Ezgi Alhun

Joshua Cohen

Peter Neumann

Center for the Evaluation 

of Value and Risk in Health

Institute for Clinical Research

and Health Policy Studies

New England Medical Center

T–NEMC #063

750 Washington Street

Boston, MA 02111

MedPAC

601 New Jersey Avenue, NW

Suite 9000

Washington, DC 20001

(202) 220-3700

Fax: (202) 220-3759

www.medpac.gov

•

•

The views expressed in this report

are those of the authors.

No endorsement by MedPAC

is intended or should be inferred.

Review and Analysis

of Cost-Effectiveness

Analyses for Two

Medicare-Covered

Services

A study conducted by staff from the Institute

for Clinical Research and Health Policy Studies 

at the New England Medical Center for the

Medicare Payment Advisory Commission

June 2006 • No. 06–2
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Review and Analysis of Cost-Effectiveness Analyses for Two Medicare-Covered Services 

Conducted by 

Ezgi Alhun 

Joshua Cohen 

Peter Neumann 

Center for the Evaluation of Value and Risk 

Institute for Clinical Research and Health Policy Studies 

New England Medical Center 

T-NEMC #063 

750 Washington Street 

Boston, MA 02111 

Submitted to 

Medicare Payment Advisory Commission (MedPAC) 

601 New Jersey Avenue, N.W. 

Suite 9000 

Washington, DC 20001 

May 2006 
- 2 -

Table of Contents 

1 Introduction .......................................................................................... 3

2 Studies of the Cost-Effectiveness of Colorectal Cancer (CRC) 

Screening .............................................................................................. 4

2.1 Model Design, Methodology, and Assumptions......................................... 4 

2.1.1 Funding Source ........................................................................................ 4

2.1.2 Model Type .............................................................................................. 5

2.1.3 Software ................................................................................................... 5

2.1.4 Perspective ............................................................................................... 5

2.1.5 Costs......................................................................................................... 6

2.1.6 Discounting .............................................................................................. 6

2.1.7 Outcome Measures................................................................................... 6

2.1.8 Sensitivity Analysis.................................................................................. 7

2.1.9 Adherence to Cost-Effectiveness Panel Criteria ...................................... 7

2.2 Concordance of Assumptions, Methods, and Results Across Studies........ 7 

2.3 Identifying Influential Assumptions ......................................................... 12 

2.4 Obtaining Model Software from Study Authors....................................... 13 

3 Studies of Implantable Cardioverter Defibrillators (ICDs)................ 13

3.1 Model Design, Methodology, and Assumptions....................................... 13 

3.1.1 Funding Source ...................................................................................... 13

3.1.2 Model Type ............................................................................................ 13

3.1.3 Software ................................................................................................. 14

3.1.4 Perspective ............................................................................................. 14

3.1.5 Costs....................................................................................................... 14

3.1.6 Discounting ............................................................................................ 14

3.1.7 Outcome Measures................................................................................. 15

3.1.8 Sensitivity Analysis................................................................................ 15

3.1.9 Adherence to Cost-Effectiveness Panel Criteria .................................... 15

3.2 Concordance of Assumptions, Methods, and Results Across Studies...... 15 

3.3 Identifying Influential Assumptions ......................................................... 19 

3.4 Obtaining Model Software from Study Authors....................................... 21 

4 Conclusions ........................................................................................ 21

Appendix – Summary of Study Design, Methodology, and Assumptions .. 24

References..................................................................................................... 27
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1 Introduction 

 The objective of this project has been to review the methodologies and assumptions used 

in selected cost-effectiveness studies published in peer-reviewed journals for two Medicarecovered services. 

 In order to facilitate identification of these two covered services, the Tufts New England 

Medical Center (NEMC) research team (formerly at the Harvard Center for Risk Analysis) 

submitted to MedPAC in November 2005 a preliminary analysis of four candidate services that 

included pharmaceuticals, medical devices, surgical procedures, diagnostic procedures, and 

cognitive services covered by Medicare. The four services identified for this purpose were 

implantable cardioverter defibrillators (ICDs); colorectal cancer (CRC) screening alternatives; 

positron emission tomography (PET) for Alzheimer’s disease; and erythropoietin for cancer 

patients undergoing chemotherapy. These services were selected because they represent a diverse 

group with respect to type of intervention (i.e., medical devices, pharmaceuticals, procedures), 

and because they represent important interventions for the Medicare population in terms of their 

impact on beneficiary health and their impact on Medicare spending. 

 Following submission of our November, 2005 review, MedPAC selected CRC screening 

and ICDs as the two services for the analysis described in the present report. These services were 

selected because we identified a large number of studies that assessed their cost-effectiveness in 

the health economics literature. For this deliverable, per the scope of work, for each of these two 

services, we first summarize characteristics for each identified study, including 1) the type of 

model used; 2) the model’s perspective; 3) the study funding source; 4) the modeling software 

used; 5) cost categories included; 6) discount rates used; 7) outcome measures (e.g., life years, 

quality adjusted life years, etc.); 8) type of sensitivity analysis conducted; and 9) adherence to 

criteria specified by the Panel on Cost-Effectiveness in Health and Medicine (1). 

 Second, we discuss the extent to which the studies for each of these two services are in 

accordance with respect to their assumptions, methods, and results. 

 Third, we attempt to identify factors that most explain the differences in results across 

studies. 
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 Finally, we are in the process of contacting the lead authors of each study to see if they 

would be amenable to providing their modeling software for a more comprehensive evaluation of 

the literature and to gather their comments on the analysis described in this report. 

 The remainder of this report has three sections. Section 2 discusses the CRC literature. 

Section 3 discusses the ICD literature. Finally, we provide our conclusions in Section 4. 

2 Studies of the Cost-Effectiveness of Colorectal Cancer (CRC) 

Screening 

 We eliminated from consideration two of the 28 studies identified in the November, 2005 

report to MedPAC. We eliminated Sorentino et al. (2) because we were unable to obtain a 

manuscript for this study. We eliminated Tarraga Lopez et al. (3) because of problems 

interpreting the methodology and results of the study. 

