoon Dahab1, Rahaf AbuKoura1, Francesco Checchi1

  • 1Department of Infectious Disease Epidemiology and International Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.

The Lancet. Global Health
|August 22, 2025
PubMed
Summary
This summary is machine-generated.

Sudan

More Related Videos

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K
Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
09:23

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Published on: November 1, 2017

12.1K

Related Experiment Videos

Last Updated: Sep 10, 2025

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

14.6K
Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K
Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
09:23

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Published on: November 1, 2017

12.1K

Area of Science:

  • Public Health
  • Epidemiology
  • Conflict Studies

Background:

  • War in Sudan (since April 2023) has led to unmeasured mortality.
  • Sparse data and restricted access hinder accurate assessment.
  • Pre-war vital registration systems were inadequate.

Purpose of the Study:

  • To quantify undocumented war-time mortality levels and patterns in Sudan.
  • To estimate all-cause and intentional-injury mortality in Khartoum State.
  • To analyze age and cause-of-death patterns by region and month.

Main Methods:

  • Retrospective observational study.
  • Data collected from social media surveys and obituaries.
  • Probabilistic record matching and multiple systems estimation used.

Main Results:

  • In the first 14 months of war, preventable causes predominated.
  • Intentional-injury deaths were disproportionately high in Khartoum, Gezira, Kordofan, and Darfur.
  • Estimated 61,202 all-cause deaths in Khartoum (April 2023-June 2024), with 26,024 from intentional injuries.

Conclusions:

  • Sudan's war caused a substantial, undocumented rise in mortality, particularly in Khartoum.
  • Intentional-injury deaths in Khartoum exceeded reported conflict fatalities.
  • Preventable disease, hunger, and intentional injuries drove mortality nationwide, necessitating urgent action.