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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
Impact of Hypertension on Healthcare Costs Among Children
Todd P. Gilmer, PhD; Patrick J. O'Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD; and Karen L. Margolis, MD, MPH
Tracking Spending Among Commercially Insured Beneficiaries Using a Distributed Data Model
Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
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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
Contemporary Use of Dual Antiplatelet Therapy for Preventing Cardiovascular Events
Andrew M. Goldsweig, MD; Kimberly J. Reid, MS; Kensey Gosch, MS; Fengming Tang, MS; Margaret C. Fang, MD, MPH; Thomas M. Maddox, MD, MSc; Paul S. Chan, MD, MSc; David J. Cohen, MD, MSc; and Jersey Chen, MD, MPH
Potential Benefits of Increased Access to Doula Support During Childbirth
Katy B. Kozhimannil, PhD, MPA; Laura B. Attanasio, BA; Judy Jou, MPH; Lauren K. Joarnt; Pamela J. Johnson, PhD; and Dwenda K. Gjerdingen, MD
Synchronization of Coverage, Benefits, and Payment to Drive Innovation
Annemarie V. Wouters, PhD; and Nancy McGee, JD, DrPH
The Effect of Depression Treatment on Work Productivity
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
Economic Implications of Weight Change in Patients With Type 2 Diabetes Mellitus
Kelly Bell, MSPhr; Shreekant Parasuraman, PhD; Manan Shah, PhD; Aditya Raju, MS; John Graham, PharmD; Lois Lamerato, PhD; and Anna D'Souza, PhD
Optimizing Enrollment in Employer Health Programs: A Comparison of Enrollment Strategies in the Diabetes Health Plan
Lindsay B. Kimbro, MPP; Jinnan Li, MPH; Norman Turk, MS; Susan L. Ettner, PhD; Tannaz Moin, MD, MBA, MSHS; Carol M. Mangione, MD; and O. Kenrik Duru, MD, MSHS
Does CAC Testing Alter Downstream Treatment Patterns for Cardiovascular Disease?
Winnie Chia-hsuan Chi, MS; Gosia Sylwestrzak, MA; John Barron, PharmD; Barsam Kasravi, MD, MPH; Thomas Power, MD; and Rita Redberg MD, MSc
Effects of Multidisciplinary Team Care on Utilization of Emergency Care for Patients With Lung Cancer
Shun-Mu Wang, MHA; Pei-Tseng Kung, ScD; Yueh-Hsin Wang, MHA; Kuang-Hua Huang, PhD; and Wen-Chen Tsai, DrPH
Health Economic Analysis of Breast Cancer Index in Patients With ER+, LN- Breast Cancer
Gary Gustavsen, MS; Brock Schroeder, PhD; Patrick Kennedy, BE; Kristin Ciriello Pothier, MS; Mark G. Erlander, PhD; Catherine A. Schnabel, PhD; and Haythem Ali, MD

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.
Value-based insurance design (V-BID) has emerged as an approach to improve health outcomes and contain healthcare costs by encouraging use of high-value care. We estimated the impact of a V-BID for osteoporosis treatments using comparative effectiveness evidence and real-world data from a California health insurance plan to estimate the benefits of the design’s implementation.

This study consisted of 4 steps. First, we reviewed randomized clinical trials including osteoporosis treatments—alendronate, ibandronate, risedronate, raloxifene, and teriparatide—reported in a recent Agency for Health Research Quality systematic review. Second, we performed a network meta-analysis to synthesize data from the clinical trials and estimate the comparative effectiveness of included treatments. Third, we implemented a V-BID by removing co-payments for the most effective treatments. Fourth, using a Monte Carlo simulation, we estimated the impact of the V-BID in terms of fracture reduction and cost-savings.

Thirty-two randomized controlled trials were included in the network meta-analysis. We estimated that alendronate, risedronate, and teriparatide have the highest probability of being most effective across each fracture type—vertebral, hip, and nonvertebral/ nonhip. After eliminating co-payments, (ie, reducing them to zero), for these treatments, we estimated the health plan would experience a 7% (n = 287) decrease in fractures and an 8% ($6.8 million) decrease in costs.


Our study illustrates the benefits of comparative effectiveness evidence in V-BID development. We show that where clinical trials are lacking, network meta-analysis can provide valuable insights into the potential clinical and economic benefits of V-BID.