2.1 Model Design, Methodology, and Assumptions 

 The Appendix tables summarize model design, methodology, and assumptions (see 

Tables 1a and 1b in the Appendix). Note that these studies fall into two categories. Of the 26 

studies, 22 evaluated screening programs designed to identify cancer or indications of precancer 

in asymptomatic individuals. The remaining four studies (4-7) evaluated screening programs 

designed to identify individuals with a genetic predisposition to develop CRC. For the purpose of 

the following discussion in this section, these two sets of studies are aggregated. As is evident 

from the discussion in Section 2.2, none of the studies alone addresses the full range of 

interventions available. However, collectively, the literature addresses this set of interventions. 

2.1.1 Funding Source 

 Of the 26 studies included in our analysis, 18 clearly specified their funding source. Of 

these, six listed a foundation (or what appeared to be a foundation) as their sole source of 

financial support, nine listed government as their sole source of support, two listed both 

government and foundation support, and one listed professional society support. 
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2.1.2 Model Type1

 Of the 26 studies, 11 studies indicated the use of a Markov model, and one other 

indicated use of discrete event simulation. Five others indicated the use of a decision analytic 

model, but did not clearly describe what type (discrete event simulation, Markov simulation, or 

some other type of simulation). Of the remaining nine studies, four statistically compared two 

groups, and five used a “static” model, which is essentially a closed form equation that specifies 

the benefits and costs of each intervention. 

2.1.3 Software 

 For Markov models (N=11), four studies used TreeAge DATA , two used Microsoft 

Excel, one used SML Tree , and one used Decision Maker. The three remaining Markov study 

papers did not specify what software they used. The six other decision model papers (including 

one that reported use of discrete event simulation) reported use of MISCAN-COLON in one case 

(apparently special purpose software), Insight version 5.4 in one case, and Decision Maker in one 

case. The other three decision models did not specify the type of software used. Of the four 

papers using statistical comparisons, one reported use of published results, while the other three 

did not report the underlying software used. Finally, this issue is not applicable to the static 

models, as no specialized software is needed to implement the needed calculations. 

2.1.4 Perspective 

 Just under half the studies (N=12) provided an explicit indication of the study 

perspective. Of these, nine indicated that the analysis was conducted from the perspective of the 

health care payer, while the other three explicitly stated that the analysis was conducted from a 

societal perspective. 

 

1

Researchers use a variety of approaches to evaluate the cost effectiveness of medical interventions. Perhaps the most 

straight-forward approach is the direct comparison of empirically recorded costs and therapeutic benefits for two 

groups receiving alternative treatments. This report refers to this approach as a “statistical comparison.” In order to 

extrapolate beyond the period during which data were collected, or to extend results to a broader population, 

researchers develop various types of “decision analytic policy models” that combine empirically recorded results with 

other assumptions. A Markov model specifies a set of health states (e.g., healthy, early cancer, late cancer, dead) and 

uses empirical information to quantify the probability that individuals will move from one state to another during a 

fixed period of time (e.g., one month or one year). By assigning costs and benefits to each of these states, Markov 

models can tabulate costs and therapeutic effectiveness for the population over time. Discrete event simulations are a 

different type of computer model, allowing for greater flexibility than Markov models (because they do not limit 

characterization of disease progression to a fixed number of states) but typically this flexibility is gained at the cost of 

greater computational complexity. At the other end of the complexity spectrum are so-called “static models” that 

quantify benefits and costs for alternative treatments using formulas that can be implemented in a conventional 

spreadsheet program, such as Excel.
- 6 -

 Nonetheless, it is not clear what authors really mean when they indicate use of the 

societal perspective. For example, none of the three studies stating that they used the societal 

perspective indicated inclusion of out-of-pocket expenses (e.g., lost wages, time spent by the 

patient, and so forth). Nor was it typically clear if the costs were net of co-payments made by the 

patient. We therefore concluded that only one of the 26 studies used the societal perspective (and 

that study did not even specify the perspective it used). Among the other 25 studies, it was 

generally unclear what fraction of the health care costs were accounted for in the analysis. 

Hence, the extent to which the payer’s perspective was reflected in these analyses was not always 

clear. 

2.1.5 Costs 

 All of the studies included health care costs, although at least one study included what 

was in our judgment clearly a limited subset of these costs (8). Only one study (9) indicated 

incorporation of out of pocket costs into the analysis. No studies indicated inclusion of lost 

productivity costs. 

2.1.6 Discounting 

 Of the 26 studies, five did not indicate whether they discounted either costs or benefits. 

An additional study reported discounting of costs (at 5% annually), but did not indicate whether 

benefits were also discounted. Three studies explicitly stated that they did not discount costs or 

benefits. One study reported discounting costs at 5% annually but indicated that benefits were 

not discounted. Of the remaining 16 studies, the most commonly used annual discount rate was 

3% for both costs and benefits (N=10). Another five studies discounted both costs and benefits at 

5% annually, while the last study discounted both costs and benefits at 4% annually. 

2.1.7 Outcome Measures 

 Most studies quantified benefits in terms of life years only (N=14). Two studies 

quantified benefits in terms of quality adjusted life years (QALYs) only. Six studies quantified 

benefits in terms of a natural unit (deaths prevented, cases detected, or genetically susceptible 

patients identified, or adenomas detected). The remaining four studies quantified benefits in 

terms of both life years and natural units. 
- 7 -

2.1.8 Sensitivity Analysis 

 All but three studies conducted univariate sensitivity analyses. All of these analyses were 

“bounding” analyses, meaning that they explored the impact on the CE ratio of varying 

assumptions from their base case value to some extreme value, one at a time. They did not assign 

any relative probability to the CE values within the identified range of plausible values. 

 Only a single study conducted a multivariate sensitivity analysis, more properly referred 

to as an uncertainty analysis. This analysis evaluated the impact of simultaneous deviations of 

multiple assumptions from their base case values. Because the alternative values were identified 

probabilistically, the resulting alternative CE ratios could be assigned relative probabilistic 

weights. 

2.1.9 Adherence to Cost-Effectiveness Panel Criteria 

 In some respects, the studies described here generally adhere to the Cost-Effectiveness 

Panel criteria. The vast majority of studies discount benefits and costs, for example. On the other 

hand, only a minority even claim to report cost-effectiveness from a societal perspective. We 

judged even those that did make this claim to have been conducted essentially from a healthcare 

payer perspective. In addition, most of the studies used life years as the outcome measure, rather 

than QALYs. 

