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Related Concept Videos

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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.
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...

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Related Experiment Video

Updated: May 29, 2026

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

Measuring NHS performance 1990-2009 using amenable mortality: interpret with care.

Monica Desai1, Ellen Nolte, Marina Karanikolos

  • 1European Centre on Health of Societies in Transition, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK.

Journal of the Royal Society of Medicine
|September 2, 2011
PubMed
Summary

Mortality amenable to healthcare declined slower in England and Wales initially, but improved after 1999. This indicator is useful for assessing health system performance but requires careful interpretation.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Last Updated: May 29, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

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

Area of Science:

  • Public Health
  • Health Services Research
  • Epidemiology

Background:

  • A new NHS performance framework in England will use mortality amenable to healthcare to assess premature death prevention.
  • Historical health expenditure varied across the UK, with England and Wales initially spending less than Scotland and Northern Ireland.

Purpose of the Study:

  • To evaluate how the UK nations would have performed under the new mortality amenable to healthcare framework over the past two decades.
  • To analyze trends in amenable mortality in England and Wales, Northern Ireland, and Scotland.

Main Methods:

  • Age-standardized death rates from causes amenable to healthcare were analyzed for England and Wales, Northern Ireland, and Scotland between 1990-1999 and 1999-2009.
  • Linear regression assessed absolute changes, while average annual percent decline estimated relative changes.

Main Results:

  • From 1990-1999, amenable mortality declined more slowly in England and Wales compared to Scotland and Northern Ireland.
  • Following increased NHS funding in England and Wales after 1999, the rate of decline accelerated.
  • Analysis of specific causes revealed varied trends, with some improvements like breast cancer deaths occurring concurrently across the UK.

Conclusions:

  • Amenable mortality serves as a valuable metric for evaluating health system performance.
  • Methodological considerations are crucial for the accurate interpretation of amenable mortality data when implemented routinely in England.