http://mdm.sagepub.com/Medical Decision Making

http://mdm.sagepub.com/content/18/4/365The online version of this article can be found at:

 DOI: 10.1177/0272989X9801800402

1998 18: 365Med Decis MakingTammy O. Tengs, Eric P. Winer, Susan Paddock, Omar Aguilar-Chavez and Donald A. Berry

Testing for the BRCA1 and BRCA2 Breast-Ovarian Cancer Susceptibility Genes: A Decision Analysis  

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365

Testing for the BRCA1 and BRCA2Breast-Ovarian Cancer Susceptibility Genes:A Decision Analysis

TAMMY O. TENGS, SCD, ERIC P. WINER, MD, SUSAN PADDOCK,OMAR AGUILAR-CHAVEZ, DONALD A. BERRY, PhD

Objective. The authors developed a Markov decision model to evaluate the healthimplications of testing for mutations in the BRCA1 and BRCA2 breast-ovarian cancersusceptibility genes. Prophylactic measures considered included various combinationsof immediate and delayed bilateral mastectomy and oophorectomy or taking no action.Methods. The model incorporated the likelihood of developing breast and/or ovariancancer, survival, and quality of life. Parameter values were taken from public data-bases, the published literature, and a survey of cancer experts. Outcomes consideredwere additional life expectancy and quality-adjusted life years (QALYs). Results arereported for 30-year-old cancer-free women at various levels of hereditary risk. Resultsand conclusions. The vast majority of women will not benefit from testing because theirpre-test risks are low and surgical prophylaxis is undesirable. However, women whohave family histories of early breast and/or ovarian cancer may gain up to 2 QALYsby allowing genetic testing to inform their decisions. Key words: BRCA1; BRCA2; ge-netic testing; breast cancer; ovarian cancer; decision analysis. (Med Decis Making1998;18:365-375)

Two breast cancer susceptibility genes, BRCAll andBRCA22 have been identified recently. About 5% ofall breast cancers’ and 10% of ovarian cancers’ are

thought to be due to hereditary mutations, andBRCA1 and BRCA2 are believed to be responsible formost hereditary breast and ovarian cancers. Womenwho have a mutation in BRCA1 have a 56-85%chance of developing breast cancer and a 16-63%chance of developing ovarian cancer in their life-times, with estimates varying depending on the pop-ulation studied. 1,5 The cancer risks associated withBRCA2 are thought to be similar, but because it wasmore recently cloned, its penetrance remains un-certain.

Many women who believe that they carry muta-tions will consider having their breasts and/or ova-

Received August 14, 1997, from the School of Social Ecology,University of California at Irvine, Irvine, California (TOT); the In-stitute of Statistics and Decision Sciences, Duke University, Dur-ham, North Carolina (DAB, SP, OA-C); and the Division of He-

matology Oncology, Department of Medicine, Duke University,Durham, North Carolina (EPW). Revision accepted for publica-tion May 5, 1998. Supported in part by the National Cancer In-stitute through a Specialized Program of Research Excellence(SPORE) grant in Breast Cancer at Duke University, P50 CA68438.

Address correspondence and reprint requests to Dr. Tengs:Department of Urban and Regional Planning, School of SocialEcology, University of California, Irvine, Irvine, CA 92697-7075;telephone: (949)824-4630; fax: (949)824-2056; e-mail: (tengs@uci.edu).

ries surgically removed to reduce the risks of can-cer. Schrag et al.’ recently reported that carrierswho elect prophylactic mastectomy may gain 2.9 to5.3 years in life expectancy) and those who elect pro-phylactic oophorectomy may gain 0.3 to 1.7 years. Ofcourse, if survival were the only concern, then evenwomen who were not carriers could improve theirlongevity by surgically removing organs (e.g.,breasts, ovaries) that might become cancerous.

Women will want to take many additional factors,such as the quality-of-life implications, into accountin deciding whether to get tested.What is the value of testing for BRCA1 and BRCA2?