 For this particular intervention, we believe that of these deviations, the omission of 

patient-incurred costs may have had the greatest impact on the study results. In particular, it is 

conceivable that a non-trivial proportion of CRC cases may result in substantial productivity 

losses as a result of illnesses that might occur prior to retirement. Other deviations (e.g., use of 

life years) may not have as important an effect because it is likely that the majority of the QALY 

losses associated CRC reflect changes in life expectancy anyway. 

2.2 Concordance of Assumptions, Methods, and Results Across Studies 

 In some respects, the studies described above often used similar assumptions. In 

particular, they typically used a 3% discount rate and conducted their analyses from the 

perspective of the health care payer. Beyond these limited similarities, however, study 
- 8 -

methodologies and assumptions differed substantially. Although 11 analyses developed Markov 

models, the remaining 15 included a range of other types of models, including statistical 

comparisons of different groups. The vastly different nature of the underlying models alone 

complicates comparison of assumptions across studies. 

 Assessing concordance of results across evaluations of CRC interventions is complicated 

by the diverse range of interventions, comparators, populations analyzed, and units used to 

quantify benefits. In order to conduct this assessment, we limited attention to CE ratios that 

compared a strategy of screening asymptomatic individuals for CRC to conducting no screening 

in this population. This criterion eliminated four studies (4-7) that evaluated strategies designed 

to identify individuals at high genetic risk for CRC. Among the remaining studies, requiring a 

“no-screening” comparator eliminated 23 of the 92 reported CE ratios. 

The remaining 69 CE ratios quantified benefits in terms of cancer cases detected (N=7), 

fatalities avoided (N=5), life years (N=53), and QALYs (N=4). Given the dominance of the life 

year metric, we eliminated from consideration CE ratios that used other benefit measures. 

Next, within each study, we consolidated multiple CE ratios that evaluated the same 

technology applied at the same frequency, but to different populations. This consolidation aimed 

to mitigate the potential of assigning excess weight to studies reporting multiple CE ratios that 

effectively represent very similar underlying analyses. For example, Theuer et al. (10) reported 

four CE ratios for the use of colonoscopy to screen for CRC every ten years (starting at age 50), 

one each for the Asian, Black, Latino, and White populations. Because the CE ratios were similar 

for these populations, we averaged these values to produce a single estimate of the costeffectiveness of screening 50-year olds once every 10 years using colonoscopy, compared to 

conducting no CRC screening at all. In addition to replacing the four CE ratios from the Theuer 

et al. study with one ratio, this step replaced four other CE ratios from this study (for use of both 

fecal occult blood testing and sigmoidoscopy in four populations) with a single ratio, and five 

ratios from Gyrd-Hansen et al. (11) with a single ratio (for use of fecal occult blood testing every 

1.5 years or every 2 years in various age groups). In summary, this step replaced 13 CE ratios 

with 3 CE ratios, decreasing the total number CE ratios to 43. 

 Finally, Sonnenberg et al. reported the same CE ratio in two papers (12;13) comparing 

colonoscopy screening every 10 years to no screening. Eliminating this redundancy reduced the 
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number of CE ratios for our analysis to 42. These values, which are summarized in Table 2-1 

(converted to 2004 U.S. dollars), cover 5 distinct screening technologies (colonoscopy, catscan 

colonoscopy, double barium contrast enema, fecal occult blood testing, and sigmoidoscopy) and 

two sets of combined uses of these technologies (fecal occult blood testing and sigmoidoscopy, 

and fecal occult blood testing and double contrast barium enema). For some of these 

technologies, there are CE ratios evaluating more than one screening frequency. 
- 10 -

Table 2-1 

CE Ratios for CRC Screening Strategies: 

Ratios Using Life Years and Comparing Screening to No Screening 

Intervention 

Technology 

Intervention 

Frequency 

Study CE 

2004 US $/LY 

 

COL Every 3 yr Glick et al. (14) 21,763 

 Every 5 yr Glick et al. (14) 17,316 

 Khandker et al. (15) 36,612 

 Every 10 yr Glick et al. (14) 26,693 

 Khandker et al. (15) 22,556 

 O’Leary et al. (16) 10,633 

 Sonnenberg et al. (12;13) 12,728 

 Theuer et al. (10) 17,041 

CAT COL Every 10 yr Sonnenberg et al. (17) 13,309 

DCBE Every 3 yr Glick et al. (14) 16,010 

 Every 5 yr Glick et al. (14) 16,247 

 Khandker et al. (15) 22,374 

 Every 10 yr Glick et al. (14) 26,351 

FOBT Every yr Glick et al. (14) 16,351 

 Gyrd-Hansen et al. (11) 4,643 

 Helm et al. (18) 24,127 

 Khandker et al. (15) 18,347 

 Salkeld et al. (19) 22,996 

 O’Leary et al. (16) 25,860 

 Sonnenberg et al. (12) 11,247 

 Every 2 yr Gyrd-Hansen et al. (11) 7,446 

 Helm et al. (18) 2,942 

 Lejeune et al. (20) 9,185 

 O’Leary et al. (16) 10,861 

FOBT + DCBE Every 3 yr Glick et al. (14) 19,462 

 Every 5 yr Glick et al. (14) 17,060 

 Every 10 yr Glick et al. (14) 18,860 

FOBT + SIG Every 3 yr Glick et al. (14) 18,050 

 Khandker et al. (15) 25,918 

 Every 5 yr Glick et al. (14) 16,421 

 Khandker et al. (15) 23,203 

 Theuer et al. (10) 18,877 

 Every 10 yr Glick et al. (14) 17,468 

SIG One time Frazier et al. (21) 1,391 

 Every 3 yr Glick et al. (14) 16,318 

 Khandker et al. (15) 20,727 

 Every 5 yr Glick et al. (14) 14,384 

 Khandker et al. (15) 16,106 

 Loeve et al. (22) Dominates (b) 

 Sonnenberg et al. (12) 42,310 

 Every 10 yr Glick et al. (14) 24,226 

 O’Leary et al. (16) 9,264 
- 11 -

 Abbreviations: COL (colonoscopy), CAT COL (cat scan, i.e., virtual, colonoscopy), 

DCBE (double barium contrast enema), FOBT (fecal occult blood testing), SIG 

(sigmoidoscopy). 

 (a) Average CE ratio for 50 year-old Asian, Black, Latino, and White populations 

 (b) Screening strategy produces more health (life years) and costs less than the 

alternative (no screening). 

