Original articles
Performing Cost-Effectiveness Analysis by Integrating Randomized Trial Data with a Comprehensive Decision Model: Application to Treatment of Acute Ischemic Stroke

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Abstract

A recent national panel on cost-effectiveness in health and medicine has recommended that cost-effectiveness analysis (CEA) of randomized controlled trials (RCTs) should reflect the effect of treatments on long-term outcomes. Because the follow-up period of RCTs tends to be relatively short, long-term implications of treatments must be assessed using other sources. We used a comprehensive simulation model of the natural history of stroke to estimate long-term outcomes after a hypothetical RCT of an acute stroke treatment. The RCT generates estimates of short-term quality-adjusted survival and cost and also the pattern of disability at the conclusion of follow-up. The simulation model incorporates the effect of disability on long-term outcomes, thus supporting a comprehensive CEA. Treatments that produce relatively modest improvements in the pattern of outcomes after ischemic stroke are likely to be cost-effective. This conclusion was robust to modifying the assumptions underlying the analysis. More effective treatments in the acute phase immediately following stroke would generate significant public health benefits, even if these treatments have a high price and result in relatively small reductions in disability. Simulation-based modeling can provide the critical link between a treatment’s short-term effects and its long-term implications and can thus support comprehensive CEA.

Introduction

Stroke is the third leading cause of death in the United States and is the leading cause of disability among adults [1]. Approximately 500,000 Americans suffer a stroke each year; of these, 150,000 die and another 150,000 suffer severe disability [1]. The economic cost of stroke exceeds $30 billion per year [2].

New therapies (e.g., thrombolytics and neuroprotective agents) show considerable promise in reducing death and disability due to acute ischemic stroke [3]. Randomized controlled trials (RCTs) are the “gold standard” for demonstrating the efficacy of therapies, and therapies for acute stroke treatment are no exception. Randomized controlled trials of acute stroke treatments typically have a fixed duration, with the maximum possible follow-up time ranging from a few weeks to a few years. Certainly, it is impractical to follow up the patients in a RCT of an acute stroke treatment until all the participants have died.

Once a treatment is determined to be efficacious, attention shifts to considerations of cost-effectiveness. This is typically operationalized using an incremental cost effectiveness ratio (ICER) comparing the new treatment with a less efficacious yet less costly alternative. To be complete, cost-effectiveness analysis (CEA) must not only consider short-term costs and benefits (e.g., as are observed during an actual RCT) but must also assess longer-term outcomes as well [4]. The assessment of long-term outcomes requires somehow linking information from RCTs with information from other data sources.

The methodology of decision analysis is ideally suited to provide the link between short- and long-term outcomes. As part of the Patient Outcomes Research Team (PORT) for the prevention of stroke [5], we developed a comprehensive simulation model of the natural history of stroke (the Stroke Prevention Policy Model [SPPM]) 6, 7, 8. The SPPM is noteworthy in that it has the ability to use and integrate the best available evidence—including premier epidemiologic studies, intervention trials, claims databases, and patient interviews—in order to simulate long-term outcomes after stroke.

This article describes the application of the SPPM to produce a comprehensive CEA for a hypothetical new agent used to treat acute ischemic stroke. In particular, we perform a “thought experiment,” whereby the efficacy of this new agent for treatment of acute ischemic stroke has been studied in a RCT with a follow-up period extending for 6 months per patient. From the hypothetical trial, we can calculate quality-adjusted survival and costs (during the first 6 months of follow-up) for both the intervention and usual care groups. We also observe disability level for each patient at the conclusion of the trial. Suppose that, apart from any effects during the 6 months of follow-up, the intervention produces a change in this pattern of disability levels. The key question becomes: “What are the long-term implications of these differences in disability?”

The SPPM may be used to answer this question. In particular, we enhanced the basic SPPM to incorporate the effect of disability at 6 months on long-term outcomes, and then used this enhanced SPPM to perform a simulation-based analysis. The methods illustrated here not only provide information that may help to determine the economic value of new stroke treatments but also provide an example of a general methodology for integrating simulation modeling with CEA.

Section snippets

Stroke Prevention Policy Model—Basic Structure

The basic SPPM is described in more detail elsewhere 6, 7, 8. Briefly, the SPPM is a semi-Markov simulation model for studying the costs and outcomes associated with the “natural history” of stroke in comparison to the outcomes expected with one or more preventive or therapeutic interventions. The SPPM operates by simulating the natural history of a large number of patients. Each patient is followed for relevant health events (transient ischemic attack, ischemic stroke, hemorrhagic stroke,

Expert Panel

As a preliminary exercise, we asked panelists to estimate the amount of time required for patients to achieve a stable Rankin score (i.e., “steady state”). Panelists believed that although recovery of higher-level function could continue for 12 or more months after stroke, the steady state for the Rankin score would typically occur within 3–6 months. Time until steady state depends on initial level of disability, with patients with initial Rankin scores of 2 or less usually reaching a plateau

Discussion

We found that treatments that produce even subtle improvements in the pattern of outcomes after ischemic stroke are likely to be cost-effective, the primary reasons being that (1) highly disabled persons have high per diem costs (e.g., especially for nursing homes, and caregivers), and (2) as the period of follow-up increases, even small differences in short-term mortality and morbidity can translate into sizable long-term differences in outcomes. Our sensitivity analyses indicated that these

Acknowledgements

Funding for this project was provided by G.D. Searle and Company. We acknowledge the contributions of the expert panel: Jack Whisnant, MD, Bruce Coull, MD, Pamela Duncan, PhD, PT, Ralph Sacco, MD, and Byron Hamilton, MD, PhD; John Bian, MS, and Victor Hasselblad, PhD, for their comments; and the leaders of the Framingham Project, in particular, Philip Wolf, MD, and Ralph D’Agostino, PhD, for their willingness to reanalyze their data to fit the format of the SPPM.

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