Contrary to what the public might believe, testingmay not provide a definitive answer about whethera particular woman has a genetic predisposition forbreast cancer, whether she has a mutation in the

BRCA1 or BRCA2 gene, or whether she will ulti-

mately develop cancer. A woman might well have agenetic predisposition, even if no mutation in thesegenes is found. Further, any test for mutations willhave some error rate, with the potential for bothfalse negatives and false positiveS.7 Finally, althoughwomen with a mutation have higher rates of breastand ovarian cancers, it is not possible to say withcertainty which women will develop cancers, and atwhat ages. In a recent editorial, Bernadine Healy lik-ened BRCA1 and BRCA2 testing to reading a &dquo;cloudycrystal ball. &dquo;8

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FIGURE 1. Markov decision model for whether a woman should be tested for BRCA1 and BRCA2.

Despite its limitations, genetic testing may havevalue. While not definitive, the result of testingchanges the assessment of the probability that awoman carries a mutation, and thus the assessed

probability that she will develop breast and/or ovar-ian cancer. Such information may help some

women decide to pursue courses of action (such as

prophylactic mastectomy) to reduce their risks ofdeveloping cancer. If this measure reduces the riskof cancer, it might result in a net improvement insurvival and also overall quality of life, even aftertaking into account the negative quality-of-life as-pects of the prophylactic intervention. In addition,women who are contemplating mastectomy and/oroophorectomy but who test negative for a mutationmight benefit by avoiding surgery and its associatednegative sequelae.Commercial testing for BRCA1 and BRCA2 is now

available. Despite the concerns of some scientists,policymakers, ethicists, and breast cancer experts, 8

many people will choose to take advantage of clini-cal testing. Initial surveys have indicated a stronginterest in testing; in one study, 79% of the membersof breast-ovarian-cancer families indicated that

they would &dquo;definitely&dquo; wanted to be tested.~ Thus,it is now appropriate to ask: &dquo;Are there benefits as-sociated with BRCA1 and BRCA2 testing?&dquo; &dquo;Who, if

anyone, should be tested?&dquo; In this study, we soughtto answer these questions.

Methods

We considered the following scenario:

A 30-year-old woman who has completed her child-bearing and is currently free of cancer is contem-plating testing for BRCA1 and BRCA2. She wants toensure the best possible expected health outcome for

herself. She cares about both quantity and quality oflife and is willing to make tradeoffs between them.She wants to make the decision that maximizes her

expected quality-adjusted life years (QALYs).

Prior to testing, the probability that such a womancarries a mutation can be determined from her fam-

ily history using the methods described by Berry etal.9 and Parmigiani et al.1o If her family history isunknown, then the population average of 1/30011 willserve as her pretest probability of a mutation. In ei-ther case, the test will be used to update the pretestprobability, which has already taken into accountany available information on family history.To implement the scenario described above, we

appended a decision model to a Markov model anddeveloped the combined models in Microsoft Excel,as described below.

MARKOV DECISION MODEL

We evaluated the decision of whether or not a

woman should get tested for BRCA1 and BRCA2 as

diagrammed in figure 1. Because combination test-ing for both BRCA1 and BRCA2 is now the commer-cial standard, we modeled the information value ofthe combined tests. When one or more mutations

are detected in either BRCA1 or BRCA2 or both, thisis considered a &dquo;positive&dquo; test result for the purposeof this model. When no mutation is detected in

BRCA1 and no mutation is detected in BRCA2, this

is considered a &dquo;negative&dquo; test result. Thus, hereaf-ter we refer to the combined tests for BRCA1 and

BRCA2 as simply &dquo;the test.&dquo; If a woman elects testingand tests positive, then the probability that she car-ries a mutation (in either BRCA1 or BRCA2 or both)is adjusted upward using Bayes’ rule. 12,13 If she testsnegative, the probability is adjusted downward.

