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Related Concept Videos

Levels of Health Promotion and Illness Prevention01:26

Levels of Health Promotion and Illness Prevention

Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
In primary prevention, actions taken before disease onset prevent the disease from...
Preventive Healthcare Services01:30

Preventive Healthcare Services

Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:
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,...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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,...
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...

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Related Experiment Video

Updated: Jul 3, 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

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years

Lingxiao Wang1,2, Hormuzd A Katki1, Anil K Chaturvedi1

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, Maryland, USA.

Statistics in Medicine
|July 1, 2026
PubMed
Summary

Precision prevention often selects high-risk individuals, but benefit-based selection may be better when disease risk and competing mortality are correlated. This approach prioritizes maximizing life-years gained from interventions like lung-cancer screening.

Keywords:
absolute risk predictioncompeting riskspopulation representative risk modelingprecision cancer screeningrestricted mean survival timesurvival analysis

Related Experiment Videos

Last Updated: Jul 3, 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

Area of Science:

  • Biostatistics
  • Preventive Medicine
  • Health Services Research

Background:

  • Precision prevention typically identifies high-risk individuals for screening, assuming maximum benefit for those most at risk.
  • This assumption may fail when disease risk and competing mortality are highly correlated, potentially leading to interventions with limited life-years gained and increased harm for older patients with comorbidities.

Purpose of the Study:

  • To propose and evaluate a benefit-based selection strategy for prevention services, prioritizing individuals with the greatest expected gain in life-years.
  • To compare the effectiveness of benefit-based selection against traditional risk-based selection, particularly for lung-cancer screening in ever-smokers.

Main Methods:

  • Estimating expected life-years gained by integrating data from a randomized trial and a population-representative survey.
  • Deriving Taylor-linearized variances to account for sampling variability from both data sources.
  • Conducting simulation studies to compare benefit-based and risk-based selection strategies under various conditions.

Main Results:

  • Benefit-based selection identified individuals with more favorable benefit-harm trade-offs for lung-cancer screening compared to risk-based selection.
  • Simulation studies explored scenarios where each selection strategy may be preferable.

Conclusions:

  • A benefit-based selection strategy offers a potentially more equitable and effective approach to precision prevention, especially when disease risk and competing mortality are intertwined.
  • This method optimizes the allocation of preventive resources by focusing on maximizing individual life-years gained.