gms | German Medical Science

54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

07. bis 10.09.2009, Essen

Fertility biomarkers predict differential survival in a long-term follow up study of male infertility. Capturing and treating heterogeneity

Meeting Abstract

  • Ronny Westerman - Philipps-Universität Marburg, Marburg
  • Kathrina Pyka - Philipps-Universität, Marburg
  • Hanna Seydel - Philipps-Universität, Marburg
  • Walter Krause - Philipps-Universität, Marburg
  • Ulrich Mueller - Philipps-University, Marburg

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Essen, 07.-10.09.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09gmds125

doi: 10.3205/09gmds125, urn:nbn:de:0183-09gmds1254

Published: September 2, 2009

© 2009 Westerman et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

Introduction: In this paper we want to prove the effect of heterogeneity in order to defective survival curves for fertile ans sufertile men. The intention of our research isn’t only focused on testing the effect of heterogeneity more over we want to implicate indicators for evaluating the intensities. A convenient way to demonstrate the intensity of heterogeneity on the population hazards could be done by using Frailty Models.

Methods: The use of a gamma distribution for the frailty variation have two essen-tial advantages: First the frailty distribution of the survivors at any given age follows a gamma distribution specified with the same shape parameter but a different scale parameter, second the frailty distribution among person dying at any age is also gamma distributed with an in-creasing shape parameter by one and a scale parameter, as a function for the age of death. The gamma distribution is one of the most flexible statistical distributions and can be used alternatively for any other distributions.

Data set: The data set includes all infertility patients who had attended the fertility and sterility office of the department of andrology at Marburg University Hospital for semen analysis between 1949 and 1998 who were born before 1942. Until now we have analyzed more than 2.000 medical records. After excluding cases for diseases which were identified that might be effect fertility, we have to distinguish between two subgroups.

Results and conclusions: In our analysis the intensity of unobserved heterogeneity is very low but therefore one have to consider for the estimations. It has to be mention that age-specific effects and within-group correlation in cohorts can be assert as the major reason for the disparity for azoospermic and oligospermic men and might be better predictors for the population hazards.


References

1.
Aalen OO. Heterogeneity in survival analysis. Statistics in Medicine. 1988;7:1121-37.
2.
Abramowitz M, Stegun IA. Handbook of Mathematical Functions. Dover, New York; 1972.
3.
Clevens MA, Gould WM, Gutierrez RG, Marchenko YU. An Introduction to Survival Analysis Using Stata. 2nd Ed. Stata Press Publication StataCorp LP; 2008.
4.
Dorn K. Einflussfaktoren von Seiten des Mannes auf den Erfolg einer in-vitro-Fertilisation mit intracytoplasmatischer Spermieninjektion (ICSI). Klinik für Urologie und Kinderurologie der Universität Ulm. 2007.
5.
Hougaard P. Survival models for heterogeneous populations derived from stable dis-tributions. Biometrika. 1986;73:387-96.
6.
Hougaard P. A class of multivariate failure time distributions. Biometrika. 1986;73:671-8.
7.
Hougaard P. Analysis of Multivariate Survival Data: Statistics for Biology and Health. New York: Springer; 2000.
8.
Ghosh SK, Ghosal S. Semiparametric Accelerated Failure Time Models for Censored Data, Department of Statistics. North Carolina State University Raleigh. 2006.
9.
Groos S. Lebenszeit-Mortalität von Männern mit normalen und subnormalen Spermienkonzentrationen. Marburg: Philipps-Universität Marburg; 2006.
10.
Küpker W, Schwinger E, Mennicke K, Hiort O, Bals-Pratsch M, Ludwig M, Schlegel PN, Diedrich K. Genetik der männlichen Infertilität. Gynäkologe. 2007;33:79-87.
11.
Manton KG, Stallard E, Vaupel JW. Methods for Comparing the Mortality of Heterogeneous Populations. Demography. 1986;18:389-410.
12.
McGilchrist, CA, Aisbett CW. Regression with frailty in survival analysis. Biometrics. 1991;47:221-5.
13.
Semenchenko GV, Yashin AI, Johnson TE, Cypser JW. New Modells for Survival Analysis of Experimental Data. In: Agente JL. Advances in Statistical Methods for the Health Sciences, Application to Cancer and AIDS studies, Genome Sequence Analysis and Survival Analysis. Boston: Birkhäuser; 2007. p. 179-91.
14.
Silber SJ. The Genetics of male infertility. Evolution of the the X and Y chromosome and transmission of male fertility to future generations. Infertility Center of St. Louis, MO. 2003.
15.
Rothman KJ, Greenland S. Modern Epidemiology. 2nd Ed. Philadelphia, Pa: Lippincott-Raven; 1998.
16.
Turek P. The genetics of male infertility: The Turek Clinic, Men’s Reproductive Health Specialists. 2008.
17.
Vaupel JW, Manton K, Stallard E. The impact of heterogeneity in individual frailty on dynamics of mortality. Demography. 1979;16:439-54.
18.
Wienke A, Lichtenstein P, Czene K, Yashin AI. The role of correlated Frailty Models in studies of Human Health, Ageing and Longevity. In: Agente JL. Advances in Statistical Methods for the Health Sciences, Application to Cancer and AIDS studies, Genome Sequence Analysis and Survival Analysis. Boston: Birkhäuser; 2007. p. 151-61.
19.
Wieke A. Frailty Models in Survival Analysis [Habilitation]. Halle-Wittenberg: Medizinische Fakultät der Martin-Luther-Universität; 2007.
20.
World Health Organisation. WHO laboratory manual for the examination of human semen ans spermcervical mucus interaction. 4 ed. Cambridge: Cambridge University Press; 1999.
21.
Yashin AI, Arbeey KG, Akushevich I, Kulminski A, Akushevich L, Ukraintseva SV. Model of hidden heterogeneity in longitudinal data.Theor Biology. 2008;73(1):1-10.