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Do Economic Evaluations of Targeted Therapy Provide Support for Decision Makers?
Ilia L. Ferrusi, PhD(c); Natasha B. Leighl, MD; Nathalie A. Kulin, MSc; and Deborah A. Marshall, PhD

Do Economic Evaluations of Targeted Therapy Provide Support for Decision Makers?

Ilia L. Ferrusi, PhD(c); Natasha B. Leighl, MD; Nathalie A. Kulin, MSc; and Deborah A. Marshall, PhD
Economic evaluations of adjuvant trastuzumab were reviewed. Three primary shortcomings were identified including incorporation of local data and estimation and representation (visual) of decision uncertainty.

 

This article was published as part of a special joint issue and also appears in the Journal of Oncology Practice.
 

Objective: Decision makers must make decisions without complete information. That uncertainty can be decreased when economic evaluations use local data and can be quantified by considering the variability of all model inputs concurrently per international evaluation guidelines. It is unclear how these recommendations have been implemented in evaluations of targeted cancer therapy. By using economic evaluations of adjuvant trastuzumab, we have assessed the extent to which decision support recommendations were adopted.

 

Study Design: Systematic review.

 

Methods: Published economic evaluations of adjuvant trastuzumab treatment in early-stage breast cancer were examined as an established example of targeted therapy. Canadian, United Kingdom, and US economic evaluation guidelines were reviewed to establish extraction criteria. Extraction characterized the use of effectiveness evidence and local data sources for model parameters, sensitivity analysis methods (scenario, univariate, multivariate, and probabilistic), and uncertainty representation (ie, cost-effectiveness plane, scatterplot, confidence ellipses, tornado diagrams, cost-effectiveness acceptability curve).

 

Results: Fifteen economic evaluations of adjuvant trastuzumab were identified in the literature. Local data were used to estimate costs (15 of 15) and utilities rarely (2 of 15) but not trastuzumab efficacy. Univariate sensitivity analysis was most common (12 of 15), whereas probabilistic analysis was less frequent (10 of 15). Two-thirds of allstudies provided visual representation of results and decision uncertainty.

 

Conclusion: Authors of adjuvant trastuzumab economic evaluations rarely use local data beyond costs. Quantification of uncertainty and its representation also fell short of guideline recommendations. This review demonstrates that economic evaluations of adjuvant trastuzumab, as an example of targeted cancer therapy, can be improved for decision-making support.

 

(Am J Manag Care. 2011;17(5 Spec No.):SP61-SP70)

Economic evaluations of adjuvant trastuzumab, as an example of targeted therapy, can better support informed decision making through increased use of local data to inform model parameters and quantification and graphic communication of decision uncertainty. Data reflecting local practice is rarely used to inform model parameters beyond costs.

  • Joint analysis of parameter uncertainty using probabilistic sensitivity analysis was reported in two-thirds of reviewed studies.
  • Graphics aimed at communicating the results of economic analysis and sensitivity analysis were provided in two-thirds of all studies.
  • These methods are supported by several international guidelines.
Economic evaluation is a tool used by policy and decision makers to address the relationship between clinical effects and costs associated with diagnosis, treatment, adverse effects, supportive healthcare, and life gained or lost. Payers, providers, and physicians can use economic evaluations to inform drug formulary listing, procedure or device reimbursement, and patient care decisions.1-8 Decision analytic models have provided valuable support for health policy decisions ever since the Centers for Disease Control first presented such evidence to support vaccine recommendations in the late 1960s.2 More recently, these methods are being applied to targeted drug therapies.

Targeted therapies, or personalized medicines, allow physicians to tailor treatment to individual patients. These medicines exert their effect by specifically targeting biologic processes via gene or protein expression9 and, though costly, can potentially offer substantial clinical and economic offsets by avoiding ineffectual treatment and  minimizing adverse effects. Therefore, decision analytic modeling and economic evaluation of targeted therapies are powerful tools with which clinical efficacy and costs can be weighed against standard care. Nonetheless, care must be taken to ensure that analyses are conducted in a manner that supports informed healthcare decision making. Many countries have outlined explicit economic evaluation guidelines to encourage appropriate conduct for decision-making purposes. To date, it is unclear how closely researchers have followed guidelines. Understanding how economic evaluations of targeted therapies are designed to inform decision making could enhance

the health policy and managed-care environments.

In this article, we examine how economic analyses of targeted therapy were conducted with a focus on informing healthcare decisions from the payer’s perspective. Given its widespread uptake and considerable success in the treatment of breast cancer, trastuzumab (Herceptin; Genentech, South San Francisco, CA) was chosen for  assessment. Two decades of clinical study and application have facilitated several economic evaluations of the drug and this systematic review examines those  evaluations to understand whether analyses of targeted therapy were reported in a manner that supports informed healthcare decision making. We used economic evaluation guidelines from Canada, the United Kingdom, and the United States to establish decision support criteria. Our review focuses on recommendations specifically designed to aid the decisionmaking process by increasing the relevance of the economic evaluation to the decision maker’s setting and encouraging quantification and representation of decision uncertainty.

METHODS

Systematic Search Strategy and Study Selection

A search strategy was previously developed10 to identify published, peer-reviewed economic analyses of trastuzumab in the adjuvant treatment of breast cancer. The search encompassed literature published through October 2008 that were indexed in Biosis, Cochrane, the Centre for Reviews and Dissemination, EconLit, EMBASE, the Health Economic Evaluations Database, MEDLINE, and PubMed electronic databases; we updated EMBASE and MEDLINE searches to February 2011. Only English language citations were considered. Economic evaluations were included if they represented original research; considered 2 or more alternatives in an incremental of cost-effectiveness, cost-utility, cost-benefit, or cost-minimization; and focused on the evaluation of trastuzumab therapy in the adjuvant setting. Abstracts were reviewed independently by 2 assessors, and relevant articles were obtained in full for additional review. Selection of studies on the basis of reviews of the full articles was conducted by a single reviewer, and a random sample was verified independently.

