PMC:4636534 / 15971-18788
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4636534","sourcedb":"PMC","sourceid":"4636534","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4636534","text":"Indirect effects\nIndirect effects of a diagnostic test depend on the consequences of the test result. Both health status and costs related to this health status are characteristics of the patient and diagnostic tests do not directly change these parameters. Diagnostic tests guide the management of patients. Assuming optimal circumstantial factors, a “perfect” diagnostic test will theoretically result in the optimal management. One may assume that the optimal management leads to the best health status (though not necessarily to lowest costs). “Imperfect” diagnostic tests will in some cases lead to suboptimal management. The effect of diagnostic tests on outcome parameters lies, therefore, only in the imperfectness of the test, usually presented as sensitivity and specificity, or positive and negative predictive values. Indirect psychological effects are gaining more attention and consist of the positive or negative psychological effects of the diagnostic information of the test result on a patient’s view on his or her health [32–34].\nOutcome parameters for health status are generally represented as quality-adjusted life years (QALYs), although other outcome parameters might be used for specific study objectives (e.g., life-years saved). QALYs are derived from the general life expectancy estimates of the target population and disutilities (i.e., reduction in quality of life) related to the disease of interest, the treatment, and the diagnostic test. The use of QALYs allows for the relative importance of false positive or negative results of a diagnostic test (reflected in sensitivity and specificity) to be weighed. Costs are calculated from the allocated treatment, medication, hospitalization, etc. Furthermore, costs can be transferred from known societal costs of specific health conditions that might apply. Table 1 provides a schematic overview of model input parameters for the example of different follow-up imaging strategies in curatively treated NSCLC [6].\nTable 1 Schematic example of model input parameters\nModel Parameter Mean SE/SD/range Distribution Source\nProbabilities p\n p Progressive Disease 0.85 fixed *\nDiagnostics\nPET-CT\n p imaging test true positive (Sensitivity) * * beta *\n p imaging test true negative (Specificity) * * beta *\nCT\n p imaging test true positive (Sensitivity) * * beta *\n p imaging test true negative (Specificity) * * beta *\nX-Ray\n p imaging test true positive (Sensitivity) * * beta *\n p imaging test true negative (Specificity) * * beta *\nCosts c (€)\nDiagnostics\n c PET-CT whole body 1.364 € fixed [35],*\n c CT chest 204 € fixed [35],*\n c X-Ray chest 39 € fixed [35],*\nTreatment\n c * * * * *\nUtilities (u)\n u No disease 0.68 0.1 beta [1],*\n u Progression, detected * * beta [1],*\n u Progression, undetected * * beta [1],*\n u Dead 0.00 * fixed 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