The Value of a Patient-Level Modeling Approach and Need for Better Reporting in Economic Evaluations of Osteoporosis

DISCLOSURES
No funding was involved in the writing of this letter. Outside of the submitted work, Hiligsmann has received research grants through institution from Amgen, Radius Health, UCB, and Teva/Theramex. Reginster has received research grants and/or consulting fees from Servier, Novartis, Negma, Lilly, Wyeth, Amgen, GlaxoSmithKline, Roche, Merckle, Nycomed-Takeda, NPS, IBSA Genevrier, Theramex, UCB, Asahi Kasei, Endocyte, Merck Sharp and Dohme, Rottapharm, Teijin, Teva, Analis, NovoNordisk, Ebewee Pharma, Zodiac, Danone, Will Pharma, Meda, Bristol Myers Squibb, Pfizer, Organon, Therabel, Boehringer, Chiltern, and Galapagos. Silverman has received grant support from Amgen, Radius, and Lilly; consulting fees from Amgen and Radius; has served on scientific advisory boards for Lilly and Amgen; and has served on speakers bureaus for Amgen, Lilly, and Radius.


■■ The Value of a Patient-Level Modeling Approach and Need for Better Reporting in Economic Evaluations of Osteoporosis
The article "Patient-Level Modeling Approach Using Discrete-Event Simulation: A Cost-Effectiveness Study of Current Treatment Guidelines for Women with Postmenopausal Osteoporosis" by Quang A. Le, 1 which was published in the October 2019 issue of JMCP, raised some important points of consideration, especially with regard to our recent recommendations for economic evaluations in osteoporosis that resulted from an ESCEO-IOF working group with a U.S. predominant perspective. 2 First, we recognize the value of a patient-level approach to simulate osteoporosis events that could address some of the limitations of cohort Markov models lacking comprehensive memory management. Patient-level modeling by the use of a discrete-event simulation or microsimulation Markov model that provides similar advantages could better accommodate the natural history of patients with osteoporosis. 3 In the development of our recommendations, experts highlighted the importance of avoiding a hierarchy of fractures in economic modeling and restrictions after fracture events. In addition, in the Le study, 1 we appreciate the presentation of various patient characteristics and clinical profiles. Our guidelines also recommend the conduct of multiple scenarios according to patients' characteristics, such as age and fracture risk.
On the other hand, the Le study has some potential limitations with respect to our guidelines. First, the model lacked a lifetime horizon consideration. A shorter time horizon (such as 10 years 1 ) limits the benefits of effective drugs. For example, the sequential therapy abaloparatide/alendronate was shown to be dominant (more effects for less costs) compared with no treatment using a lifetime model horizon, while the cost per quality-adjusted life-years was estimated at $62,861 using a 10-year time horizon. 4 Second, in the selection of osteoporosis treatments, Le omitted sequential therapies. There is evidence that now supports the concept of sequential therapy with the initiation of anabolic therapy first followed by an antiresorptive to improve health outcomes in osteoporosis, 5 and sequential therapies should thus be considered as relevant alternative options. Third, some model data were not appropriately reported regarding treatment side effects, treatment duration, effect of medication adherence, and treatments effect after discontinuation. Recent studies have suggested rapid bone loss and increased risk of multiple vertebral fractures after denosumab, but this does not seem to have been included. 6,7 Fourth, we would have liked to see more sensitivity analyses on key model parameters (such as treatment effect after discontinuation, time horizon) and probabilistic sensitivity analyses presented in cost-effectiveness acceptability curves.
All these reasons limit our ability to make a judgment about the reliability of Le's results. Although we do not question the comparable medicines are precluded by differences in the design, data, and measurement aspects of trials. Regulators may have missed an opportunity to create research standards that enable such comparisons. These advanced methods are therefore akin to a supercar without a suitable test track-nice to look at but impossible to drive.

■■ The Authors Respond
We appreciate the interest in our recent study and the opportunity to respond to the critiques by Hiligsmann, Reginster, and Silverman (henceforth HiRS). 1 Before we respond to comments raised by HiRS, we wanted to acknowledge that our paper was originally written as a full-length research paper for the PhRMA Foundation Award; however, it was submitted to JMCP as a Viewpoints article with 2 other papers that also won a PhRMA Foundation Award. As a result, some details of our discrete-event simulation (DES) model and sensitivity analysis were shortened but remained concise to accommodate the journal restrictions in terms of number of tables and figures, as well as total number of words allowed.
First, HiRS highlighted a lack of lifetime-horizon consideration in our DES model. It is important to note that our study examined monotherapy options for women with postmenopausal osteoporosis (PMO) based on the current treatment guidelines. 2,3 Within the treatment options, the longest duration of treatment was 5 years, recommended for oral bisphosphonate (alendronate). An assumed linear, gradual offset of fracture-reduction benefits (treatment effects) would occur over the subsequent 5 years after treatment discontinuation. 4-6 Therefore, it was reasonable, clinically and economically, to use a 10-year time horizon rather than a lifetime horizon where treatment effects were unrealistically overestimated beyond the possible time horizon. Furthermore, we carefully examined the economic evaluation study of sequential therapy with abaloparatide/alendronate (ABL/ALN) as an illustration of its advocacy of lifetime horizon. 7 In the study, HiRS made an exaggerated assumption where treatment effect of ABL value of patient-value modeling, we would recommend that policymakers interpret study conclusions with caution. In addition, we urge researchers to be more transparent regarding model assumptions and data and to conduct extensive univariate and probability sensitivity analyses. By providing recommendations and minimum criteria for an economic evaluation in osteoporosis and an osteoporosis-specific checklist for reporting, our recent guidelines could help to improve the reporting, quality, and reliability of economic evaluations. 2