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Because testing does not have a direct impact onhealth, the outcomes from testing are necessarily afunction of the measure chosen in light of positiveor negative test results. Further, the net value of test-ing must be measured relative to the outcomes thatwould result from any decision made in the absenceof test information. Thus, to consider the value of

testing we had to evaluate the various prophylacticoptions that might be chosen after, or in the absenceof, testing. The full set of options that we consideredare:

1. Mastectomy2. Mastectomy and oophorectomy3. Oophorectomy4. No prophylactic measure

The optimal measure for a woman who tests pos-itive, tests negative, or does not get tested is expectedto vary as a function of carrier risk. Thus, we didnot establish fixed posttest or no-test strategies. Forexample, we could have assumed that women whoare not tested will pursue no prophylactic measureand established the following fixed testing strategy:if positive then mastectomy; if negative then no pro-phylactic measure. To understand why this might belimiting, consider a woman who has a high pretestrisk of a mutation, feels that oophorectomy wouldnot significantly reduce her quality of life, and iscontemplating a test that is somewhat inaccurate.For such a woman, oophorectomy, rather than do-ing nothing, might be her best option in the absenceof testing or given a negative test result. Thus, anyeffort to preset strategies might have led us to modelsuboptimal posttest decisions, which would havehad the effect of underestimating the value of test-ing. Hence, we simply assumed that upon testingpositive, upon testing negative, or in the absence oftesting a woman would choose the measure that isoptimal for her-and that measure would vary ac-cording to her carrier risk.

In general, we assumed that whatever measurewas chosen, it would commence immediately at age30, but we also explored outcomes for oophorec-tomy/mastectomy when oophorectomy is delayed toage 50 and mastectomy is delayed to ages 35, 40, or50. We allowed for the possibility that a woman whochose to delay an intervention might develop breastor ovarian cancer during the interim. We furtherassumed that natural menopause would occur at

age 50 and that when oophorectomy was pursuedbefore that time the woman would take hormone-

replacement therapy until age 50. We assumed thatafter age 50, all women would cease or decline hor-

mone-replacement therapy.For every combination of genetic status (carrier

or non-carrier) and prophylactic measure, the Mar-

kov simulation model depicted in figure 1 was usedto determine the expected years of life saved andquality-adjusted years of life saved (QALYs). TheMarkov model contains five health states: no cancer,breast cancer, ovarian cancer, both breast cancerand ovarian cancer, and death. Transitions betweenstates were assumed to occur annually, and transi-tion probabilities were captured in multiple transi-tion matrices, one for each age. Standard techniqueswere used to develop the model&dquo; and to estimatetransition probabilities. 15 When cumulative proba-bility distributions were available or could be esti-mated (as in the case of the probability of developingcancer described below), transition probabilitieswere derived directly from these distributions.When only five-year rates were available (as in thecase of some cancer-survival statistics, also de-

scribed below), it was assumed that survival fol-lowed a simple declining exponential function andthe transition probability of dying at age t, given thata woman is alive at age t - 1, was calculated as 1 -EXP(- annual rate at age t).

DATA SOURCES

Data for test accuracy, the likelihood of developingbreast or ovarian cancer, and survival prospectswere obtained from a variety of sources. When eval-uating QALYs, we also included quality-of-life ad-justments. The sources or derivations of all esti-

mates are described below.

Test accuracy. To get information about the ac-

curacy of BRCA1 and BRCA2 testing, we contactedthree companies that have developed or are devel-oping commercial tests. Two of these companieswere not able to give us estimates of test accuracy.A third company estimated that the specificity of itstest was quite high, approaching 99%. As for sensi-tivity, the company based its estimate on the per-centage of known mutations that a testing methodis designed to identify. A method designed to identifyX% of known mutations is estimated to have an X%

sensitivity. There are many potential problems withthis method of estimating sensitivity, not the least ofwhich is that there are probably mutations that havenot yet been discovered and the number of theseundiscovered mutations is unknowable. So, in theabsence of good data-driven estimates of the relia-bility of testing, we examined a range of possibilities.First, we considered a hypothetical perfect test withsensitivity and specificity of 100%. This allowed us todetermine the maximum possible value of test in-formation. Second, we considered an imperfect testwith a sensitivity of 0.8 and a specificity of 0.99. Thisallowed us to determine a lower bound on the value

of test information.

