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Diagnostic tests for Alzheimer's disease: generating and evaluating evidence to inform insurance coverage policy
Diagnostic tests for Alzheimer's disease: generating and evaluating evidence to inform insurance coverage policy
- Collection:
- Health Policy and Services Research
- Author(s):
- Pearson, Steven D., author
Ollendorf, Daniel A., author
Colby, Jennifer A., author - Contributor(s):
- Institute for Clinical and Economic Review, issuing body.
- Publication:
- [Boston, MA] : Institute for Clinical and Economic Review, [2012]
- Language(s):
- English
- Format:
- Text
- Subject(s):
- Alzheimer Disease -- diagnosis
Diagnostic Tests, Routine -- methods
Evidence-Based Practice
Insurance Coverage
United States - Genre(s):
- Technical Report
- Abstract:
- Insurers and technology assessment groups look for evidence that can persuade them that diagnostic tests for AD improve patient outcomes. All the other potential uses for diagnostic tests, and in particular biomarkers, in drug development and clinical trial design are viewed as important by payers, but coverage determinations will be driven largely by whether insurers believe that there is adequate evidence to demonstrate that the use of a test will improve patient outcomes. Based on this perspective, the following targeted recommendations are intended to guide the development of further research that will help create a body of evidence adequate to meet all relevant evidentiary standards. These recommendations include those intended to frame the broader research agenda to establish on more solid ground our understanding of the relationship between diagnostic tests and the course of AD, as well as recommendations focused on clinical trial design of studies designed to measure the effectiveness of test-and-treat strategies for AD. Broad Research Agenda Recommendations. (1) In the current era of AD treatments of limited effectiveness, randomized controlled trials should be performed to evaluate diagnostic tests with potential overall net health benefits. (2) Develop a framework for assessing the social and economic impact of diagnosis of pre-clinical AD. (3) Looking to a future when there are more effective treatments for AD, continue to conduct biomarker studies in selected populations as well as in large population-based cohorts to evaluate the natural history of AD as well as the prognostic value of multiple combinations of neuropsychological testing and biomarkers. (4) Develop consensus standards for biomarker test deployment and interpretation. (5) As certain biomarkers gain validation for use as predictive of progression of disease, it will be important to study their predictive accuracy across the full spectrum of AD. (6) In studies that have used positive biomarker tests as inclusion criteria (enrichment design studies), include in baseline tests other potential biomarkers that can also be evaluated (nested marker-by-treatment-interaction studies). Ideally, always include additional test options that would be simpler, more accessible, and less expensive than the "gold standard" set of biomarkers used to qualify for inclusion. (7) Given that diagnostic testing for AD may involve expensive tests such as imaging, radionuclide tests, and CSF biomarkers, and that future therapies for AD may themselves be quite expensive, certainly on the cumulative, population-based level, evidence on comparative value should be included as a goal of the research agenda for AD diagnostics. (8) Given that many important clinical and economic outcomes occur years after diagnostic testing, a broad research agenda will benefit from the use of simulation modeling (decision analysis). Trial Design. (1) Design clinical trial protocols to enhance the generalizability of results to typical clinical practice. (2) Use a common set of consensus-based diagnostic test measurement thresholds and patient outcome measures. (3) Complement unusual enriched populations in early studies with studies that enroll representative patient populations in order to enhance the generalizability of results to real-world clinical practice. (4) Broaden the potential treatment population in trials. (5) For effective therapeutic agents developed through enrichment designs, consider further analyses to evaluate whether the original enrichment criteria were so narrow that less stringent enrichment criteria might identify many other patients who would benefit from treatment. (6) Retrospective assessment of a prognostic biomarker can only be done using data from well-conducted randomized controlled trials and with prospectively stated hypotheses, analysis techniques, and patient populations, with a pre-defined and standardized assay and scoring system for "positive" results. In other words: data mining should not be done to search retrospectively for combinations of clinical characteristics and biomarker results that are correlated with positive treatment outcomes. (7) Consider clinically-equivalent but lower-cost diagnostic strategies in translating trial results to clinical practice.
- Copyright:
- Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY license. (More information)
- Extent:
- 1 online resource (1 PDF file (69 pages))
- Illustrations:
- Illustrations
- NLM Unique ID:
- 101634790 (See catalog record)
- Permanent Link:
- http://resource.nlm.nih.gov/101634790