 Visual inspection of the values listed in Table 2-1 reveals that the degree of agreement 

within each of the groups varies. Developing a summary measure of agreement is helpful only if 

there are more than a limited number of measurements. For the purpose of describing withingroup variation, consider the coefficient of variation (CV), defined as the ratio of the standard 

deviation divided by the mean. We report the CV for the three groups that include at least four 

CE ratios: for colonoscopy screening every 10 years, CV=0.37 (N=5); for fecal occult blood test 

screening conducted annually, CV = 0.44 (N=7); and for fecal occult blood test screening 

conducted every two years, CV = 0.45 (N=4). It must be emphasized that even in these groups, 

the number of observations is limited, so the CV values are only indicative of the degree of 

spread. Nonetheless, they indicate reasonable agreement. 

 The degree of agreement can be viewed more qualitatively, as well. For example, the 

potential range of CE values can be divided into qualitative but generally accepted categories. 

Such categories might include 1) screening dominates the no screening alternative (i.e., it 

produces more health measured in life years and costs less than the alternative); 2) screening is 

“inexpensive” (CE ratio < $50,000/LY) (e.g., see (23)); 3) screening is expensive (CE ratio > 

$50,000/LY); 4) screening is dominated by the no screening alternative (i.e., it produces less 

health measured in life years and costs more than the alternative). From this perspective, the CE 

ratios in each of the groups listed in Table 2-1 are generally consistent. 

 To the extent that the CE ratios from various studies differ, it is difficult to attribute these 

differences to the nature of the populations studied. The populations reflected in these 

evaluations typically include asymptomatic individuals between the ages of approximately 50 and 
- 12 -

80 years. More specific characterizations of these populations are typically not possible because 

the assumptions for these studies are often drawn from multiple sources. 

2.3 Identifying Influential Assumptions 

 Influential assumptions can in principle be identified in either of two ways. First, 

sensitivity analyses conducted in each of the studies can be reviewed and the most influential 

assumptions catalogued. This approach is complicated by the fact that the studies review a 

widely varying set of assumptions, even those that evaluate the same technologies. Consider, for 

example, the five studies that evaluated use of colonoscopy screening. The assumptions reviewed 

as part of sensitivity analysis in these studies were: 

• Glick et al. (14): Test sensitivity for detection of polyps, test sensitivity for 

detection of cancer, test specificity, polyp prevalence, polyp incidence, annual 

cancer incidence, polyp dwell time, CRC survival rates, surgical mortality 

(colonic resection), lifetime prevalence and incidence of cancer, cost 

assumptions; 

• Khandker (15): Polyp dwell time, screening compliance rates, test sensitivity; 

• O’Leary (16): Probability that adenomas > 10 mm will progress to cancer, 

discount rate, costs; 

• Sonnenberg (12): Screening compliance rates, test sensitivity and specificity; 

• Theuer (10): Polyp dwell time. 

 The fact that some assumptions (e.g., polyp dwell time and test specificity and 

sensitivity) are analyzed in multiple studies suggests that they are likely to be important. Because 

the lists of assumptions evaluated are so diverse, however, it is difficult to use the sensitivity 

analysis results in a more rigorous manner to comprehensively evaluate the relative importance of 

various assumptions. 

 A second approach that might be used to evaluate the relative importance of alternative 

assumptions would attempt to associate differences in CE ratio values across studies with 

assumptions made in those studies. For CRC, such an approach in practice is not feasible because 

the number of CE ratios that can be directly compared in this manner is limited (as discussed 

earlier), while the number of assumptions are relatively numerous. 
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2.4 Obtaining Model Software from Study Authors 

 We requested a copy of software used in each study, with the exception of those studies 

that indicated use of a static model (see discussion above). We are currently waiting for 

responses to this request from lead authors of the studies. 

3 Studies of Implantable Cardioverter Defibrillators (ICDs) 

 This assessment includes 15 of the 16 studies identified in the November, 2005 report to 

MedPAC. We eliminated one study (24) because it quantifies benefits in terms of a relative risk 

reduction for mortality, not in terms of additional survival. We combined two studies (25;26) 

because they describe two applications of the same model. These revisions leave a total of 14 

distinct studies. 

3.1 Model Design, Methodology, and Assumptions 

 The appendix tables summarize model design, methodology, and assumptions (see Tables 

2a and 2b in the Appendix). Because the set of interventions for this technology is limited 

(compared to the wide range of interventions available for CRC screening), many studies analyze 

the most relevant comparison – i.e., between ICD use and pharmaceutical treatment. 

3.1.1 Funding Source 

 Of the 14 studies included in our analysis, 11 clearly specified their funding source. Of 

these, six listed government agencies as their sole source of funding, two listed institutional 

sources (i.e., they indicated that the research time was funded the researchers’ home institutions), 

and two listed corporations as their sole source of support. The final study identified both 

government and corporate (insurance company) support. 

3.1.2 Model Type 

 Eight of the 14 studies developed cost-effectiveness ratios by statistically comparing the 

experience of cohorts receiving ICDs to the experience of control groups. Five of the remaining 

studies developed Markov models. The final study developed a static model, which is essentially 

a closed form equation that specifies the benefits and costs of each intervention. 
- 14 -

3.1.3 Software 

 All five Markov models were developed in SMLTree. Only one of the eight studies that 

statistically compared ICD and non-ICD intervention groups identified the software used (SAS). 

Finally, this issue is not applicable to the static models, as no specialized software is needed to 

implement the needed calculations. 

3.1.4 Perspective 

 Of the 14 studies, 11 provided a clear indication of the analytical perspective, of which 

seven indicated they took a societal perspective. We judged that only one of these seven actually 

did take a societal perspective, and that the other six took the perspective of the payer. As in the 

case of the CRC studies, the omission of expenses incurred by parties other than the health care 

payer (e.g., productivity losses, time spent by the patient, and so forth) lead us to disagree with 

the societal perspective designation reported by the authors. The four studies that indicated that 

they took the perspective of the payer were consistent with our own evaluation of the perspective 

for these studies. 

 Of the three studies that did not clearly indicate the authors’ intended analytical 

perspective, we judged two as taking the payer’s perspective. We could not determine the 

perspective of the last study. In total, we concluded that 12 of the 14 studies took the payer’s 

perspective, one took a societal perspective, while the perspective of one study could not be 

determined. 

