Related Experiment Video
Updated: Jun 27, 2026

Measurement of Lifespan in Drosophila melanogaster
Published on: January 7, 2013
Survival after 100 years of age: a multivariate model of exceptional survival in Swedish centenarians
Bo Hagberg1, Gillis Samuelsson
1Hanö, S-29407 Solvesborg, Sweden. bo.lennart@mbox306.swipnet.se.
Background:
Most survival studies of the elderly population have set their baselines for first examinations between 60 and 80 years. The rapidly increasing numbers of exceptionally old persons call for knowledge about determinants of exceptional survival.
Methods:
The Swedish Centenarian Study followed 100 centenarians from the age of 100 to death of the entire cohort, by age 111 years. A biomedical, psychological, and social multivariate survival analysis was performed based on factors identified as important in earlier studies of older adults. Latent Variable Partial Least Square Estimation (LVPLS) Soft Modeling was used to test the hypothesized predictions of survival in centenarians.
Results:
Fewer predictors for survival were found in centenarians than were observed in studies of younger elderly persons. Survival after age 100 was dependent mainly on better baseline physical reserve, as measured by body mass index and body weight, and better baseline physical and cognitive function, as measured by activities of daily living and verbal ability/spatial orientation, respectively.
Conclusions:
Individual characteristics such as physiological reserve, present health and functional status, as well as chance appear important for centenarian survival. Hereditary factors, social relationships, marital status, and personality did not contribute to survival prediction in this exceptional age group. From a theoretical point of view, our data suggest that, in very old age, stochastic determinants may dominate over programmed factors (e.g., family longevity) in determining survival. More research is needed to assess survival factors at exceptional ages.
Related Concept Videos
Assumptions of Survival Analysis
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Cancer Survival Analysis
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Life Tables
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

