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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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,...
Life Tables01:22

Life Tables

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,...
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.
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

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Measurement of Lifespan in Drosophila melanogaster
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Mortality and refusal in the longitudinal 90+ project.

Rocío Fernández-Ballesteros1, María Dolores Zamarrón, Juan Díez-Nicolás

  • 1Department of Psychobiology and Health, Ivan Paulov, 6 Autonomous University of Madrid, 28049 Madrid, Spain. r.fballesteros@uam.es

Archives of Gerontology and Geriatrics
|October 15, 2010
PubMed
Summary

Attrition in aging studies is high due to mortality and dropout. Successful aging factors like physical activity and mental status at baseline predict survival and participation in the very old.

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Area of Science:

  • Gerontology
  • Epidemiology
  • Longitudinal Studies

Background:

  • Longitudinal studies on aging face significant attrition challenges, primarily from participant mortality and dropout.
  • Understanding factors influencing attrition is crucial for maintaining study integrity and generalizability.

Purpose of the Study:

  • To analyze attrition causes and identify predictors of mortality, dropout, and follow-up participation in a cohort of centenarians.
  • To explore bio-psycho-social differences between participants who completed follow-up, died, or dropped out.

Main Methods:

  • The 90+ Project baseline cohort (n=188, age >90) was assessed using the European Survey on Aging Protocol (ESAP).
  • Data included anthropometric, health, lifestyle, bio-behavioral, psychological, and social variables.
  • Participants were categorized post-follow-up as re-assessed (55%), deceased (11%), or dropouts (34%).

Main Results:

  • Mortality rates were three times higher for individuals in residences compared to those in the community.
  • Regular physical activity, mental status, leisure activities, fitness, perceived control, and openness at baseline differentiated the three groups.
  • Individuals classified as 'non-successful agers' at baseline were more likely to die during the study period.

Conclusions:

  • Willingness to participate, rather than mortality alone, is a key factor in very old adult longitudinal studies.
  • Contextual, behavioral, and psychological factors significantly influence mortality, survival, and participation in advanced age.