3.1.5 Costs 

 Thirteen of the 14 studies included health care costs, while the remaining study did not 

clearly indicate what costs were included in the analysis. Only one study reported accounting for 

any patient costs (time). Whether patient costs were included could not be determined for two 

studies. 

3.1.6 Discounting 

 Of the 14 studies, one discounted both costs and benefits at a 5% annual rate. In addition, 

two studies did not clearly indicate whether they discounted either costs or benefits. All of the 11 
- 15 -

remaining studies discounted costs at a 3% annual rate. Ten of these also discounted benefits at 

3% annually. The eleventh study did not clearly indicate whether it discounted benefits. 

3.1.7 Outcome Measures 

 Most studies quantified benefits in terms of life years only (N=8). Tow studies quantified 

benefits in terms of QALYs only. Two studies quantified benefits in terms of both QALYs and 

life years. Finally, one study quantified benefits in terms of natural units (the number of hospital 

days avoided). 

3.1.8 Sensitivity Analysis 

 Four of the 14 studies conducted no sensitivity analyses. Seven studies conducted a 

univariate sensitivity analysis (a non-probabilistic analysis in all cases), while two conducted both 

a univariate and multivariate sensitivity analysis. In both cases, the univariate analyses were nonprobabilistic. One of the two multivariate analyses was non probabilistic, while the other 

included a multivariate probabilistic analysis. The last study conducted a non-probabilistic 

multivariate sensitivity analysis but no univariate analysis. 

3.1.9 Adherence to Cost-Effectiveness Panel Criteria 

 Adherence to the Cost-Effectiveness Panel criteria for the studies of ICD costeffectiveness was similar to the adherence for the CRC studies. The major deviation we 

identified was the omission of costs absorbed by parties other than the health care system, and in 

particular, costs associated with lost productivity (due to mortality occurring before the age of 

retirement). Even if most mortality results from deaths that occur after the typical retirement age, 

the substantial productivity costs associated with those that occur earlier might conceivably be 

sufficiently large to substantively affect the cost-effectiveness estimates. 

3.2 Concordance of Assumptions, Methods, and Results Across Studies 

 As with the studies evaluating the cost-effectiveness of CRC screening, the 

methodologies used in studies evaluating the cost-effectiveness of ICDs differed substantially. 

While assumptions were similar for discounting and analytical perspective, model types differed, 

with eight studies relying on statistical comparisons, five using Markov models, and one using a 
- 16 -

closed form equation. In the case of ICDs, the substantial number of well-conducted randomized 

controlled trials (for example, see Sanders et al. (27)) has meant that study authors have had a 

wide range of data to choose from. As described below, study population differences can have an 

important impact on the results. 

 To assess concordance of results across studies, we divided ratios into three categories 

based on the intervention and comparator (ICD vs. pharmaceutical therapy; ICD vs. no treatment; 

and pharmaceutical treatment vs. no therapy), and then further divided these categories into two 

groups each based on the units used to quantify benefits (life years or QALYs). This 

classification yielded 39 ratios comparing ICDs and pharmaceutical therapy (22 life year and 17 

QALY ratios); 7 ratios comparing ICDs to no treatment (4 life year and 3 QALY ratios); and 7 

ratios comparing pharmaceutical treatment to no treatment (4 life year and 3 QALY ratios). In 

addition to these categories, we identified 11 ratios comparing other interventions, such as a 

comparison of different types of ICD technology (28), a comparison of (at the time) state of the 

art ICD technology and less advanced ICD technology (29), and an evaluation of a hybrid 

strategy that involves potentially switching from pharmaceutical treatment to ICDs if warranted 

by health considerations (30). As in our analysis of CRC CE ratios, we first converted all 

currency values to 2004 U.S. dollars. Tables 3-1 and 3-2 summarize the resulting ratios. Note 

that these tables also quantify the ratio denominator (incremental costs) and denominator 

(incremental benefits). 
- 17 -

Table 3-1 

CE Ratios for ICD and Corresponding Pharmaceutical Strategies: 

Ratios Using Life Years

Author 

Incremental 

Costs 

Incremental 

Benefits 

CE Ratio 

2004 US $/LY 

 

ICD vs. Pharmaceutical Treatment 

Al-Khatib et al. (31) $95,373 1.8 $53,026 

Larson et al. (32) $16,596 0.21 $78,464 

McGregor et al. (33) NA NA $49,832 

Mushlin et al. (34) $25,399 0.8 $31,778 

Owens et al. (30) $46,357 0.69 $66,933 

$48,465 0.69 $69,412 

Sanders et al. (35) $42,520 0.59 $71,773 

$39,231 0.2 $196,610 

$43,427 0.08 $568,628 

Sanders et al. (27) $89,072 3.64 $24,468 

$53,965 -0.4 Dominated(a)

$98,162 4.14 $23,694 

$76,789 2.04 $37,718 

$97,195 2.72 $35,590 

$56,963 -0.48 Dominated(a)

$66,054 1.87 $35,300 

$68,665 1.4 $49,033 

Sheldon et al. (26) and O’Brien et al. (25) $37,395 -0.4 Dominated(a)

$40,035 0.22 $181,954 

$32,656 0.44 $73,822 

$30,246 1.7 $17,818 

Weiss et al. (36) $47,055 0.5 $88,894 

 

ICD vs. No Treatment 

Hauer et al. (29) -$24,178 0.41 Dominates(b)

Sanders et al. (35) $60,775 1.01 $59,754 

$60,208 0.51 $116,560 

$67,804 0.26 $258,292 

 

Pharmaceutical Treatment vs. No Treatment 

Hauer et al. (29) $82,400 1.98 $41,512 

Sanders et al. (35) $18,255 0.42 $42,860 

$20,976 0.31 $66,104 

$24,378 0.18 $131,073 

NA – Information not available. 

Notes: 
- 18 -

 (a) Use of ICDs produces less health (life years) and costs more than the use of 

pharmaceuticals. 

 (b) Use of ICDs produces more health (life years) and costs less than no treatment. 

