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Do Intraindividual Variation in Disease Progression and the Ensuing Tight Window of Opportunity Affect Estimation of Screening Benefits?

Author

Listed:
  • Hendrik Koffijberg

    (Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, h.koffijberg@umcutrecht.nl)

  • Gabriel Rinkel

    (Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands)

  • Erik Buskens

    (Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands)

Abstract

Background . The effects of variation in disease progression between individuals on the effectiveness of screening have been assessed extensively in the literature. For several diseases, progression may also vary within individuals over time. The authors study the effects of intraindividual variation and the combined effects of inter- and intraindividual variation in disease progression on the effectiveness of screening. Methods . The authors investigated the risk reduction of aneurysmal subarachnoid hemorrhage (SAH) achieved by screening for intracranial aneurysms in a simulation study as a function of the inter- and intraindividual variation in the risk of aneurysm rupture. They also extended a previously constructed Markov model for the cost-effectiveness analysis of screening for new aneurysms in patients with clipped aneurysms after SAH. A time-varying risk of aneurysm rupture was introduced, and the influence of this variation on cost-effectiveness was assessed. Results . The risk reduction provided by screening decreased with increasing intraindividual variation in disease progression. The expected number of prevented instances of SAH was overestimated by 58% in this simulation study when high degrees of inter- and intraindividual variation were present. Interindividual variation alone resulted in up to 33% overestimation and intraindividual variation in up to 43% overestimation. In the extended Markov model, screening benefits were overestimated by 24% when a high degree of intraindividual variation was present but ignored. Conclusions . If intraindividual variation in disease progression is ignored in decision models, subsequent cost-effectiveness analyses of screening strategies will overestimate the benefits provided by screening. This bias is comparable to, but partially independent of, the bias caused by ignoring interindividual heterogeneity.

Suggested Citation

  • Hendrik Koffijberg & Gabriel Rinkel & Erik Buskens, 2009. "Do Intraindividual Variation in Disease Progression and the Ensuing Tight Window of Opportunity Affect Estimation of Screening Benefits?," Medical Decision Making, , vol. 29(1), pages 82-90, January.
    • Handle: RePEc:sae:medema:v:29:y:2009:i:1:p:82-90
    DOI: 10.1177/0272989X08322012
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    1. R. Henderson & P. Oman, 1999. "Effect of frailty on marginal regression estimates in survival analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 367-379, April.
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