gms | German Medical Science

49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI)
Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Schweizerische Gesellschaft für Medizinische Informatik (SGMI)

26. bis 30.09.2004, Innsbruck/Tirol

Comparison of Different Approaches to Calculate Remaining Life Expectancy in Decision Models

Meeting Abstract (gmds2004)

Suche in Medline nach

  • corresponding author presenting/speaker Uwe Siebert - Bavarian Public Health Research and Coordinating Center, Institute of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Deutschland
  • Annette Conrads-Frank - Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, USA

Kooperative Versorgung - Vernetzte Forschung - Ubiquitäre Information. 49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI) und Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI) der Österreichischen Computer Gesellschaft (OCG) und der Österreichischen Gesellschaft für Biomedizinische Technik (ÖGBMT). Innsbruck, 26.-30.09.2004. Düsseldorf, Köln: German Medical Science; 2004. Doc04gmds340

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2004/04gmds340.shtml

Veröffentlicht: 14. September 2004

© 2004 Siebert et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction

Remaining life expectancy (RLE) and quality-adjusted remaining life expectancy (QALE) are standard outcomes of decision-analytic Markov models, but their application to decision trees is less straightforward. We sought to explore and cross-validate different options of modeling RLE and QALE in decision trees.

Methods

In order to extend the short and fixed time horizon of a decision tree, RLE can be linked to the tree's final nodes. QALE is usually calculated by multiplying each year of the RLE with the utility, that represents the health-related quality of life of the patient in that year. Based on a simple hypothetical model with background mortality and disease-specific (additive and multiplicative) mortality (DSM) and utilities, we computed RLE as a function of age using two RLE approximation methods: (1) Gompertz parameterization (GP) [4] and (2) Declining Exponential Approximation of Life Expectancy (DEALE) [1], [2]. For both methods, background mortality was estimated from statistical life table data [6]. Results were compared to actuarial life table analysis representing the gold standard. Bias was defined as percentage deviation of the area under RLE curve (age 30-90). All analyses were performed separately for men and women. In the base-case analysis, no discounting was used and DSM was set to be twice the background mortality at age 30. Parameters and discount rates were varied in sensitivity analyses and analyses were repeated for the outcome QALE.

The following formulas for the Gompertz mortality model [5] and for the DEALE model [1] were used in our analysis. [Tab. 1]

Results

For men, both approximation methods underestimated RLE in all models. For additive DSM, the bias was -13% for GP and -2% for DEALE. For multiplicative DSM, bias was -5% for GP and -49% for DEALE. When varying DSM, bias was positively correlated with DSM, but bias direction (sign) and ranking of both methods did not change. Results for women and QALE showed similar patterns regarding magnitude and direction of bias. Results for male remaining life expectancy are shown in Figure 1 [Fig. 1].

Discussion

Based on our simple model, the Gompertz function should be preferred for multiplicative and the DEALE approach for additive models. The magnitude of the bias depends strongly on model parameters. Further research should be undertaken to extend our simulations to more complex models.


References

1.
Beck JR, Kassirer JP, Pauker SG. A convenient approximation of life expectancy (the "DEALE"). I. Validation of the method. American Journal of Medicine. 1982a; 73: 883-8.
2.
Beck JR, Pauker SG, Gottlieb JE, Klein K, Kassirer JP. A convenient approximation of life expectancy (the "DEALE"). II. Use in medical decision-making. American Journal of Medicine. 1982b; 73: 889-97.
3.
Durand-Zaleski I, Zaleski S. DEALE-ing and discounting: a simple way to compute the accrued costs of preventive strategies. Medical Decision Making. 1994; 14: 98-103.
4.
Gompertz B. On the nature of the function expressive of the law of human mortality. Philos. Trans. R. Soc. London 1825; 115: 513-585.
5.
Pollard JH. Fun with Gompertz. Genus 1991; 47: 1-20.
6.
Statistisches Bundesamt, 2002. Aktuelle Sterbetafeln fuer Deutschland 1999-2001. URL: http://www.destatis.de/download/d/bevoe/sterbetafel.xls