Table 3-2 

CE Ratios for ICD and Corresponding Pharmaceutical Strategies: 

Ratios Using QALYs

Author 

Incremental 

Costs 

Incremental 

Benefits 

CE Ratio 

2004 U.S. 

$/QALY 

 

ICD vs. Pharmaceutical Treatment 

Chen et al. (37) $102,613 0.9986 $102,790 

Owens et al. (30) $46,357 0.5 $92,219 

$48,465 0.51 $95,194 

Owens et al. (38) NA NA $55,219 

NA NA $50,910 

NA NA $60,094 

Sanders et al. (35) $42,520 0.52 $81,411 

$39,231 0.18 $221,895 

$43,427 0.06 $632,577 

Sanders et al. (27) $89,072 2.64 $33,752 

$53,965 -0.29 Dominated 

$98,162 2.99 $32,882 

$76,789 1.47 $52,321 

$97,195 1.96 $49,613 

$56,963 -0.34 Dominated 

$66,054 1.36 $48,646 

$68,665 1.01 $67,892 

 

ICD vs. No Treatment 

Sanders et al. (35) $60,775 0.89 $67,804 

$60,208 0.45 $132,434 

$67,804 0.23 $293,441 

 

Pharmaceutical Treatment vs. No Treatment 

Sanders et al. (35) $18,255 0.37 $48,869 

$20,976 0.27 $75,401 

$24,378 0.17 $150,236 

 Unlike in the case of CRC, we have in the case of ICDs retained multiple CE ratios from 

several studies for the same intervention. For example, Sanders et al. (27) contributes eight 

values to the list of life year CE ratios that compare ICDs to pharmaceutical treatment. In the 
- 19 -

case of CRCs, the CE ratios were similar for different populations. On the other hand, population 

characteristics (e.g., baseline mortality rate and ratio of sudden to non-sudden deaths) have a 

substantial influence on CE ratios for ICDs (see discussion below). Qualitatively, the costeffectiveness ratios also diverge. For example, for QALY CE ratios comparing ICDs to 

pharmaceutical treatment, four ratios fall below the $50,000/QALY threshold, eight are between 

$50,000/QALY and $100,000/QALY, and the remaining three range from just over 

$100,000/QALY to more than $600,000/QALY. 

3.3 Identifying Influential Assumptions 

 The fact that there are a relatively large number of comparable CE ratios (19 positive 

values for the life year ratios) facilitates an analytical evaluation of why they differ. Because 

Benefits

Cost CE

∆

∆

= , it follows that log(CE) = log(∆Cost) − log(∆Benefits). Based on this 

relationship, we can regress the log of the CE ratio against the log of the incremental costs and 

incremental benefits to determine which of these two factors contributes the most to the CE 

ratio’s variability. The proportion of variation explained by each of these terms serves as an 

indicator of the degree of variation explained2

. As expected, very close to 100% of the variation

is explained by log(∆Benefit) and log(∆Cost) (the model R2 > 99.9%, with the shortfall 

appears to reflect errors introduced by the rounding of reported values). Of the total variation in 

log(CE) , log(∆Benefit) explains 90% and log(∆Cost) explains approximately 20%3. 

 The implication of this analysis is that differences in the CE ratio values reported across 

studies are due to factors affecting estimated benefits, and not for the most part due to factors that

influence cost estimates. Factors that might have a substantial impact on estimated benefits 

include 1) the baseline mortality rate (i.e., the mortality rate among individuals who do not 

receive ICDs); and 2) the mortality relative risk associated with ICDs. 

 Differences in the baseline mortality rate and estimated relative risk for ICDs may be due 

to different results from randomized controlled trials (RCTs). Figure 2 in Sanders et al. (27) 

 

2

 The values reported here are based on the Type III sums of squares computed using SAS procedure GLM 

(version 9, SAS Institute, Cary, NC. The proportion of variation is the Type III sums of squares divided by 

total sums of squares. 

3

 ). If the two explanatory variables were statistically independent, the proportion of variation explained 

would sum to 100%. Because they are modestly correlated, there is some overlap in their explanatory 

power, and as a result, these proportions sum to more than 100%. 
- 20 -

shows the degree to which both of these factors vary across studies. The figure suggests that 

mortality relative risk ranges from approximately 0.45 to more than 1.0 and that baseline annual 

mortality ranges from as little as approximately 7.5% to nearly 20%. Goldberger and Lampert 

(39) reviewed these studies. They concluded that mortality relative risk exceeded unity in two 

studies (DINAMIT and CABG) because the baseline mortality risk in those two studies was 

relatively low (see pp 812-13 in that review). On the other hand, Sanders et al. (27) noted that the 

DEFINITE trial reported that ICDs substantially reduce mortality even though the baseline 

mortality risk for that study’s cohort was already low (see their Figure 2). In any case, 

differences in the results reported by RCTs do not explain all differences in CE ratio estimates. 

Based on the six RCTs reporting relative risks below unity (i.e., among those studies that found 

ICDs reduce risk), Sanders et al. (27) estimated that incremental life expectancy ranged from 1.40 

to 4.14 life years. Other CE ratio studies report substantially smaller (although still positive) 

incremental life expectancy estimates (see Table 3-1). 

 A further assumption that may be important is the estimated ratio of sudden cardiac 

mortality (which can be prevented by an ICD) to non-sudden cardiac mortality (which cannot). 

Owens et al. (38) demonstrated that the ratio of these two mortality rates can substantially 

influence the CE ratio, even if total mortality remains unchanged. For example, Owens et al. 

estimated the cost effectiveness of ICDs relative to pharmaceutical treatment to be $29,800 if the 

ratio of sudden to non-sudden cardiac death is 15. If, on the other hand, the ratio of non-sudden 

to sudden cardiac mortality is assumed to be 15, the cost-effectiveness ratio is nearly an order of 

magnitude greater ($236,000/QALY). 

 It is also true that the benefit afforded by ICDs depends on patient baseline health. For 

example, results reported by Sanders et al. (35) indicate that ICDs offer the greatest advantage 

over pharmaceutical treatment for patients with a low ejection fraction (EF). For patients with EF 

<= 0.3, ICDs extend life expectancy by 0.59 years compared to pharmaceutical treatment. For 

patients with EF > 0.4, the corresponding improvement is only 0.08 years. Sheldon et al. (26) 

estimated cost-effectiveness after stratifying patients according to the number of risk factors they 

had (factors were age >= 70, left ventricle ejection fraction <= 35%, New York Heart Association 

risk class III). For patients with none of these risk factors, ICDs reduced life expectancy, 

compared to treatment with pharmaceuticals. For patients having 1, 2, or 3 risk factors, ICDs 

increased life expectancy by 0.22, 0.44, and 1.70 life years, respectively. Unfortunately, many of 
- 21 -

the CE studies described here do not evaluate the impact of baseline risk on the CE ratio. The 

results described here indicate, however, that baseline risk plays an important role. 

