Using the net benefit regression framework to construct costeffectiveness acceptability curves: an example using data from a trial of external loop recorders versus Holter monitoring for ambulatory monitoring of "community acquired" syncope
 Jeffrey S Hoch^{1, 2}Email author,
 Marie Antoinette Rockx^{3} and
 Andrew D Krahn^{3}
DOI: 10.1186/14726963668
© Hoch et al; licensee BioMed Central Ltd. 2006
Received: 18 February 2006
Accepted: 06 June 2006
Published: 06 June 2006
Abstract
Background
Costeffectiveness acceptability curves (CEACs) describe the probability that a new treatment or intervention is costeffective. The net benefit regression framework (NBRF) allows costeffectiveness analysis to be done in a simple regression framework. The objective of the paper is to illustrate how net benefit regression can be used to construct a CEAC.
Methods
One hundred patients referred for ambulatory monitoring with syncope or presyncope were randomized to a onemonth external loop recorder (n = 49) or 48hour Holter monitor (n = 51). The primary endpoint was symptomrhythm correlation during monitoring. Direct costs were calculated based on the 2003 Ontario Health Insurance Plan (OHIP) fee schedule combined with hospital case costing of labour, materials, service and overhead costs for diagnostic testing and related equipment.
Results
In the loop recorder group, 63.27% of patients (31/49) had symptom recurrence and successful activation, compared to 23.53% in the Holter group (12/51). The cost in US dollars for loop recording was $648.50 and $212.92 for Holter monitoring. The incremental costeffectiveness ratio (ICER) of the loop recorder was $1,096 per extra successful diagnosis. The probability that the loop recorder was costeffective compared to the Holter monitor was estimated using net benefit regression and plotted on a CEAC. In a sensitivity analysis, bootstrapping was used to examine the effect of distributional assumptions.
Conclusion
The NBRF is straightforward to use and interpret. The resulting uncertainty surrounding the regression coefficient relates to the CEAC. When the link from the regression's pvalue to the probability of costeffectiveness is tentative, bootstrapping may be used.
Background
Out patient ambulatory monitoring is often performed in patients with syncope (e.g., fainting or passing out) that present in the primary care setting to diagnose or exclude an arrhythmia, a potentially serious etiology [1–6]. This shortterm monitoring device may take the form of an external loop recorder or a Holter monitor. The purpose of monitoring is to obtain a symptomrhythm correlation during the monitored period (i.e., to have the monitoring device actively record a patient experiencing symptoms). Several studies have reported the diagnostic yield of the two monitoring modalities, suggesting a higher yield from the longer duration of monitoring provided by a loop recorder [3, 7–12]. One recent randomized trial confirmed the higher diagnostic yield [5]. There is a lack of data about the cost of investigation of syncope presenting in the community. Referred and hospitalized patients are known to generate costs estimated between $3,000 and $25,000 dollars [13–19]. After a primary diagnostic trial [5], we sought to establish the cost of investigation of "communityacquired" syncope and to evaluate the costeffectiveness of the two monitoring strategies in a prospective randomized trial [20].
A new health care treatment, intervention or technology is costeffective if (1) the extra cost of (2) an extra unit of effect is less than (3) the decision maker's willingness to pay for it. A costeffectiveness analysis (CEA) can report (1) and (2), representing two of the three pieces of information necessary to determine costeffectiveness. Specifically, an incremental costeffectiveness ratio (ICER) is the ratio of extra cost to extra effect (i.e., ΔC/ΔE). Thus, a CEA generates an estimate of the extra cost for an additional unit of effect, but the merit of the tradeoff is typically a matter of opinion. In other words, the data are generally silent on whether the extra effect is worth the extra cost. For example, a new drug for multiple sclerosis may provide an extra quality adjusted life year (QALY) for £35,000. The new drug is costeffective if the decision maker is willing to pay £35,000 or more for an extra QALY. Thus the verdict of costeffectiveness depends upon the decision maker's willingness to pay (λ), a value not known from the cost and effect data. There is additional uncertainty beyond the fact that λ is unknown. The uncertainty comes from the fact that the sample ICER is a statistical estimate. For example, if the true ICER is £30,000 per QALY, the ICER estimate could be more or less due to sampling variability. In fact, the multiple sclerosis drug with the ICER estimate of £35,000 per QALY could have a true ICER of £30,000 per QALY. It would be a mistake to conclude there is no chance that the drug is costeffective if λ = £31,000, for example.
The costeffectiveness acceptability curve (CEAC) elegantly handles both uncertainty problems. This paper, building on recent work by Fenwick and colleagues [21], illustrates how to use the net benefit regression framework (NBRF) [22] to construct a CEAC. After a brief summary of relevant statistical concepts, this paper uses clinical trial data from a recently published CEA comparing external loop recorders with Holter monitors for ambulatory monitoring of syncope.
Methods
