Developing Economic Models for Assessing the Cost-Effectiveness of Multiple Diagnostic Tests: Methods and Applications

Background

Clinical pathways with multiple diagnostic tests are complex to model, but problematic and simplistic approaches are often used in economic evaluations.

Methods

We analyzed statistical methods of handling multiple diagnostic tests and provided guidance on applying these methods in economic modeling. We first introduced a statistical model to quantify the correlations between 2 tests and how those correlations can be incorporated within an economic model. We also presented the general form of conditional dependence among multiple tests. We then introduced net reclassification improvement (NRI), a measure that evaluates the added value of a new risk factor (e.g., biomarker) for risk prediction. We further provided 2 examples to illustrate the application of these methods.

Results

Our first example illustrated how to model an add-on test to an existing test, in the absence of a perfect reference standard. After accounting for the imperfect nature of both tests and the conditional dependence between tests, the potential health benefits from the additional test were reduced. This led to differential cost-effectiveness results when comparing models using the perfect test and conditional independence assumptions. The second example illustrated how to evaluate the added value of a new risk factor using the NRI measure. Using the new risk classification provides greater precision in risk prediction, and in the example, the strategy using the new risk classification with treatment for selected individuals led to more favorable cost-effectiveness results.

Conclusions

These innovative methods for handling multiple diagnostic tests have improved the methodology within the field and should be adopted to provide more accurate estimates within cost-effectiveness analyses.

Highlights

Economic evaluations of multiple diagnostic tests often apply problematic simplistic approaches, such as ignoring conditional dependence between 2 tests or assuming a perfect final test in the diagnostic pathway. We provided guidance on how to apply improved methods for economic modeling. We introduced methods to model conditional dependence between 2 imperfect tests. We used an example to illustrate how assumptions about perfect diagnostic test accuracy and conditional independence between tests affect cost-effectiveness. Compared with the results of the area under the receiver-operating-characteristic curve, net reclassification improvement has distinct advantages in measuring the added value of a new risk factor for model-based economic evaluation. Economic evaluations that appropriately account for the complexities of diagnostic test pathways can help decision makers ensure efficient use of resources.

Cost-effectiveness of genome-wide sequencing for unexplained developmental disabilities and multiple congenital anomalies

PURPOSE

Genetic testing is routine practice for individuals with unexplained developmental disabilities and multiple congenital anomalies. However, current testing pathways can be costly and time consuming, and the diagnostic yield low. Genome-wide sequencing, including exome sequencing (ES) and genome sequencing (GS), can improve diagnosis, but at a higher cost. This study aimed to assess the cost-effectiveness of genome-wide sequencing in Ontario, Canada.

METHODS

A cost-effectiveness analysis was conducted using a discrete event simulation from a public payer perspective. Six strategies involving ES or GS were compared. Outcomes reported were direct medical costs, number of molecular diagnoses, number of positive findings, and number of active treatment changes.

RESULTS

If ES was used as a second-tier test (after the current first-tier, chromosomal microarray, fails to provide a diagnosis), it would be less costly and more effective than standard testing (CAN$6357 [95% CI: 6179-6520] vs. CAN$8783 per patient [95% CI: 2309-31,123]). If ES was used after standard testing, it would cost an additional CAN$15,228 to identify the genetic diagnosis for one additional patient compared with standard testing. The results remained robust when parameters and assumptions were varied.

CONCLUSION

ES would likely be cost-saving if used earlier in the diagnostic pathway.