 Although differences in cost-effectiveness results for ICDs cannot be completely 

explained, this analysis has shown that the major factors can be identified. 

3.4 Obtaining Model Software from Study Authors 

 For each study, we sent a request to the first author requesting a copy of the software 

used to implement the paper’s model. An initial email request was sent to the author on April 13, 

2005, with follow-up requests sent on May 11. Table 3-3 summarizes the responses received 

through May 24, 2006 for the 40 studies retained for the analysis in this report. 

Table 3-3 

Author Responses to Requests for a Copy of Their Model Software(a)

Response Category Number of 

Responses 

Percent 

 

Unable to locate lead author or author deceased. 5 12.5 

 

Emailed author but did not receive a response within one month and 

following two emails. 

17 42.5 

 

Forwarded our request to another author or directed us to contact 

someone else. 

7 17.5 

 

Replied that no model software was used - just arithmetic (e.g., 

Excel), statistical analysis, or something similar.

5 12.5 

 

Author not willing to provide model details (e.g., because of ongoing 

work) or because model is difficult to interpret by others and author is 

afraid it will be misinterpreted 

6 15 

Notes: (a) Responses from authors listed on multiple studies are recorded once for each 

study they were contacted for, even if they responded only once to our inquiry. 

4 Conclusions 

 Our review indicates that for high profile and potentially high-cost Medicare-reimbursed 

services, such as ICDs and CRC screening, there are numerous cost-effectiveness analyses in the 
- 22 -

medical literature. Many of the studies measure outcomes in terms of costs per life year or 

QALY gained, hence providing a convenient basis for comparison. In some respects, such as the 

discounting of costs and benefits, the studies we reviewed adhered to key criteria specified by the 

Panel on Cost-Effectiveness in Health and Medicine (1). The use of QALYs to quantify benefits 

was less common than the use of life years. However, the fact that mortality is likely to account 

for vast majority of the QALY loss associated with the relevant health effects means that this 

deviation from the Cost-Effectiveness Panel criteria is unlikely to be important for these 

particular interventions. For these reasons, the literature provides a rough estimate of the net 

costs and clinical effects for the two interventions we reviewed. Collectively, the literature also 

helps to shed light on key areas of uncertainty where additional data collection might be most 

fruitful. 

 To be sure, relying on the literature to rigorously evaluate specific policy issues poses a 

number of challenges. First, few studies take into account costs beyond those incurred by the 

health care payer. To the extent that policy makers wish to include other costs (most notably, lost 

productivity costs) in their assessments, the literature would be inadequate. Second, cost 

effectiveness analyses use a wide range of assumptions. Where there are multiple effectiveness 

studies available (as is clearly evident in the case of ICDs), this issue can cause substantial 

divergence in cost-effectiveness estimates. Those studies can produce different results for a 

variety of reasons, some of which may be related to the nature of the populations included. Third, 

studies can differ in terms of the specific interventions evaluated. This issue was particularly 

evident in the case of CRC screening, where there were many combinations of screening 

technologies and screening frequencies that could be evaluated. Finally, comparisons across 

studies can be complicated by different approaches to the analysis. For example, some studies 

relied essentially on statistical comparisons of survival, while others extrapolated beyond what 

was observed through the use of decision analytic simulation models. 

 Despite these challenges, the literature offers a useful guide for identifying the most 

promising interventions, and in providing a ballpark estimate of the net costs and clinical effects 

for the two interventions. It is also important to keep in mind that much of the variation across 

cost-effectiveness analyses in terms of the populations addressed and the comparators analyzed is 

not unique to CEAs. Randomized clinical trials of the same intervention typically differ across 

these dimensions as well (as is evident in the case of the trials evaluating ICDs). To the extent 

that policy makers are interested in evaluating specific interventions and understanding the 
- 23 -

implications of different assumptions on cost-effectiveness ratios, they may wish to conduct or 

contract for additional analyses of the published estimates. 
- 24 -

Appendix – Summary of Study Design, Methodology, and Assumptions 

Table 1a 

CRC Screening: Model Design, Methodology and Assumptions – Part 1 of 2 

 Perspective Costs 

Study Funding Model Type Software Author Reader 

Health 

Care 

Out of 

Pocket 

Berchi et al. (40) Foundation? Markov DATA Payer Payer Yes No 

Frazier et al. (21) Government Markov SMLTREE Not stated Payer Yes No 

Glick et al. (14) Not stated 

Decision 

Model Not stated Not stated Payer Yes No 

Gyrd-Hansen et al. 

(11) Not stated Static Model 

Not 

applicable Payer Payer Yes No 

Heitman et al. (41) Foundation Markov DATA Payer Payer Yes No 

Helm et al. (18) 

Government + 

Foundation 

Statistical 

comparison 

Used 

Published 

Results Not stated Payer Yes No 

Khandker et al. (15) Government Markov 

Decision 

Maker Not stated Payer Yes No 

Kievit et al. (4) Foundation? Markov DATA Payer Payer Yes No 

LeJeune et al. (20) Government Markov Not stated Payer Payer Yes No 

Lieberman et al. (42) Not stated Static formula 

Not 

applicable Not stated Payer Yes No 

Loeve et al. (22) Government 

Simulation - 

Type unclear 

MISCANCOLON Not stated Payer Yes No 

Nakama et al. (8) Government 

Statistical 

comparison Not stated Not stated Payer Some No 

Ness et al. (43) 

Professional 

Association (Am. 

