For prediction of elder survival by a Gompertz model, number dead is preferable to number alive

被引:0
|
作者
Dexter M. Easton
Henry R. Hirsch
机构
[1] Florida State University,Department of Biological Science
[2] University of Kentucky,Department of Physiology
来源
AGE | 2008年 / 30卷
关键词
Centenarians; Death rate; Gerontology; Gompertz survival; Mortality rate; Supercentenarians;
D O I
暂无
中图分类号
学科分类号
摘要
The standard Gompertz equation for human survival fits very poorly the survival data of the very old (age 85 and above), who appear to survive better than predicted. An alternative Gompertz model based on the number of individuals who have died, rather than the number that are alive, at each age, tracks the data more accurately. The alternative model is based on the same differential equation as in the usual Gompertz model. The standard model describes the accelerated exponential decay of the number alive, whereas the alternative, heretofore unutilized model describes the decelerated exponential growth of the number dead. The alternative model is complementary to the standard and, together, the two Gompertz formulations allow accurate prediction of survival of the older as well as the younger mature members of the population.
引用
收藏
相关论文
共 50 条
  • [41] Study on the Influence of the Number of Features on the Performance of Software Defect Prediction Model
    Cui, Mengtian
    Sun, Yue
    Lu, Yang
    Jiang, Yue
    ICDLT 2019: 2019 3RD INTERNATIONAL CONFERENCE ON DEEP LEARNING TECHNOLOGIES, 2019, : 32 - 37
  • [42] Model averaging prediction for time series models with a diverging number of parameters
    Liao, Jun
    Zou, Guohua
    Gao, Yan
    Zhang, Xinyu
    JOURNAL OF ECONOMETRICS, 2021, 223 (01) : 190 - 221
  • [43] Assessing model prediction performance for the expected cumulative number of recurrent events
    Olivier Bouaziz
    Lifetime Data Analysis, 2024, 30 (1) : 262 - 289
  • [44] Empirical comparison between different methods for genomic prediction of number of piglets born alive in moderate sized breeding populations
    Fangmann, A.
    Sharifi, R. A.
    Heinkel, J.
    Danowski, K.
    Schrade, H.
    Erbe, M.
    Simianer, H.
    JOURNAL OF ANIMAL SCIENCE, 2017, 95 (04) : 1434 - 1443
  • [45] Grey number prediction using the grey modification model with progression technique
    Shih, Chi-Sheng
    Hsu, Yen-Tseng
    Yeh, Jerome
    Lee, Pin-Chan
    APPLIED MATHEMATICAL MODELLING, 2011, 35 (03) : 1314 - 1321
  • [46] Nonlinear aerodynamics of airfoils at low Reynolds number and its prediction model
    Zhang P.
    Sun S.
    Hangkong Dongli Xuebao/Journal of Aerospace Power, 2024, 39 (01):
  • [47] A retweet number prediction model based on followers' retweet intention and influence
    Zhao, Huidong
    Liu, Gang
    Shi, Chuan
    Wu, Bin
    2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), 2014, : 952 - 959
  • [48] Prediction on the number of railway freight trains based on binary linear model
    Mu, Xin
    Cheng, Xueqing
    Zhu, Yongxia
    Tang, Yuan
    Mu, X., 1600, Chinese Academy of Railway Sciences (34): : 113 - 117
  • [50] PREDICTION MODEL FOR KAPPA NUMBER IN WHOLE STALK KENAF KRAFT PULPING
    Tang Jiebin
    Chen Kefu
    Xu Jun
    Li Jun
    RESEARCH PROGRESS IN PAPER INDUSTRY AND BIOREFINERY (4TH ISETPP), VOLS 1-3, 2010, : 1232 - 1235