Cancer incidence. The likelihood that a woman

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with no mutation in the BRCA1 or BRCA2 gene will

develop breast or ovarian cancer was estimated us-ing the 1990-92 data in the National Cancer Insti-tute’s Surveillance, Epidemiology and End Results(SEER) public-use database. SEER data indicate thata 30-year-old woman has a 12.84% chance of devel-oping breast cancer and a 1.78% chance of devel-oping ovarian cancer in her remaining lifetime. Be-cause SEER data actually include a fraction ofcarriers along with non-carriers, we adjusted thedata and calculated that a 30-year-old non-carrierhas a 12.60% chance of developing breast cancerand a 1.57% chance of developing ovarian cancer inher remaining lifetime.To capture the likelihood that a woman with a

BRCA1 mutation will develop breast or ovarian can-cer, we used estimates from Struewing et al.s TheStruewing data indicate that a female carrier has a56% chance of developing breast cancer and a 16%chance of developing ovarian cancer by age 70. Welater performed sensitivity analyses using the higherestimates of Easton et al.3 Lacking data on pene-trance for BRCA2, we assumed that it was the sameas for BRCA1.To estimate the cumulative probability of devel-

oping breast or ovarian cancer by any age we fittedseparate gamma functions to the SEER and Struew-

ing data using S-Plus software. Conditioned on car-rier status, we assumed that breast and ovarian can-cers occurred independently of one another, andthus calculated the joint probability of developingbreast and ovarian cancer as the product of the twomarginal probabilities.

Risk reduction. Prophylactic mastectomy and oo-phorectomy may delay the development of canceror reduce the probability of developing cancer. Re-cently, Hartmann16 estimated that bilateral mastec-tomy reduced the risk of breast cancer by 91% re-gardless of family history. In the cohort examined,89% of the women received subcutaneous mastec-

tomy. Subcutaneous mastectomy, however, leavesthe nipple and areola, and many experts believe thatthe more tissue remaining, the greater the subse-quent risk. Thus, in this model we considered com-

plete, rather than subcutaneous, bilateral mastec-tomy and estimated risk reduction at 92%.We could find no published information about the

effectiveness of oophorectomy or the combinationof mastectomy plus oophorectomy. Consequently, togather additional needed data on the likely effective-ness of these interventions, we elicited estimatesfrom cancer experts.Our sample of experts consisted of all MDs who

were principal investigators (PIs) on projects of NCI-sponsored Specialized Programs in Oncology Re-search and Education (SPOREs) in breast cancer atsix U.S. cancer centers. We mailed these experts a

one-page questionnaire. In an accompanying letter,we asked them to fax back the completed question-naire. Of the 55 experts contacted, 18 responded, fora response rate of 33%. Some self-selection oc-

curred ; perhaps those Pls who believed themselvesmore knowledgeable were more likely to respond.Further, following a suggestion in our solicitationletter, some of the Pls consulted colleagues in com-pleting the questionnaire.For each prophylactic intervention, the experts

were asked to estimate the percentage decrease inthe lifetime probability of developing breast or ovar-ian cancer that would be experienced by a 30-year-old woman who carried a BRCA1 mutation. We used

the average of the responses as our estimate of theeffectiveness of each intervention in our model.The expert estimates were elicited for women

with a BRCA1 mutation. We assumed that the effec-tiveness of each intervention would be the same forwomen with a BRCA2 mutation. Women with nei-

ther mutation will start with a lower risk, but weassumed that prophylactic interventions are alsolikely to have the same proportional benefit in pre-venting sporadic cancers, so the percentage de-crease in risk was assumed to be the same for non-carriers as well. Further, although the percentagedecrease in risk was elicited for a lifetime, we as-sumed that the cumulative probability of developingbreast cancer at every age was reduced by the samepercentage. The estimates from the experts, their

ranges, and estimates obtained from the literature

appear in table 1.Survival. Information about the survival prospects

for women who develop breast cancer was alsotaken from the SEER database. For example, age-specific five-year survival rates for women who werediagnosed as having breast cancer ranged from 78%for women diagnosed before age 45 to 86% forwomen diagnosed after age 75.Data on survival prospects following ovarian can-

cer were obtained from Rubin et all These authorsfound that BRCA1 carriers who developed ovariancancer had a better survival rate than did non-car-

riers who developed ovarian cancer. Five-year sur-vival was approximately 60% for women with a

BRCA1 mutation; it was 17% for women without a

mutation. We assumed that survival for BRCA2 car-

riers was the same as that for BRCA1 carriers. We

also assumed that women who develop both breastcancer and ovarian cancer would have the same

survival prospects as those with ovarian cancer

alone.