Am J Manag Care. 2014;20(8):641-648
In this study, we use network meta-analysis to generate comparative effectiveness evidence and rank osteoporosis treatments in order of efficacy. Using claims data from a large California health plan, we illustrate 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 (V-BID). This study:
  • Emphasizes the importance of taking an evidence-based approach to pharmacy benefit design.
  • Illustrates the potential value of network meta-analysis in the absence of appropriate clinical trial evidence.
  • Provides a framework for using evidence synthesis methods in V-BID.
It is estimated that more than 10 million Americans 50 years or older have osteoporosis and more than 43 million have low bone mass.1 A chronic condition characterized by low bone mass and deterioration of bone microarchitecture, osteoporosis increases patients’ risk of bone fracture. Most common fractures include those of the vertebrae, hip, and wrist, and result in substantial morbidity and medical and hospital costs. It is estimated that approximately 2.05 million osteoporosis-related fractures occur annually in the United States, costing about $16.9 billion.Osteoporosis management focuses on reducing fracture risk, and includes lifestyle modification (eg, smoking cessation and alcohol moderation), weight-bearing exercises, and treatment with pharmaceuticals. Various classes of pharmaceuticals are indicated for osteoporosis treatment, among them bisphosphonates (including alendronate, ibandronate, and risedronate), recombinant parathyroid hormone (teriparatide), selective estrogen receptor modulator (raloxifene), and monoclonal antibodies (denosumab). osteoporosis therapies—alendronate, ibandronate, risedronate, raloxifene, teriparatide—are generally available through a health plan’s pharmacy benefit.

Historically, drug co-payments and coinsurance have been largely based on drug cost, and did not take treatment benefit into account. However, this paradigm is changing, and a move toward value-based insurance design (V-BID) is gathering momentum.3-5 Essentially, the aim of V-BID is to improve healthcare and reduce costs by encouraging high-value care— care that offers clinical benefits at a reasonable cost—and discouraging low-value care. First proposed more than a decade ago, various value-based health insurance programs are now established and subject to much discussion and evaluation in the medical literature. V-BID has been applied to many indications, including diabetes, hyperlipidemia, and asthma.6-9   In contrast to existing approaches to V-BID in which copayments are reduced to encourage use of broad categories of high-value care (eg, statins for hypercholesterolemia or hyperglycemic medications for type 2 diabetes mellitus), we sought to use comparative effectiveness evidence to prioritize treatments within a drug class, using drugs that treat osteoporosis as a case study.7,10 However, as osteoporosis treatments have not been adequately compared against one another in head-to-head studies, we could not rely on clinical trial data to inform our approach, and therefore we used network meta-analysis to synthesize the necessary comparative evidence.

Network meta-analysis is a statistical approach to synthesizing comparative effectiveness evidence and is a generalization of meta-analysis, combining head-tohead clinical trial evidence with statistically inferred indirect comparisons across treatments not studied within head-to-head clinical studies.11,12 The approach requires a connected network of clinical trials. For example, in a data set consisting of pairwise comparisons (eg, A compared with B, and A compared with C), the relative efficacy can be inferred for those comparisons not studied directly (eg, in the previous example, B compared with C through common comparator A). Thus, through a combination of direct and indirect evidence, the network meta-analysis provides the relative efficacy of the whole network. Network meta-analysis has 2 principal roles: first, to strengthen inference of relative effectiveness between a pair of treatments through the combination of direct and indirect evidence; and second, to infer relative efficacies between treatments that have not been evaluated in a head-to-head study. Furthermore, the approach allows estimation of the probability that each included treatment is most effective, an important consideration for V-BID. Network meta-analysis methods are increasingly used and are promoted by health technology assessment agencies including the National Institute for Health and Clinical Excellence (NICE) and the Canadian Agency for Drugs and Technology in Health (CADTH).13-15

The objectives of this study were to construct a V-BID for osteoporosis treatments using comparative effectiveness evidence synthesized from a network meta-analysis; and to illustrate the potential of this approach through estimation of the number of avoided osteoporosis-related fractures and of cost savings using a simulation model. These estimates were specific to a large California-based health plan. We considered the osteoporosis treatments alendronate, ibandronate,  risedronate, raloxifene, and teriparatide in this  research, as these drugs are commonly available through drug formularies. We excluded the intravenously administered osteoporosis treatments denosumab and zoledronic acid, as they are not typically part of a tiered drug formulary and not subject to the same co-payment structure.


  We used claims obtained from a large California-based private health insurance plan. These data specified the number of patients with an osteoporosis diagnosis, and, for these patients, which one of the included treatments they received. We also used this plan data as the source of drug acquisition cost and patient co-payments. 

