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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
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Potential Role of Network Meta-Analysis in Value-Based Insurance Design
James D. Chambers, PhD, MPharm, MSc; Aaron Winn, MPP; Yue Zhong, MD, PhD; Natalia Olchanski, MS; and Michael J. Cangelosi, MA, MPH
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Arne Beck, PhD; A. Lauren Crain, PhD; Leif I. Solberg, MD; Jürgen Unützer, MD, MPH; Michael V. Maciosek, PhD; Robin R. Whitebird, PhD, MSW; and Rebecca C. Rossom, MD, MSCR
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Potential Role of Network Meta-Analysis in Value-Based Insurance Design

James D. Chambers, PhD, MPharm, MSc; Aaron Winn, MPP; Yue Zhong, MD, PhD; Natalia Olchanski, MS; and Michael J. Cangelosi, MA, MPH
This study illustrates that where clinical trials are lacking, network meta-analysis can provide valuable insights into the potential clinical and economic benefits of value-based insurance design.
  Thirty-two studies met our inclusion criteria and were included in the network meta-analysis, including 8 randomized, placebo-controlled trials pertaining to alendronate, 4 to ibandronate, 3 to raloxifene, 13 to risedronate, and 4 to teriparatide (details of each study provided in the eAppendix, available at www.ajmc.com). Results from the network meta-analyses are shown in Table 2. For vertebral, hip, and nonvertebral/nonhip fractures, alendronate, risedronate, and teriparatide were consistently estimated to have the 3 highest probabilities of being the most effective treatment, although rank order varied by end point. Teriparatide and risedronate had the highest probabilities of being the best treatment for vertebral and nonvertebral/nonhip fractures with alendronate ranked third. Alendronate was ranked second to risedronate for hip fracture. To confirm efficacy order and provide an aggregate efficacy end point, we constructed the synthetic end point of total fractures. Consistent with the other end points, alendronate, risedronate, and teriparatide were estimated to have the highest probability of being the most effective. Across each end point, ibandronate and raloxifene were consistently estimated to have the lowest probability of being most effective. Consistent with these findings, we constructed a V-BID by eliminating copayments for alendronate, risedronate, and teriparatide, while maintaining the existing co-payment for ibandronate and raloxifene.

We estimated that plan beneficiaries receiving treatments under the current benefit structure would suffer an estimated 3668 total fractures and incur roughly $72.9 million in osteoporosis-related costs, with $67.8 million related to fracture costs (Table 3). Implementing the V-BID resulted in an estimated 3381 total fractures and costs of approximately $66.1 million, with $60.9 million related to fracture costs. Therefore, compared with the status quo, the V-BID was estimated to result in 287 fewer fractures (a 7% reduction) while reducing health plan costs by approximately $6.8 million (an 8% reduction). Percentage reduction in costs was greater than the percentage reduction in fractures as the majority of avoided fractures were of the hip (221, 77% of total fractures), the fracture type associated with the highest average cost ($26,545). Indeed, hip fracture reduction accounted for the majority of cost savings, approximately $6.2 million (approximately 90% of total fracture- related cost saving). Implementing the V-BID reduced co-payment revenue by approximately $113,000 (7%), but this was more than offset by cost savings resulting from reduced hospitalizations of approximately $6.9 million (8%), and reduced drug costs of approximately $63,000 (1%).

We simulated a scenario in which 50% (range 10% to 90%) of individuals using the least effective treatments were shifted to the most effective treatments. In this scenario, the number of total fractures was reduced by 8.5% (range 1.8% to 15.5%; corresponding to a 10% and 90% shift, respectively) and total costs for the plan were reduced by 9.0% (range 1.9% to 16.0%).

DISCUSSION

  Osteoporosis is a burdensome disease that will become increasingly prevalent as the population ages.2 Alongside lifestyle changes, pharmaceuticals are an integral part of disease management. This study builds on a growing body of literature showing how V-BID can lead to improved drug adherence, health outcomes, and cost savings.6,19,20 We took a novel approach to constructing a V-BID formulary and estimating potential health benefits and cost savings using real-world data from a major Californiabased private health plan. Our approach incorporated available randomized clinical trial evidence reported in an AHRQ systematic review, and used established statistical approaches to develop a V-BID. We estimated that alendronate, risedronate, and teriparatide were the most efficacious treatments, with ibandronate and raloxifene performing unfavorably across the considered end points. These findings are consistent with other network metaanalyses evaluating these treatments.21,22 We estimated that implementation of the V-BID would avoid 287 fractures (7%), including 19 vertebral fractures, 221 hip fractures, and 47 nonvertebral nonhip fractures, and would result in $6.8 million (8%) in cost savings for 1 California-based health plan. However, as our approach is associated with inherent uncertainties, and assumptions regarding adherence and therapeutic substitution, study results should be considered illustrative. Nevertheless, our findings support a burgeoning body of literature showing that V-BIDs can lead to both clinical and economic benefits.

