Multi-state Markov models in cancer screening evaluation: a brief review and case study.
This work presents a brief overview of Markov models in cancer screening evaluation and focuses on two specific models. A three-state model was first proposed to estimate jointly the sensitivity of the screening procedure and the average duration in the preclinical phase, i.e. the period when the cancer is asymptomatic but detectable by screening. A five-state model, incorporating lymph node involvement as a prognostic factor, was later proposed combined with a survival analysis to predict the mortality reduction associated with screening. The strengths and limitations of these two models are illustrated using data from French breast cancer service screening programmes. The three-state model is a useful frame but parameter estimates should be interpreted with caution. They are highly correlated and depend heavily on the parametric assumptions of the model. Our results pointed out a serious limitation to the five-state model, due to implicit assumptions which are not always verified. Although it may still be useful, there is a need for more flexible models. Over-diagnosis is an important issue for both models and induces bias in parameter estimates. It can be addressed by adding a non-progressive state, but this may provide an uncertain estimation of over-diagnosis. When the primary goal is to avoid bias, rather than to estimate over-diagnosis, it may be more appropriate to correct for over-diagnosis assuming different levels in a sensitivity analysis. This would be particularly relevant in a perspective of mortality reduction estimation.
Département des Maladies Chroniques et des Traumatismes, Institut de veille sanitaire, St-Maurice, France. z.uhry@invs.sante.fr
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Uhry
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Hédelin
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Colonna
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Asselain
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Arveux
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Rogel
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Exbrayat
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Guldenfels
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Courtial
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Soler-Michel
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Molinié
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Eilstein
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Duffy
D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical