Skip to main content
SpringerLink
Log in

Standard and Innovative Statistical Methods for Empirically Analyzing Cancer Morbidity and Mortality

  • Chapter
Cancer Mortality and Morbidity Patterns in the U.S. Population

Part of the book series: Statistics for Biology and Health ((SBH))

  • 1046 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Edmond Halley (1656–1742), an English astronomer, geophysicist, mathematician, meteorologist and physicist, who published in 1693 an article on life annuities, in which he analyzed the age-at-death on the basis of the city of Breslau statistics provided by Caspar Nemann (a clergyman from Breslau who had a special interest in mortality rates). Thus Halley influenced the developing of actuarial science. His work followed a more primitive work by Graunt, and is one of the most important studies in the history of demography.

References

  • Akushevich I., Kulminski A., Manton K., 2005a. Life tables with covariates: life tables with covariates: dynamic model for nonlinear analysis of longitudinal data. Math Popul Stud 12(2):51–80.

    Google Scholar 

  • Akushevich I., Kulminski A., Manton K.G., 2005b. Human Mortality and Chronic Disease Incidence at Extreme Ages: New Data and Analysis. Talk given at Session “New Direction on Mortality Research” in the Population Association of America Annual Meeting, Philadelphia, March 31–April 2, 2005.

    Google Scholar 

  • Akushevich I., Kulminski A., Akushevich L., Manton K.G., 2006. Age Patterns of Disease Incidences in the U.S. Elderly: Population-Based Analysis. Trends Working Paper Series.

    Google Scholar 

  • Allison P.D., 1984. Event History Analysis. Beverly Hills, CA: SAGE Publications.

    Google Scholar 

  • Anderson D.E., 1974. Genetic study of breast cancer: identification of a high risk group. Cancer 34(4):1090–1097.

    Article  Google Scholar 

  • Armitage P., Doll R., 1954. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer 8(1):1–12.

    Google Scholar 

  • Armitage P., Doll R., 1961. Stochastic models for carcinogenesis. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA: University of California Press.

    Google Scholar 

  • Beard, R.E. (1959). Note on some mathematical mortality models. In: Wolstenholme, G.E.W., O’Connor, M. (Eds.). The Lifespan of Animals (pp. 302–311). Boston: Little, Brown.

    Google Scholar 

  • Beard, R.E. (1971). Some aspects of theories of mortality, cause of death analysis, forecasting and stochastic processes. In: Brass, W. (Ed.), Biological Aspects of Demography (pp. 57–68). London: Taylor and Francis.

    Google Scholar 

  • Bishop Y.M.M., Fienberg S.E. Discrete Multivariate Analysis: Theory and Practice, Cambridge, MA: The MIT Press, 1975.

    MATH  Google Scholar 

  • Brown L.D., Cai T.T., DasGupta A., 2001. Interval Estimation for a Binomial Proportion. Stat Sci 16:101–117.

    MATH  MathSciNet  Google Scholar 

  • Chiang C.L., 1968. Introduction to Stochastic Processes in Biostatistics. New York : John Wiley

    MATH  Google Scholar 

  • Cox D., 1972. Regression models and life tables (with discussion). J R Stat Soc, Ser B 34:187–220.

    MATH  Google Scholar 

  • De Waard F., Halewijn B., Huizinga J., 1964. The bimodal age distribution of patients with mammary carcinoma. Cancer 17:141–152.

    Article  Google Scholar 

  • Frier B., 1982. Roman life expectancy: Ulpian’s evidence. Harv Stud Classic Philol 86:213–251.

    Article  Google Scholar 

  • Gavrilov L.A., Gavrilova N.S., 2006a. Models of systems failure in aging. In: P. Michael Conn (Ed): Handbook of Models for Human Aging, Burlington, MA: Elsevier Academic Press, 45–68.

    Google Scholar 

  • Gavrilov L.A., Gavrilova N.S., 2006b. Reliability theory of aging and longevity. In: Masoro E.J. & Austad S.N. (eds.): Handbook of the Biology of Aging, 6th edition. San Diego, CA, USA: Academic Press 3–42.

    Google Scholar 

  • Hebert P.L., Geiss L.C., Tierney E.F. et al., 1999. Identifying person with diabetes using medicare claims data. Am J Med Qual 14(6):270–277.

    Article  Google Scholar 

  • Hougaard P., 1988. A boundary modification of kernel function smoothing, with application to insulin absorption kinetics. In Compstat Lectures 31–36. Physica, Vienna.

    Google Scholar 

  • Jacquez J.A., 1972. Compartmental Analysis in Biology and Medicine, volume 50. New York: Elsevier.

    Google Scholar 

  • Jones H.W., 1945. John Graunt and His Bills of Mortality. Bull Med Libr Assoc 33(1):3–4.

    Google Scholar 

  • Kaplan E.L., Meier P., 1958. Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481.

    Article  MATH  MathSciNet  Google Scholar 

  • Kertzer D.I., Laslet P. (eds.), 1995. Aging in the Past. Berkley-Los Angeles-Oxford: University of California Press. At: http://ark.cdlib.org/ark:/13030/ft096n99tf.