Information about the survival prospects for

women who do not develop breast or ovarian can-cer was taken from standard life tables published bythe National Center for Health Statistics.18 Along withother more minor effects, operative mortality was

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Table 1 . Estimated Decreases in the Lifetime Probability of Developing Primary Breast or Ovarian Cancer Given VariousProphylactic Measures

*The experts were asked to provide upper and lower 95% probability limits. The lower bound of the range shown here is the smallest of all experts’lower bounds and the upper bound is the largest of all experts’ upper bounds. Positive values indicate decreased risk and negative values indicateincreased nsk.

thartmann et al.’5 report a 91% risk reduction following bilateral mastectomy in a cohort in which 89% of procedures were subcutaneous. Becausewe are assuming that mastectomy is complete rather than subcutaneous, we use a slightly higher estimate of 92%.

testimated by the panel of experts.

not included in the model, as it would have been

unlikely to have any appreciable effect on the re-sults.

Quality of Life. In deciding whether to seek testing,and whether to pursue some risk-reduction strategyupon learning the results, most women will want toconsider changes in quality of life as well as quantityof life. The model allows for individualized health-

related quality-of-life assessment, but for expositorypurposes, we began by considering a particular setof assessments. We assigned a year with breast can-cer the weight of 0.85; a year with ovarian cancer,0.65; and a year with both breast and ovarian can-

cer, 0.60.

As for the quality of life following prophylacticmeasures, consider mastectomy as an example. Anumber of authors have reported that women as-sign the quality of life associated with unilateral

mastectomy following breast cancer an averageweight of around 0.8.~’~ But this scenario differsfrom the present one in that it involves unilateral

mastectomy as a treatment for breast cancer. Thus,for expository purposes, we assumed that bilateralprophylactic mastectomy would have a quality-of-lifeweight of 0.9. Later, we conducted sensitivity analy-ses varying this weight from 0.5 to 1.0. We assumedthat following oophorectomy, before age 50, eachyear while a woman was taking estrogen-replace-ment therapy (and possibly experiencing side ef-

fects) had a quality-of-life weight of 0.97. Quality oflife after menopause, when no estrogen was taken,was assumed to be 1.0.We combined the quality-of-life estimates associ-

ated with cancer status, Qi, and prophylactic mea-

sure, 0,~, into a compound measure Ql. Qz. So, forexample, a woman who developed ovarian cancerafter prophylactic mastectomy was assumed to ex-perience a 0.585 quality of life in subsequent years(i.e., 0.65 ~ 0.9 = 0.585), but a woman who remainedcancer-free after mastectomy would experience a0.9 quality of life (calculated as 1.0 ~ 0.9 = 0.9).For our baseline analyses, we fixed quality-of-life

weights at the values described above, set all otherparameters at their most plausible values, and as-sessed the value of testing as a function of the prob-ability that a woman had a mutation in BRCA1 orBRCA2. As noted previously, the pretest probabilityof a mutation is largely a function of family historyand the age at which family members were diag-nosed as having cancer. Several models have beendeveloped to predict this probability. 1,10,23 Evalua-tions24-26 of the Duke University models9&dquo;0 have re-vealed that they predict mutations extremely well.Thus, it is possible to map any family history to theresults presented here in order to assess the valueof testing for women with different family histories.We explored the sensitivity of our conclusions to

various assumptions. First, we explored the useful-ness of testing depending upon the specific outcomemeasure chosen-survival vs quality-adjusted sur-vival. Second, we varied our assumption about theage at which prophylactic surgery would be pur-sued. Third, we varied our assumption about theaccuracy of the genetic test, from a plausible lowerbound to a perfect test. Fourth, we performed a two-way sensitivity analysis varying the probability thatcarriers would develop breast cancers in their life-times, which is uncertain, and the quality-of-life

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weight assigned to mastectomy, which is likely to bedifferent for different women. Fifth, we explored thesensitivity of our overall conclusions to the particu-lar combination of parameter estimates that weused by investigating what we would have found ifwe had used the parameter estimates employed bySchrag et al.~ Finally, note that our baseline resultsfor the benefits to be derived from testing over arange of carrier risks essentially represent a sensi-tivity analysis over the full range of family histories.