Limitations. One limitation of our study is that it does not account for drug adherence, an important aspect of care for which V-BIDs have been shown to have a positive impact.6,19,20 Another limitation is that we based our V-BID solely on efficacy data, which is only 1 input into prescribing decisions, and did not account for other factors that play a role, including side effect profile and drug interactions. Patient and physician preferences also likely play a role. For example, unlike the other considered orally administered treatments, teriparatide is administered via subcutaneous injection, which may affect patient treatment preference.23 In addition, the FDA-approved labels for teriparatide and raloxifene include a black box warning for potential increase in the incidence of osteosarcoma, and increased risk of venous thromboembolism and death from stroke, respectively, which may also affect patient and physician choice.1,23 Further, we did not consider whether physicians account for treatment cost in prescribing decisions; evidence as to whether physicians account for the cost of technology, or patients’ ability to pay, in prescribing decisions is conflicting.24-26 These limitations may have affected estimated health gain and cost savings.

Moreover, network meta-analyses do not possess the validity of head-to-head clinical trials and it is important to recognize the approach’s limitations. By extrapolating the available data, network meta-analysis makes indirect comparisons between treatments not subject to head-to head clinical trials. As is the case for traditional meta-analysis, because randomization only holds within and not across the clinical trials included in a network meta-analysis, there is a risk of consistency violations, ie, that patients included in different comparisons are dissimilar. Therefore, a firm conclusion can be best drawn when a network meta-analysis is restricted to well-conducted, adequately powered randomized trials including similar patient populations.

These caveats notwithstanding, evidence suggests that results from well-conducted network meta-analyses are typically consistent with those from head-to-head randomized clinical trials.27 Further, while adequately powered prospective randomized clinical trials comparing all competing treatments provide the most robust information, they are infeasible in practice. Network meta-analysis approaches likely will become increasingly recognized and accepted as the source of this evidence, since they allow decision makers to immediately compare treatments as new evidence and treatments are produced.

We did not include osteoporosis treatments administered in an outpatient setting (eg, denosumab and zoledronic acid), because they are typically not part of the pharmacy benefit. While including outpatient drugs in the analysis would more comprehensively account for osteoporosis therapeutic options, the study would need to be redesigned and expanded beyond the scope of a pharmacy benefit.

Osteoporosis was particularly amenable to this study as fracture occurrence is the predominant cause of disease- related morbidity and costs. Minimizing fractures leads to unambiguous health benefits for patients, and the avoidance of costly hospitalizations for the health plan. While this approach could be in theory extended to other indications, complex chronic diseases with multiple health states would require a more elaborate disease model and simulation approach.

CONCLUSIONS
Our approach illustrates the potential of using existing systematic review evidence and established evidence synthesis techniques for V-BID. It is a novel evidencebased approach that relies on comparative effectivenes evidence, and allows all available clinical trial evidence to be accounted for. While pharmacy benefit design should be informed by well-structured randomized controlled trials when available, we show that applying properly done network meta-analysis can provide insights where trials alone are lacking, and a realistic means of simulating the clinical and economic benefits of V-BID.


Acknowledgments

The authors are grateful to Peter J. Neumann, ScD, for his helpful comments on an earlier draft of this manuscript.

Author Affiliations: All authors are from The Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, except Michael J. Cangelosi, who is now with Boston Scientific.

Funding Source: This research was based in part on support from the California Health Care Foundation.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JDC, AW, NO, MJC); acquisition of data (JDC, AW, YZ, NO, MJC); analysis and interpretation of data (JDC, AW, YZ); drafting of the manuscript (JDC, AW, YZ, MJC); critical revision of the manuscript for important intellectual content (JDC, AW, YZ, NO, MJC); statistical analysis (JDC, AW, YZ, MJC); administrative, technical, or logistic support (YZ, MJC); and supervision (JDC).

Address correspondence to: James D. Chambers, PhD, MPharm, MSc, assistant professor, The Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington St, Boston, MA 02111. E-mail: JChambers@tuftsmedicalcenter.org.
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