One hundred patients referred for ambulatory monitoring with syncope or presyncope (hereafter described as syncope) were randomized to a onemonth external loop recorder (n = 49) or 48hour Holter monitor (n = 51). Patients provided written informed consent, and the protocol was approved by the University of Western Ontario Ethics Review Board. The primary endpoint was symptomrhythm correlation during monitoring. Direct costs in Canadian dollars were calculated from the Ministry of Health's perspective based on the 2003 Ontario Health Insurance Plan (OHIP) fee schedule for professional fees and on hospital case costing data for the calculation of labour, materials, service and overhead for diagnostic testing and related equipment. Costs were converted to US Dollars using a conversion rate converted on July 20th, 2005 of ($1 USD = $1.21543 CAD) [20].
Loop recorders were both more costly and more effective than Holter monitors. For the loop recorder, the cost in US dollars was $648.50 and for the Holter monitor $212.92 [20]. The extra cost of $435.58 for the loop recorder was accompanied by a 39.74% increase of success while monitoring (in the loop recorder group 31 of 49 or 63.27% of patients had symptom recurrence and successful activation, compared to 12 of 51 or 23.53% in the Holter group). The ICER estimate was $1096 per additional diagnosis. The CEAC finds purchase here as there is uncertainty about the maximum a decision maker would pay for an additional diagnosis coupled with the statistical variability inherent in trial data. As an alternative to the method illustrated by Fenwick and colleagues [21], we use the NBRF to show how to construct the CEAC.
The CEAC has been advocated for summarizing the results of a CEA because it highlights the relationship between the assessment of costeffectiveness and the unknown λ [23–27]. As originally described, the CEAC originates from a Bayesian context; however, the CEAC can be given a frequentist interpretation. For a given λ, the CEAC is equal to one minus the onesided significance level for testing the null hypothesis that the "new treatment" is not costeffective (i.e., the additional benefits are outweighed by the additional costs) [25, 28]. Under this frequentist framework, the CEAC can be viewed as illustrating a decision rule for rejecting the null hypothesis that the intervention is not costeffective.
Alternatively, the CEAC can be interpreted in a 'Bayesian' fashion [23, 24] as: the probability that an individual, with a set of prior beliefs about the costeffectiveness of the new treatment, now believes the new treatment to be costeffective (i.e., the additional benefits outweigh the additional costs). While a Bayesian approach provides a welljustified interpretation for a CEAC, it presents other dilemmas. For example, there exist many 'Bayesian' CEACs – namely one for every set of prior beliefs – with no criteria for choosing between them. This is important because every CEAC is 'correct' for its given prior. Thus, the calculation of a Bayesian CEAC requires the specification of the prior distribution of the costeffectiveness data before the data were collected. Typically as a reference case scenario, it is common and convenient to use a 'noninformative' prior which allows the data to overwhelm prior beliefs. However, except in the simplest of examples there is no agreement about the definition of a reference prior distribution and many socalled noninformative priors are not noninformative at all (see section 5.5.1 of [29]). When using a 'noninformative' prior with the NBRF (in this case assuming there is no reason to modify the results of the data analysis), the Bayesian mechanics work in the background and formal derivation of the posterior distribution can be avoided. In other words, one can run a net benefit regression and use the resulting parts to illustrate the probability that a new treatment or intervention is costeffective (NB: The pvalue itself does not provide an estimate of the probability of costeffectiveness when there is prior information. This is a fundamental distinction between the interpretation of a pvalue and a posterior probability [30]. For a more comprehensive discussion about the use of genuine prior information in costeffectiveness analyses readers are referred to [31–33]).
The NBRF was introduced to facilitate the use of regression tools in economic evaluation [22]. Net benefit regression uses as the dependent variable, net benefit nb _{ i }= λ·effect _{ i } cost _{ i }from personlevel effect (effect _{ i }) and cost (cost _{ i }) data (as a matter of preference, the analyst may use net health benefits [34] instead of net monetary benefits [35]). When ordinary least squares (OLS) is used to estimate the simple linear regression
nb _{ i }= β_{0} + β_{1} TX + ε
where TX is a "new treatment" indicator variable (e.g., TX = 1 if the patient received a loop recorder and TX = 0 if the patient received a Holter monitor), the coefficient estimate of β_{1}, call this b_{1}, equals the difference in mean nb for the loop and Holter groups. It can be shown [22] that when this difference is greater than zero (i.e., when the loop group has greater mean net benefits than the Holter group), then ΔC/ΔE < λ. In other words, if b_{1} > 0, then the loop recorder is costeffective relative to the Holter monitor (or the incremental net benefit is positive). The statistical uncertainty involving the cost and effect data is expressed in the pvalue for b_{1}. The pvalue for b_{1} can be used to make the yaxis of the CEAC [22, 25]; however, caution must be exercised in two regards.
Results
Each study participant who received a loop recorder incurred costs of $648.50 and 31 of the 49 (63.27%) had symptom recurrence and successful activation. In comparison, the Holter monitors cost $212.92 for each study participant and only 12 of the 51 (23.53%) experienced a successful outcome. The NBRF was implemented by estimating with OLS the regression
nb _{ i }= β_{0} + β_{1} LOOP + ε
Construction of the dependent variable (net benefit) when λ = $1000
Net Benefit with λ = $1000  Number of Subjects  Treatment Group  Successful outcome 