Results

When quality-of-life adjustments were not incor-porated and outcomes were measured in terms oflife expectancy, immediate mastectomy combinedwith oophorectomy offered the greatest survival im-provement for all women, regardless of their geneticstatus. Thus, if survival were the only outcome mea-sure, testing would appear to have no value, becausethe optimal prophylactic measure would be thesame for every probability of carrying a mutation.

Figure 2 incorporates quality of life with survivaland shows the number of QALYs that a 30-year-oldcan expect depending on the prophylactic measureshe chooses and the age at which that measure is

pursued. Assuming the quality-of-life preferencesoutlined previously, the best option for a womanwho is certain to have a mutation is immediate pro-phylactic mastectomy combined with oophorec-tomy. For a woman who is certain not to have amutation, the best option is no intervention. Be-

tween these two extremes, the optimal interventiondiffers as a function of the pretest (or posttest) prob-ability of carrying a mutation. For women with prob-abilities below 0.12, the best option is no interven-tion ; between 0.12 and 0.88, the best option is

oophorectomy; and above 0.88, mastectomy com-bined with oophorectomy is best. Figure 2 also

shows that, assuming the model parameters de-scribed previously, delaying mastectomy and oopho-rectomy is never optimal. These options are domi-nated at all pretest probabilities. Consequently, wedropped the delayed options from the remaininganalyses.Because the optimal intervention differs depend-

ing on the probability of a mutation, women withthe quality-of-life preferences incorporated into fig-ure 2 might benefit from testing. QALYs from a hy-pothetical perfect test as well as from an imperfecttest with a sensitivity of 0.8 and a specificity of 0.99are also plotted in figure 2. With the perfect test, the

FIGURE 2. Quality-adjusted life years(QALYs) as a function of the prob-ability of a BRCA1 or BRCA2 muta-tion.

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371

number of additional QALYs that a 30-year-old couldexpect, if she allowed the test to inform her decision,exceeds the QALYs she could expect in the absenceof testing, regardless of the pretest probability of amutation. For the imperfect test, however, QALYswith testing exceed QALYs without testing for onlythose pretest probabilities between 0.001 and 0.97.Below 0.001, the line representing the imperfect testcorresponds to the line representing no interven-tion, and above 0.97, the line for the imperfect testcorresponds to the line representing prophylacticmastectomy/oophorectomy. This correspondence atthe extremes occurs because the imperfect test doesnot change the pretest probability sufficiently towarrant different decisions for positive and negativetest results. Thus, at the extremes, an imperfect testhas no value. Whether we consider the perfect testor the imperfect test, however, the incremental im-provement in QALYs due to testing is a function ofthe pretest probability and is equivalent to the ver-tical distance between the line representing the op-timal intervention and the line representing the testat that probability.

Figure 3 corresponds to figure 2, but shows onlythe incremental improvement in QALYs resultingfrom testing. Figure 3 reveals that a woman of av-

erage risk with a pretest carrier probability of 1/300could expect to gain 0.008 QALYs (or about threedays) with a perfect test and 0.002 QALYs (or aboutone day) with an imperfect test. The peak at 0.12occurs because this is the point where the test ismaximally useful in helping women decide betweendoing nothing vs oophorectomy. Here, the expectedgain in QALYs is 0.32 with a perfect test or 0.21 withan imperfect test. For women with a 0.5 pretestprobability of a mutation, the test is best used tohelp them decide between oophorectomy vs mas-tectomy combined with oophorectomy. Here the ex-pected gain in QALYs is 0.45 with a perfect test or0.22 with an imperfect test.The analyses described above used the best infor-

mation available for each parameter and plausiblequality-of-life weights. There is, however, consider-able uncertainty about the probability that a carrierwill develop cancer. While we used Struewing’s re-cent estimate of 56% as the probability of breast can-cer by age 70, their 95% confidence interval aroundthis estimate was 40-73%.5 In addition, previous es-timates for all mutations combined ranged as highas 85%.22 Further, individual women will differ intheir feelings about the quality-of-life values associ-ated with various health states. For example, while

FIGURE 3. Net improvements in0/U.Ys due to testing for BRCA1and BRCA2 mutations.