  • Kestenbaum B.A., 1992. Description of extreme aged population based on improved medicare enrollment data. Demography 29(4):565–580.

    Article  Google Scholar 

  • Kestenbaum B., Ferguson B.R., 2002. Mortality of the extreme aged in the United States in the 1990s, based on improved medicare data. North Am Actua J 6(3):35–44.

    MathSciNet  Google Scholar 

  • Kloeden P.E., Platen E., 1992. Numerical Solution of Stochastic Differential Equations. Application of Mathematics Series, volume 23. Heidelberg: Springer-Verlag.

    Google Scholar 

  • Kloeden P.E., Platen E., Schurz H., 1994. Numerical Solution of SDE Through Computer Experiments. Berlin: Springer.

    MATH  Google Scholar 

  • Knudson A.G. Jr., 1971. Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 68:820–823.

    Article  Google Scholar 

  • Kulminski A., Akushevich I., Manton K., 2004. Modeling nonlinear effects in longitudinal survival data: implications for the physiological dynamics of biological systems. Front Biosci 9:481–493.

    Article  Google Scholar 

  • MacMahon B., Cole P., Brown J., 1973. Etiology of human breast cancer: a review. J Natl Cancer Inst, 50:21–42.

    Google Scholar 

  • Manton K.G., Stallard E., 1980. A two-disease model of female breast cancer: mortality in 1969 among white females in the United States. J Natl Cancer Inst. 64(1):9–16.

    MathSciNet  Google Scholar 

  • Manton K.G., Stallard E., 1984. Recent Trends in Mortality Analysis. Orlando, Florida, USA: Academic Press.

    Google Scholar 

  • Manton K.G., Stallard E., 1988. Chronic Disease Modeling: Measurement and Evaluation of the Risks of Chronic Disease Processes. London: Charles Griffin.

    Google Scholar 

  • Manton K.G., Stallard E., 1996. Longevity in the United States: age and sex-specific evidence on life span limits from mortality patterns: 1960–1990. J Gerontol Ser A-Biol Sci Med Sci 51(5):B362–B375.

    Google Scholar 

  • Manton K.G., Land K.C., 2000a. Active life expectancy estimates for the U.S. elderly population: a multidimensional continuous-mixture model of functional change applied to completed cohorts, 1982 to 1996. Demography 37(3):253–265.

    Google Scholar 

  • Manton K.G., Land K.C., 2000b. Multidimensional disability/mortality trajectories at ages 65 and over: the impact of state dependence. Soc Indic Res 51(2):193–221.

    Google Scholar 

  • Manton K.G., Gu X., 2001. Changes in the prevalence of chronic disability in the United States black and non-black population above age 65 from 1982 to 1999. Proc Natl Acad Sci USA 98(11):6354–6359.

    Article  Google Scholar 

  • Manton K.G., Patrick C.H., Stallard E., 1980. Mortality model based on delays in progression of chronic diseases: alternative to cause elimination model. Public Health Rep. 95(6):580–588.

    Google Scholar 

  • Manton, K., Woodbury, M., Stallard, E. 1981. A variance components approach to categorical data models with heterogenous mortality rates in North Carolina counties, Biometrics 37:259–269.

    Article  Google Scholar 

  • Manton K.G., Stallard E., Vaupel J.W., 1986. Alternative models for the heterogeneity of mortality risks among the aged. J Am Stat Assoc. 81(395):635–644.

    Article  Google Scholar 

  • Manton K.G., Woodbury M.A., Stallard E. et al., 1989. Empirical Bayes procedures for stabilizing maps of U.S. cancer mortality rates. J Am Stat Assoc. 84(407):637–650.

    Article  Google Scholar 

  • Manton K.G., Stallard E., Singer B.H., 1992. Projecting the future size and health status of the U.S. elderly population. Int. J. Forecast 8:433–458.

    Article  Google Scholar 

  • Manton K.G., Lowrimore G., Yashin A., 1993. Methods for combining ancillary data in stochastic compartment models of cancer mortality: generalization of heterogeneity models. Math Popul Stud 4(2):133–147.

    Article  Google Scholar 

  • Manton K.G., Woodbury M.A., Tolley H.D., 1994. Statistical Applications using Fuzzy Sets. New York: John Wiley and Sons.

    MATH  Google Scholar 

  • Manton K.G., Corder L., Stallard E., 1997. Chronic disability trends in the U.S. elderly populations 1982 to 1994. Proc Natl Acad Sci USA 94:2593–2598.

    Article  Google Scholar 

  • Manton K.G., Akushevich I., Kulminski A., 2005. The Stochastic Linkage of Mortality Declines and Declines in Functional Disability. Invited paper for a meeting on “Projecting Mortality” at Brookings Institution, sponsored by the Office of Policy, Social Security Administration and the Center for Retirement Research at Boston College.

    Google Scholar 

  • Manton K.G., Gu X., Lamb V.L., 2006. Long term trends in life expectancy and active life expectancy in the United States. Popul Develop Rev 32(1):81–105.