$1000 * 1  $648.50 = $351.50  31  Loop recorder  Yes 
$1000 * 0  $648.50 =  $648.50  18  Loop recorder  No 
$1000 * 1  $212.92 = $787.08  12  Holter monitor  Yes 
$1000 * 0  $212.92 =  $212.92  39  Holter monitor  No 
Simple net benefit regression estimates (N = 100)^{a}
Explanatory variables  λ = $500^{b} coefficient (pvalue)  λ = $1000 coefficient (pvalue)  λ = $1500 coefficient (pvalue)  λ = $2000 coefficient (pvalue)  λ = $2500 coefficient (pvalue) 

Constant term  95.27 (0.004)  22.37 (0.728)  140.02 (0.149)  257.67 (0.047)  375.32 (0.021) 
LOOP  236.90 (<0.001)  38.22 (0.678)  160.46 (0.246)  359.14 (0.053)  557.82 (0.017) 
Rsquared  0.2143  0.0018  0.0137  0.0377  0.0570 
Using the net benefit regression results to create a costeffectiveness acceptability curve (CEAC) with a comparison to bootstrapping the probability of costeffectiveness
λ  Treatment Indicator Coefficient  One sided pvalue  Probability of costeffectiveness (regression)  Probability of costeffectiveness (bootstrapping)  

