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Related Concept Videos

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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Actuarial Approach01:20

Actuarial Approach

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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,...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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.
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Cancer Survival Analysis01:21

Cancer Survival Analysis

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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...
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Longitudinal Research02:20

Longitudinal Research

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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...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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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.
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Related Experiment Video

Updated: Apr 16, 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

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Research activity and the association with mortality.

Baris A Ozdemir1, Alan Karthikesalingam1, Sidhartha Sinha1

  • 1Department of Outcomes Research, St George's Vascular Institute, London, United Kingdom.

Plos One
|February 27, 2015
PubMed
Summary

Research active National Health Service (NHS) Trusts exhibit distinct characteristics and demonstrate lower risk-adjusted mortality rates for acute admissions. These findings suggest a link between research engagement and improved patient outcomes in healthcare settings.

Related Experiment Videos

Last Updated: Apr 16, 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

15.5K

Area of Science:

  • Health Services Research
  • Clinical Outcomes Research
  • Healthcare Management

Background:

  • Acute National Health Service (NHS) Trusts vary in their research activity levels.
  • Understanding the features of these Trusts is crucial for improving healthcare delivery.
  • The association between research activity and clinical outcomes requires investigation.

Purpose of the Study:

  • To characterize NHS Trusts based on their research activity.
  • To explore the relationship between research engagement and patient mortality.
  • To identify structural factors associated with research activity and outcomes.

Main Methods:

  • Utilized National Institute for Health Research (NIHR) funding and patient recruitment data as metrics for research activity.
  • Analyzed patient-level data for adult non-elective admissions from English Hospital Episode Statistics (2005-10).
  • Employed risk-adjusted mortality analyses to assess associations between Trust characteristics, research activity, and clinical outcomes.

Main Results:

  • Trusts with lower mortality rates received higher research funding and recruited more patients per bed.
  • Research-intensive Trusts were better staffed with more doctors, nurses, critical care beds, and operating theatres, and utilized radiology more.
  • Higher research funding and patient recruitment were significantly associated with better patient survival rates.

Conclusions:

  • Research-active NHS Trusts possess distinct structural and staffing compositions compared to less active ones.
  • A significant association exists between higher research activity and reduced risk-adjusted mortality for acute admissions.
  • The improved survival in research-active Trusts remained evident even after accounting for staffing and structural variables.