    Article  Google Scholar 

  • Matis J.H., Wehrly T.E., 1979. Stochastic models of compartmental systems. Biometrics 35:199–220.

    Article  MATH  Google Scholar 

  • McGeough K., 2004. The Romans: New Perspectives. Oxford: ABC-CLIO. 381 pp.

    Google Scholar 

  • Metzler R., Klafter J., 2000. The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep 339:1–77.

    Article  MATH  MathSciNet  Google Scholar 

  • Preston S.H., Elo I.T., Rosenwaike I. et al., Hill M. 1996 African–American mortality at older ages: results of a matching study. Demography 33(2):193–209.

    Article  Google Scholar 

  • Risken H., 1996. The Fokker-Planck Equation: Methods of Solution and Applications, 2nd edition, Berlin: Springer-Verlag.

    MATH  Google Scholar 

  • Robine J-M., Jagger C., Mathers C.D. et al. (eds.), 2003a. Determining Health Expectancies. West Sussex, UK: John Wiley and Sons.

    Google Scholar 

  • Robine J-M., Romieu I., Michel J-P., 2003b. Trends in health expectancies. In: Robine J-M., Jagger C., Mathers C.D., Crimmins E.M., Suzman R.M. (eds.), Determining Health Expectancies. West Sussex, UK: John Wiley and Sons.

    Google Scholar 

  • Rosenwaike I., Stone L.F., 2003. Verification of the ages of super-centenarians in the United States: results of a matching study. Demography 40(4):727–739.

    Article  Google Scholar 

  • Simms H., 1942. The use of measurable causes of death (hemorrhage) for the evaluation of aging. J Gen Physiol 26:169–178.

    Article  Google Scholar 

  • Social Security Association, 2003. Life Tables for the United States Social Security Area 1900–2100. Actuarial Study No. 116. Distributed by Social Security Association.

    Google Scholar 

  • Society of Actuaries, 2000. RP-2000 Mortality Tables. In: http://www.soa.org/ccm/content/research-publications/experience-studies-tools/the-rp-2000-mortality-tables.

  • Strehler B., 1977. Time, Cells and Aging. New York: Academic Press.

    Google Scholar 

  • Sullivan D.F., 1971. A single index of mortality and morbidity. HSMHA Health Rep 86:347–354.

    Article  Google Scholar 

  • Tsiatis A., 1975. A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci USA 72(1):20–22.

    Article  MATH  MathSciNet  Google Scholar 

  • U.S. Bureau of the Census, 2001. 1999 LTC cross-sectional estimates: source and accuracy statement. Washington, DC: U.S. Census Bureau.

    Google Scholar 

  • Vaupel J.W., Manton K.G., Stallard E., 1979. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16:439–454.

    Article  Google Scholar 

  • Witteman J.C.M., Grobbee D.E., Valkenburg H.A. et al., 1994. J-shaped relation between change in diastolic blood pressure and aortic atherosclerosis, Lancet 343:504–507.

    Article  Google Scholar 

  • Wolf D., 2001. The role of microsimulation in longitudinal data analysis. Can Stud Popul 28:165–179.

    Google Scholar 

  • Wolter K.M., 1985. Introduction to variance estimation. New York: Springer-Verlag.

    MATH  Google Scholar 

  • Woodbury M.A., Manton K.G., Stallard E., 1981. Longitudinal models for chronic disease risk: an evaluation of logistic multiple regression and alternatives. Int J Epidemiol 10: 187–197.

    Article  Google Scholar 

  • Woodbury M.A., Manton K.G., 1977. A random walk model of human mortality and aging. Theor Popul Biol 11:37–48.

    Article  MathSciNet  Google Scholar 

  • Woodbury M.A., Manton K.G., 1983. A theoretical model of the physiological dynamics of circulatory disease in human populations. Hum Biol 55:417–441.

    Google Scholar 

  • Writing Group for the Women's Health Initiative Investigators, 2002. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women's Health Initiative randomized controlled trial. JAMA 288:321–333.

    Article  Google Scholar 

  • Yashin A.I., Manton K.G., Stallard E. (1986) Dependent competing risks: a stochastic process model. J Math Biol 24(2):119–40.

    Article  MATH  MathSciNet  Google Scholar 

  • Yashin A.I., Manton K.G., 1997. Effects of unobserved and partially observed covariate processes on system failure: a review of models and estimation strategies. Stat Sci 12(1):20–34.

    Article  MATH  MathSciNet  Google Scholar 

  • Yashin A.I., Iachine I.A., 1997. How frailty models can be used for evaluating longevity limits: taking advantage of an interdisciplinary approach. Demography 34(1):31–48.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Duke University, Durham, NC, USA

    K.G. Manton, Igor Akushevich & Julia Kravchenko

Authors
  1. K.G. Manton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Igor Akushevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julia Kravchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K.G. Manton .

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Manton, K., Akushevich, I., Kravchenko, J. (2009). Standard and Innovative Statistical Methods for Empirically Analyzing Cancer Morbidity and Mortality. In: Cancer Mortality and Morbidity Patterns in the U.S. Population. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78193-8_4

Download citation

Publish with us

Policies and ethics

Search

Navigation