Estimate  pvalue  
$500  236.90  <0.001  ≈ 0.000  0%  0% 
$750  137.56  0.048  0.024  2%  2% 
$1000  38.22  0.678  0.339  34%  33% 
$1250  61.12  0.595  0.298  70%  71% 
$1500  160.46  0.246  0.123  88%  89% 
$1750  259.80  0.108  0.054  95%  94% 
$2000  359.14  0.053  0.027  97%  97% 
$2250  458.48  0.028  0.014  99%  98% 
$2500  557.82  0.017  0.009  99%  99% 
$2750  657.16  0.011  0.006  99%  99% 
$3000  756.50  0.007  0.004  100%  100% 
Discussion
A CEAC indicates a 50% chance of costeffectiveness when λ equals the sample estimate of the ICER [26]. The ICER for the loop recorder was $1,096 per extra successful diagnosis. Table 3 shows that when λ is within $500 of the ICER estimate, the probability of costeffectiveness is quite sensitive. For example, at λ = $500, the probability that loop recorders are costeffective is 0%, but at λ = $1500 it is approximately 88%. Figure 2 illustrates this, as the most dramatic gains in the height of the curve (from 0% to 88%) occur between λ = $500 and $1500. Alternatively, the curve is mostly flat for λ < $ 500 and λ > $1500. While we may never know the real value of λ, if it is assumed to be near the low range of the costs generated by referred and hospitalized patients (e.g., $3000 dollars), there appears to be a good chance that loop recorders are costeffective.
As reflected in the last two columns of Table 3, the probability of costeffectiveness calculated using the pvalue was nearly identical to that calculated using the bootstrapping method. This finding may be related to the fact that the cost data in this trial did not vary by patient within treatment group. All patients receiving a loop recorder had costs of $648.50 and all patients receiving a Holter monitor had costs of $212.92. When both patient level costs and effect data vary, net benefit regression can still be used to construct a CEAC (i.e., the statistical uncertainty involving the cost and effect data is expressed in the pvalue). However, the assumptions necessary to use the pvalue may not hold; for example, the presence of skewness or heteroskedasticity in the data suggests caution when using the pvalue. Indeed, for low values of λ, the almost inevitable nonnormal distribution of costs can challenge the assumptions made in using the pvalue in the regression approach. For this reason, empirical examples of the NBRF typically use bootstrapping to generate CEACs [36–38]. However, as noted by a reviewer, the bootstrap is not necessarily robust, particularly in CEAs when there is also concern about the use of parametric methods because of skewness. In addition to the incremental net benefit (β_{1}), net benefit regression provides an estimate of the mean net benefit of "usual care" (β_{0}), the mean net benefit of "new treatment" (β_{0} + β_{1}) and also regression diagnostic information (e.g., the residual errors and R^{2}). Thus, the NBRF facilitates using regression diagnostics (see the "Regression Diagnostics" section and Figure 6 in [22]) to improve the quality of economic evaluations.
Conclusion
The NBRF provides a way for economic evaluations to use the variety of tools that have been developed for regression. For any value of λ, net benefit regression produces a costeffectiveness estimate, and the CEAC produces a costeffectiveness probability. To allow for the fact that the analyst does not know the decision maker's λ, the horizontal axis of a CEAC varies in the style of a sensitivity analysis, and the statistical uncertainty about costeffectiveness is reflected on the vertical axis. This paper has illustrated how the NBRF can be used to construct a CEAC. When the link from a net benefit regression's pvalue to the probability of costeffectiveness is tentative, bootstrapping provides an alternative.
Abbreviations
 CEACs:

costeffectiveness acceptability curves
 ICER:

incrememtnal costeffectiveness ratio
 NBRF:

net benefit regression framework
 OHIP:

Ontario Health Insurance Plan
 OLS:

ordinary least squares
 QALY:

quality adjusted life year
Declarations
Acknowledgements
The authors would like to thank George J. Klein, Raymond Yee, Allan C. Skanes and Lorne J. Gula for their assistance with the initial patient study. Dr. Hoch gratefully acknowledges funding from a Career Scientist Award from the Ontario Ministry of Health and Long Term Care. The Centre for Research on Inner City Health is sponsored by the Ontario Ministry of Health and LongTerm Care. The opinions, results, and conclusions are those of the authors and no endorsement by the ministry is intended or should be inferred.
Authors’ Affiliations
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