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FIGURE 4. Net improvements in QALYs due to testing for BRCA1and BRCA2 mutations as a function of the assessed quality of lifefollowing mastectomy and the lifetime probability of breast can-cer among carriers.

we used the quality-of-life adjustment for mastec-tomy of 0.9, individual women will have deeply per-sonal, but equally legitimate, quality assessments.Thus, we performed a two-way sensitivity analysis,varying the risks of breast cancer and quality-of-lifeweights. Figure 4 shows the incremental number ofQALYs expected from a perfect test relative to no testwhen a woman with a 0.5 pretest probability of amutation (resulting from an extreme family historyof early breast and/or ovarian cancer) elects testing.We considered breast cancer risks ranging from40% to 90% and quality-of-life weights associatedwith mastectomy ranging from 0.5 to 1.0.

It is clear from figure 4 that a perfect test wouldhave some value to every woman with an extreme

family history regardless of the true risk of breastcancer or her quality-of-life assessment for mastec-tomy. Even women who would assign a quality-of-life weight of less than 0.85 to the years followingmastectomy would gain 0.1 QALY from testing. Thisis because for such women the test is being used todecide between oophorectomy and doing nothing,and thus the benefit of testing is not sensitive to thetwo parameters varied here.

For women with quality-of-life weights above0.85, the net improvement in QALYs is sensitive tothe risk of breast cancer among carriers and quality-of-life preferences. For example, if the cumulativerisk of breast cancer is not 56%, as reported byStruewing/

5 but closer to 85%, as reported by

Easton) 27 then the benefit of testing depends uponthe quality of life assigned to mastectomy: Womenwho assign mastectomy a quality-of-life weight of0.95 might gain 1-1.5 QALYs from testing; those whoassign mastectomy a weight of 0.9 might gain 1.5-2

FIGURE 5. Net improvements in QALYs due to testing for BRCA1and BRCA2 mutations as a function of the assessed quality of lifefollowing mastectomy and the lifetime probability of breast can-cer among carriers using parameter values from Schrag et al.6

QALYs from testing; and those who assign mastec-tomy a weight of 0.85 might gain 0.5-1 QALY fromtesting. Viewed from the other axis, if a woman as-

signs mastectomy a quality-of-life weight of 0.95, theQALYs she could expect from testing are fairly in-sensitive to her lifetime risk of developing breastcancer. Regardless of whether penetrance is as lowas 45% or as high as 90%, she stands to gain 1 to 1.5QALYs from testing. If, however, she assigns mastec-tomy a quality-of-life weight of 0.9, the QALYs shecould expect from testing range from 0.1 to 2, de-pending on penetrance.

Figure 5 shows the same two-way sensitivity anal-ysis substituting in the parameter estimates fromSchrag et al.’ described earlier. While more than90% of the results reflected in figure 5 are identicalto those depicted in figure 4, some differences

emerge. For quality-of-life weights above 0.85, theparameter values from Schrag et al. occasionallylead to estimates of the value of testing that areabout 0.5 QALY higher than ours.

Discussion

A woman of average risk has only a 1/300 chanceof being a BRCA1 or BRCA2 carrier. Our resultsshow, not surprisingly, that women of average riskwould derive virtually no benefit from testing underany ot the circumstances explored here. Using ourbaseline parameter estimates and plausible quality-of-life weights, we found that the benefit to womenof average risk was less than 0.008 QALY (or aboutthree days).Women who have family histories of early breast

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and/or ovarian cancer and thus have elevated pre-test probabilities of carrying a mutation might ben-efit from testing. Using our baseline parameter es-timates, we found that the maximum value of testingwas on the order of half of one (?~1LY.The degree of benefit from testing is sensitive,

however, to a variety of factors. These factors in-clude the particular outcome measure chosen to as-sess the value of testing; the age at which the pro-phylactic measure is pursued; the accuracy of thetest; the risk of developing cancer given a mutation;individual preferences regarding quality of life; andthe pretest risk of having a mutation in BRCA1 orBRCA2, which depends on family history.When we explored the sensitivity of our conclu-