Data Extraction

We reviewed Canadian,11 United Kingdom,12 and US13 national drug or drug and device economic evaluation guidelines to identify recommendations for increasing the relevance of the analysis to the decision maker’s setting and quantification and representation of decision uncertainty. The items identified from each guideline were then extracted from included studies. The items selected for abstraction are listed in Table 1 along with the relevant guidance from each country. We excluded the recommendation to model local standard care and practice patterns because of the difficulty in identifying and verifying local patterns across international treatment settings and language barriers.

Data was extracted to a single form for data input and decision uncertainty. Here, we use decision uncertainty to represent our understanding of the likelihood that the result predicted by an economic evaluation will occur in practice. To understand how authors made each evaluation relevant to the decision maker’s setting, we extracted the source for the following parameters and categorized the source as “local data” or “literature”: human epidermal growth factor receptor 2 (HER2) test properties, trastuzumab efficacy, risk of recurrence or survival, cost, and utility estimates. For an item to be considered local data, the model parameter needed to be derived from actual practice in  the jurisdiction of the evaluation or measured from the disease population of that jurisdiction. For example, health state utilities used in an economic evaluation in the  United States were considered local if the utilities were measured from a US population of patients with the disease of interest.

Quantification and representation of decision uncertainty was also documented. We extracted parameter type (stochastic [point estimate selected at random from a distribution] or deterministic [single point estimate]) to gain an understanding of the approach used to represent the “best guess” estimate of any variable considered in the evaluation. The methods of assessing uncertainty in those parameters and assumptions (termed sensitivity analysis) were subsequently extracted. Use of univariate, multivariate, scenario, or probabilistic sensitivity analysis was noted, including which parameters were assessed by each method. It was crucial to distinguish the methods of sensitivity analysis, as each serves a different purpose. Univariate analysis involves changing a single parameter estimate at a time to understand how that parameter influences results.14 Multivariate or scenario analysis involves changing multiple variables simultaneously, usually to represent some alternative set of circumstances, to  understand the impact on results.14 Univariate and multivariate analysis most frequently employ deterministic parameters. Finally, probabilistic analysis involves assigning distributions to model parameters (stochastic) and allowing each to vary randomly and concurrently to generate an empirical distribution for the cost-effectiveness ratio.14 We also documented whether visual representation of results and uncertainty was provided and the type of graphic used to represent that uncertainty (collectively termed decision aids). In this context, provision of decision aids was defined as clear graphic presentation of the cost-effectiveness plane with a scatterplot or confidence ellipses or of univariate sensitivity analysis results or cost-effectiveness acceptability curves (CEACs) with tornado diagrams per the reviewed guidelines. Results presented on the cost-effectiveness plane as a scatterplot or with confidence ellipses give the reader a sense of the distribution of incremental cost-effectiveness ratio (ICER) results. The CEAC shows the probability that a given intervention is more cost-effective than its comparator(s) over a range of willingness- to-pay values, providing the decision maker with an estimate of the likelihood that choosing to adopt the intervention would in fact be the right choice.15 We also considered value of information (VOI) analysis, because this method was suggested by both Canadian and United Kingdom guidelines.11,12 Moreover, VOI relates the decision uncertainty of the model or specific parameters to the cost of conducting additional research to decrease that uncertainty16 and therefore provides information to support decision making.

RESULTS

Search Results

The updated MEDLINE and EMBASE searches returned an additional 385 citations to the 958 citations previously identified. Duplicate citations accounted for 224 of the total, which left 1119 for review. Abstract review resulted in the exclusion of an additional 694 citations. A total of 15 studies remained after application of the inclusion criteria during full citation review. The Figure summarizes the study identification and selection process. The 2006 National Institute for Health and Clinical Excellence (NICE) report on the use of trastuzumab in early-stage breast cancer was included with the additional extraction of data from the related manufacturer’s submission, which was available from the NICE Web site.17 Several conference abstracts were identified but not included in the review because complete peer-reviewed articles were not available.18-24

A brief synopsis of economic evaluation methods, settings, and findings of the reviewed articles is presented in 2009 US dollars in Table 2. Overall, trastuzumab therapy was associated with an ICER deemed cost-effective in early-stage breast cancer by the majority study authors.10 Additional studies identified in this updated review are generally consistent with that finding. However, Skedgel et al27 noted that the cost-effectiveness of adjuvant trastuzumab in Canada could exceed the widely cited $50,000 per quality-adjusted life-year and $100,000 per quality-adjusted life-year thresholds and that this finding was largely dependent on the assumed duration of trastuzumab benefit. Indeed, 10 of 15 studies in the United States and international settings noted sensitivity to the assumed duration of trastuzumab benefit (typically 5 years) or the relative risk reduction associated with therapy. This suggests that follow-up on the long-term benefits of trastuzumab and the relative benefit of 52-week therapy compared with 9-week therapy will be crucial to understanding its cost-effectiveness in the adjuvant setting. Most authors did not consider local willingness-to-pay thresholds when concluding the cost-effectiveness of trastuzumab. The choice of testing strategy significantly impacted that ICER when test properties were modeled in conjunction with treatment. Some analyses suggested that a 9-week trastuzumab regimen28 could result in potential cost savings compared with 52-week therapy29 but that additional long-term data were needed. The results of several studies were sensitive to the cost of trastuzumab therapy.

Relevance to the Decision Maker’s Setting

 
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