Coll. Of 

Gastroenterology) 

Discrete Event 

Simulation Insight 5.4 Societal Payer Yes No 

O'Leary et al. (16) Not stated Markov DATA Payer Payer Yes No 

Ramsey et al. (5) Government Static Model 

Excel and 

@Risk Societal Payer Yes No 

Reyes et al. (6) Foundation 

Decision 

Model (type 

not clear) Not stated Societal Payer Yes No 

Robinson et al. (44) Government 

Statistical 

comparison Not stated Not stated Payer Yes No 

Salkeld et al. (19) Not stated Static model 

Not 

applicable Not stated Payer Yes No 

Sieg et al. (9) Not stated Static model 

Not 

applicable Not stated Societal Yes Yes 

Sonnenberg et al. (17) Foundation Markov Not stated Payer Payer Yes No 

Sonnenberg et al. (12) 

Government 

Foundation Markov Excel Payer Payer Yes No 

Sonnenberg et al. (13) Not stated Markov Excel Not stated Payer Yes No 

Theuer et al. (10) Government 

Decision 

Model Not stated Not stated Payer Yes No 

Vasen et al. (7) Not stated 

Decision 

Model 

Decision 

Maker Not stated Payer Yes No 

Whynes et al. (45) Foundation Markov Not stated Payer Payer Yes No 

Yamamoto et al. (46) Government 

Statistical 

comparison Not stated Not stated Payer Yes No 
- 25 -

Table 1b 

CRC Screening: Model Design, Methodology and Assumptions – Part 2 of 2 

 Discounting Outcome Measures Sensitivity Analysis 

Study Costs Outcomes QALYs LYs Other Univariate Multivariate 

Berchi et al. (40) 5% None No Yes Bounding None 

Frazier et al. (21) 3% 3% No Yes Bounding None 

Glick et al. (14) 5% 5% No Yes Bounding None 

Gyrd-Hansen et al. 

(11) 5% 5% No Yes Bounding None 

Heitman et al. (41) 3% 3% No Yes 

Perforations 

of the colon, 

adenomas 

removed Bounding None 

Helm et al. (18) None None No Yes Bounding None 

Khandker et al. (15) 3% 3% No Yes Bounding None 

Kievit et al. (4) 4% 4% No Yes 

Cases 

detected Bounding None 

LeJeune et al. (20) 3% 3% No Yes Bounding None 

Lieberman et al. (42) Not stated Not stated No No 

Death 

prevented Bounding None 

Loeve et al. (22) 3% 3% No Yes Bounding None 

Nakama et al. (8) None None No No 

Case 

Detected None None 

Ness et al. (43) Not stated Not stated Yes No Bounding None 

O'Leary et al. (16) 5% 5% No Yes Bounding None 

Ramsey et al. (5) 3% 3% No Yes 

Carrier 

detected Bounding Probabilistic 

Reyes et al. (6) None None No No 

Carrier 

detected Bounding None 

Robinson et al. (44) Not stated Not stated No No 

Detected 

adenomas > 1 

cm None None 

Salkeld et al. (19) 5% 5% No Yes Bounding None 

Sieg et al. (9) Not stated Not stated No No 

Cases 

detected None None 

Sonnenberg et al. (17) 3% 3% No Yes Bounding None 

Sonnenberg et al. (12) 3% 3% No Yes Bounding None 

Sonnenberg et al. (13) 3% 3% No Yes Bounding None 

Theuer et al. (10) 5% 5% No Yes Bounding None 

Vasen et al. (7) 5% Not stated No Yes Bounding None 

Whynes et al. (45) 3% 3% Yes No Bounding None 

Yamamoto et al. (46) Not stated Not stated No No 

Cases 

detected None None 
- 26 -

Table 2a 

ICDs: Model Design, Methodology and Assumptions – Part 1 of 2 

 Perspective Costs 

Study Funding Model Type Software Author Reader 

Health 

Care 

Out of 

Pocket 

Al-Khatib et al. (31) 

Corporate 

(device maker) 

Statistical 

Comparison Not stated Societal Payer Yes No 

Cardinal et al. (28) Not stated 

Statistical 

Comparison Not stated 

Not 

Stated Payer Yes No 

Chen et al. (37) Institutional Static Model 

Not 

applicable Societal Societal Yes 

Yes 

(Time) 

Hauer et al. (29) Not stated 

Statistical 

Comparison Not stated Payer Payer Yes No 

Kupersmith et al. 

(47) Not stated Markov SMLTree Payer Payer Yes No 

Larson et al. (32) Government 

Statistical 

Comparison Not stated Societal Payer Yes No 

McGregor et al. (33) Institutional 

Statistical 

Comparison Not stated Payer Payer Yes No 

Mushlin et al. (34) 

Corporate 

(device maker) 

Statistical 

Comparison Not stated 

Not 

Stated Payer Yes 

Not 

Clear 

Owens et al. (30) Government Markov SMLTree Societal Payer Yes No 

Owens et al. (38) Government Markov SMLTree Societal Payer Yes No 

Sanders et al. (35) Government Markov SMLTree Societal Payer Yes No 

Sanders et al. (27) 

Government; 

Corporate 

(Insurance co.) Markov SMLTree Societal Payer Yes No 

Sheldon et al. (26); 

O'Brien et al. (25) Government 

Statistical 

Comparison Not stated Payer Payer Yes No 

Weiss et al. (36) Government 

Statistical 

Comparison SAS 

Not 

Stated Not Clear 

Not 

Clear 

Not 

Clear 

Table 2b 

ICDs: Model Design, Methodology and Assumptions – Part 2 of 2 

 Discounting Outcome Measures Sensitivity Analysis 

Study Costs Outcomes QALYs Lys Other Univariate Multivariate 

Al-Khatib et al. (31) 3% 3% No Yes None Bounding 

Cardinal et al. (28) Not stated Not stated No No 

Hospital 

Stay (days) None None 

Chen et al. (37) 3% 3% Yes No Bounding None 

Hauer et al. (29) Not stated Not stated No Yes None None 

Kupersmith et al. 

(47) 5% 5% No Yes None None 

Larson et al. (32) 3% 3% No Yes Bounding None 

McGregor et al. (33) 3% 3% No Yes Bounding None 

Mushlin et al. (34) 3% 3% No Yes Bounding None 

Owens et al. (30) 3% 3% Yes Yes Bounding None 

Owens et al. (38) 3% 3% Yes No Bounding Bounding 

Sanders et al. (35) 3% 3% Yes Yes Bounding Probabilistic 

Sanders et al. (27) 3% Not Clear Yes Yes Bounding None 

Sheldon et al. (26); 

O'Brien et al. (25) 3% 3% No Yes Bounding None 

Weiss et al. (36) 3% 3% No Yes None None 
- 27 -

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