This study consisted of 4 steps. First, we identified clinical trials of osteoporosis treatments that were included in a 2012 Agency for Healthcare Research and Quality (AHRQ) systematic review. The trials that met our inclusion criteria became part of our study. Second, using these studies, we performed a network meta-analysis to synthesize the baseline fracture risk for vertebral, hip, and nonvertebral/nonhip fracture, and the comparative effectiveness of competing treatments. Third, we simulated a V-BID by adjusting the co-payments of the existing pharmacy benefit in accordance with the synthesized comparative effectiveness data, with co-payments eliminated, ie, reduced to zero, for the most effective treatments. Fourth, we simulated the impact of co-payment adjustment in terms of fracture reduction and cost savings for the health plan. The details of each step follow.

Step 1: Study Identification and Review. We relied on studies reported in the 2012 AHRQ report Treatment To Prevent Fractures in Men and Women With Low Bone Density or Osteoporosis: Update of a 2007 Report.16 Included studies were limited to those that: (i) included adults with low bone density or with osteoporosis; (ii) examined a pharmacological intervention reported within the private health insurance plan’s claims data; (iii) reported vertebral, hip, and/or total fractures; (iv) lasted a minimum of 6 months; and (v) were randomized controlled trials. Eligibility criteria of each of the included studies are listed in the eAppendix (available at Pairs of reviewers read each article to confirm reported counts for vertebral, hip, nonvertebral, and nonvertebral/nonhip fractures, and in some instances contacted the original authors for clarification. 

Step 2: Network Meta-Analysis. Using the extracted data, we performed a Bayesian network meta-analysis to estimate 2 pieces of information—underlying fracture risk, ie, the fracture risk in untreated patients, and comparative effectiveness of the various agents both to the reference treatment (placebo) and to one another.

We conducted the network meta-analysis using a random- effect, binomial logit-linked model implemented in WinBUGS 1.4. We used a binomial distribution because of the binomial nature of fractures, and the logit-link assumes a linearity of effects on the logit scale. We determined to use a random effects model after our inspection of the deviance information criterion showed no significant difference in goodness of fit between fixed and random effects models. The networks of studies for each end point (ie, vertebral fractures, hip fractures, and nonvertebral/nonhip fractures) shared the same structure, with individual studies comparing treatments to placebo, though the individual patient counts varied across the outcome networks. Characteristics of the studies populating these networks are provided in the eAppendix. For each end point, ie, vertebral, hip, and nonvertebral/ nonhip fractures, we estimated the underlying fracture risk from the placebo arms of each study. We estimated the comparative effectiveness of the various agents to one another (relative risk and 95% credible interval) indirectly through the reference treatment, ie, placebo. We additionally examined a synthetic end point—total fractures—as this provided a summary efficacy end point with which to rank treatments.

Step 3: Implementation of a Value-Based Insurance Design. In accordance with the results of the network metaanalysis, we modeled the effect of implementing a V-BID by eliminating co-payments for the most effective drugs, ie, alendronate, risedronate and teriparatide. We assumed that co-payment reduction would result in a proportion of patients shifting toward these treatments. How consumers respond to price is typically quantified using the elasticity of demand, which provides an estimate of how utilization of a product is influenced by price. For example, if a product’s price decreases 10% and the elasticity of demand is –0.5, then the population will consume 5% more of the product. Based on estimates from the literature, we simulated an elasticity of demand of –0.2 to –0.6 using a uniform distribution.17 Additionally, as a sensitivity analysis, we estimated the effects of decreasing utilization of the least effective drugs by 50% (range 10%-90%), and increasing utilization of the most effective drugs, accordingly. We used a 50% decrease in utilization of the least effective drugs as we considered it infeasible, in practice, to shift all patients.

Step 4: Estimation of Fracture Reduction and Cost-Savings. We used a simulation model to estimate the aggregate reduction in fractures and cost savings associated with implementing the V-BID using a 3-year time horizon. The model compares estimated fracture incidence and health plan costs of an existing distribution of osteoporosis treatments among a cohort of patients with an osteoporosis diagnosis covered by a large, private California health plan (n = 13,777) with an alternative distribution of treatments among this same cohort resulting from the V-BID implementation. We relied on the California health insurance plan claims data as the source of baseline drug utilization, annual drug cost, and annual patient drug co-pay, and on Shi et al (2009) for fracture cost (Table 1).18 Using a Monte Carlo simulation approach (Microsoft Excel via Visual Basic), we iterated the model 1000 times, each time using a different set of random values from each input parameter probability function (Table 1 and Table 2). Using this method, uncertainties in model inputs are propagated into uncertainties in model outputs, ie, mean estimate and 95% CI.


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