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Cited by 156

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The analysis of survival (mortality) data: Fitting Gompertz, Weibull, and logistic functions

Wilson, David L.·1994·Mechanisms of Ageing and Development
DOI 10.1016/0047-6374(94)90095-7
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Abstract

Survival functions are fitted to survival data from several large populations. The Gompertz survival function corresponds to exponential mortality rate increases with time. The Weibull survival function corresponds to mortality rates that increase as a power function of time. A two-parameter, logistic survival function is introduced, and corresponds to mortality rates that increase, and then decrease, with time. A three-parameter logistic-mortality function also is examined. It reflects mortality rates that rise, and then plateau, with age. Data are from published studies of medflies, Drosophila, house flies, flour beetles, and humans. Some survival data are better fit by a logistic survival function than by the more traditionally used Gompertz or Weibull functions. Gompertz, Weibull, or logistic survival functions often fit the survival of 95+% of a population, and the 'tails' of the survival curves usually appear to fall between the values predicted by the three functions. For some populations, such 'tails' appear to be too complex to be fit well by any simple function. Survival data for males and females in some populations are best fit by different functions. Populations of 100 or more are needed to distinguish among the functions. When testing effects of environmental or genetic manipulations on survival, it has been common to determine the changes in parameter values for a given function, such as Gompertz. It may be equally important to determine whether the best-fit fu…

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2 of 2 references verified0 citation errors · 0 not checkable

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  • The functional forms such as Gompertz or Weibull curves used in previous comparisons of survival curves have no mechanistic basis.

    “Most previous approaches to compare survival curves of different species have fit functional forms such as Gompertz or Weibull curves15–18, but these functional forms have no mechanistic basis.”

    From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [15]

  • Most previous approaches to compare survival curves of different species have fit functional forms such as Gompertz or Weibull curves.

    “Most previous approaches to compare survival curves of different species have fit functional forms such as Gompertz or Weibull curves15–18, but these functional forms have no mechanistic basis.”

    From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [15]

  • Hazard curves are Weibull-like (power law) in some organisms and Gompertz-like (exponential) in others.

    “Hazard curves are Weibull-like (power law) in some organisms and Gompertz-like (exponential) in others15,59.”

    From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [15]

Citations

Cites

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Research works printed but not linked: 14

  1. Weibull, 1951, A statistical distribution function of wide applicability, J. Appl. Mech., 18, 293, 10.1115/1.4010337 · DOI
  2. Economos, 1979, A non-Gompertzian paradigm for mortality kinetics of metazoan animals and failure kinetics of manufactured products, Age, 2, 74, 10.1007/BF02432250 · DOI
  3. Carey, 1992, Slowing of mortality rates at older ages in large medfly cohorts, Science, 258, 457, 10.1126/science.1411540 · DOI
  4. Curtsinger, 1992, Demography of genotypes: failure of the limited life-span paradigm in Drosophila melanogaster, Science, 258, 461, 10.1126/science.1411541 · DOI
  5. Rockstein, 1959, A life table for the common house fly, Musca domestica, Gerontologia, 3, 23, 10.1159/000210828 · DOI
  6. U.S. Census, National Center for Health Statistics, Department of Health and Human Services, 1991, Vital Statistics of the United States, 1988, Vol. II — Mortality Part A
  7. Thatcher, 1982, The span of life, J. Inst. Actuaries, 109, 346
  8. Wilson, 1993, A comparison of methods for estimating mortality parameters from survival data, Mech. Ageing Dev., 66, 269, 10.1016/0047-6374(93)90014-I · DOI
  9. Nedler, 1965, A simplex method for function minimization, Comp. J., 7, 308, 10.1093/comjnl/7.4.308 · DOI · Paper page
  10. Caceci, 1984, Fitting curves to data: the simplex algorithm is the answer, Byte, 9, 340
  11. Juckett, 1993, Comparison of the Gompertz and Weibull functions as descriptors for human mortality distributions and their intersections, Mech. Ageing Dev., 69, 1, 10.1016/0047-6374(93)90068-3 · DOI
  12. Strehler, 1960, General theory of mortality and aging, Science, 132, 14, 10.1126/science.132.3418.14 · DOI · Paper page
  13. Lestienne, 1988, On the thermodynamical and biological interpretation of the Gompertzian mortality rate distribution, Mech. Ageing Dev., 42, 197, 10.1016/0047-6374(88)90047-4 · DOI
  14. Birch, 1953, Experimental background to the study of the distribution and abundance of insects, Ecology, 34, 698, 10.2307/1931333 · DOI

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Metadata

Authors
Wilson, D. L.
Venue
Mechanisms of Ageing and Development
Publisher
Elsevier BV
Published
1994-05
Volume
74
Issue
1-2
Pages
15-33
ISSN
0047-6374
DOI
10.1016/0047-6374(94)90095-7
Cited by
156
References
2
Language
en
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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