gms | German Medical Science

50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie (dae)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Deutsche Arbeitsgemeinschaft für Epidemiologie

12. bis 15.09.2005, Freiburg im Breisgau

Evaluating the Pharmacogenomics Bias in Decision-Analytic Modeling

Meeting Abstract

  • Uwe Siebert - Harvard Medical School, Boston, USA
  • Sue J. Goldie - Harvard Medical School, Boston, USA
  • Milton C. Weinstein - Harvard School of Public Health, Boston, USA
  • Karen M. Kuntz - Harvard School of Public Health, Boston, USA

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Deutsche Arbeitsgemeinschaft für Epidemiologie. 50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie. Freiburg im Breisgau, 12.-15.09.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc05gmds564

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2005/05gmds291.shtml

Veröffentlicht: 8. September 2005

© 2005 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 vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction and Research Question

Decision analyses of the impact of drug treatments on chronic diseases require modeling the progression of disease and treatment response beyond the time horizon of clinical trials. In many such models, progression and drug effect have been applied uniformly to all patients; potential heterogeneity in progression (HP) and heterogeneity in treatment efficacy (HT), including pharmacogenomic effects, have been largely ignored. We sought to evaluate the direction and relative magnitude of a pharmacogenomics bias resulting from failure to adjust for genetic heterogeneity in both treatment efficacy and progression of disease.

Material and Methods

We constructed two simple Markov models with three basic health states (early-stage disease, late-stage disease, dead), one adjusting for genetic heterogeneity and the other not adjusting for it. Adjustment was done by creating different disease states for presence (G+) and absence (G–) of a dichotomous genetic factor. We compared the life expectancy gains attributable to treatment resulting from both models and defined pharmacogenomics bias as percent deviation of treatment-related life expectancy gains in the unadjusted model (UAM) from those in the adjusted model (AM). We calculated the bias as a function of underlying model parameters to create generic results. We then applied our model to lipid-lowering therapy with pravastatin in patients with coronary atherosclerosis, incorporating the influence of two TaqIB polymorphism variants (B1 and B2) on progression and drug efficacy as reported in the DNA substudy of the Regression Growth Evaluation Statin Study (REGRESS) trial [1], [2].

Results

Our generic simulation showed that a purely HT-related bias is mostly negative (underestimates life expectancy gains) and a purely HP-related is positive (overestimates life expectancy gains). For many typical scenarios, the absolute bias is smaller than 10%. In case of joint HP and HT, the overall bias is likely triggered by the HP component and reaches positive values >100% if fractions of "fast progressors" and "strong treatment responders" are low. In the pravastatin example, the UAM overestimated the true life-years gained (LYG) by 5.5% in 56-year-old men.

Conclusions

We have been able to predict the pharmacogenomics bias jointly caused by HP and HT as a function of characteristics of patients, disease, and treatment. When both types of heterogeneity are present, models ignoring this heterogeneity may generate results that overestimate the treatment benefit.


References

1.
Jukema JW, Bruschke AV, van Boven AJ, Reiber JH, Bal ET, Zwinderman AH, Jansen H, Boerma GJ, van Rappard FM, Lie KI. (1995). Effects of lipid lowering by pravastatin on progression and regression of coronary artery disease in symptomatic men with normal to moderately elevated serum cholesterol levels. The Regression Growth Evaluation Statin Study (REGRESS). Circulation 91(10): 2528-40.
2.
Kuivenhoven JA, Jukema JW, Zwinderman AH, de Knijff P, McPherson R, Bruschke AV, Lie KI, Kastelein JJ (1998). The role of a common variant of the cholesteryl ester transfer protein gene in the progression of coronary atherosclerosis. The Regression Growth Evaluation Statin Study Group. New England Journal of Medicine 338(2): 86-93.