sions to the outcome measure employed, we found,interestingly, that when quality of life was not con-sidered and outcomes were measured in terms ofadditional life years, testing seemed to offer no valueto any woman. This is because mastectomy com-bined with oophorectomy can be expected to im-prove survival for any woman, regardless of her ge-netic risk. This means that in terms of

improvements in life expectancy, it dominates all

other options for women who test positive, but alsofor women who test negative and for women whodo not get tested. Thus, for the mythical woman whowants only to improve her survival and who is en-tirely unconcerned with the quality of that survival,having organs removed because they might becomecancerous can help to achieve that goal. However,this is true regardless of the probability that she car-ries a mutation, so the implication is that testing todetermine that probability would have no value. Ofcourse, the vast majority of women do care aboutquality of life as well as quantity of life, and thus thebenefits of testing expressed in terms of QALYs aremore relevant.

We also explored the sensitivity of our conclusionsto age by looking at the impact of delaying surgery.Results indicated that delaying surgery was subop-timal for all women: women with very low risks of

a mutation are better off choosing no prophylacticmeasure and women with moderate to high risks ofa mutation are better off choosing immediate sur-gery in order to avoid the risk of developing cancerin the interim. We did not vary the age of genetictesting because younger women might not havecompleted their childbearing, making assessment oftheir utilities for oophorectomy more complex.Older women who are cancer-free are unlikely tohave a mutation, because BRCA1 and BRCA2 are as-sociated more with early cancer, so testing of olderwomen would be less valuable.

The value of testing varies depending upon theaccuracy of the genetic test. This is because an in-accurate genetic test will result in false positives and

false negatives, which may lead to the wrong sub-sequent decision. We found that a perfect test wouldyield about 50% more QALYs than an imperfect testwith a 80% sensitivity and a 99% sensitivity.

In a two-way sensitivity analysis, we varied two fac-tors that we thought might have important effectson our results. These factors were the lifetime riskof breast cancer among carriers, which is uncertain,and the quality of life following prophylactic mas-tectomy, which is deeply personal and likely to bedifferent for different women. Results indicated thatwomen who have strong family histories of breastand ovarian cancers (and therefore 0.5 prior prob-ability of being carriers) would benefit from testingover the complete range of these two factors. Thus,for high-risk women the decision to seek testing wasnot sensitive to these parameters. However, the de-

grees of benefit to be derived from testing varied:Women who were very concerned about the qualityof life following prophylactic mastectomy (assigningit a 0.85 weight or less) would benefit from a perfecttest, but only because it informed their decisions be-tween prophylactic oophorectomy and doing noth-ing. For these women, the benefit of testing wouldbe modest, at less than half of one QALY. However,the value of testing to women who were only some-what concerned about quality of life following mas-tectomy (with weights above 0.85) ranged up to 2QALYs, depending on the penetrance of breast can-cer.

We then sought to determine the sensitivity of ouroverall conclusions to the specific combination ofparameter choices we employed. To do this, we firstcompared our results for the gain in life expectancyfrom surgery with those reported in Schrag et al.~

6

to reveal the differences in the models. Then, to geta sense of the robustness of our overall conclusions,we repeated the two-way sensitivity analysis de-scribed above after substituting in to our model thecombination of parameter values used by Schrag etal. In the first comparison, we found gains of 4.4 lifeyears for mastectomy, 2.1 life years for oophorec-tomy, and 6.3 life years for mastectomy and oopho-rectomy. These figures are somewhat higher thanthose of Schrag et al., who found gains of 4.1, 1.0,and 5.3 life years, respectively. These differences canbe attributed primarily to the assumed values ofmodel parameters. While they assumed that the life-time probabilities of cancers among carriers were60% for breast cancer and 20% for ovarian cancer,we used the more exact estimates of 56% and 16%

from the recent study by Struewing et a1.5 Further,they assumed that risk reduction following prophy-lactic mastectomy was 85%. But a recent Mayostudy&dquo; has shown that this risk reduction may begreater, so we assumed that complete mastectomywould offer a 92% risk reduction. In addition, they

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374

assumed that oophorectomy would offer a 50% re-duction in the risk of ovarian cancer, but our panelof experts estimated that risk reduction would bemore on the order of 77-81%. Finally, they assumethat premenopausal oophorectomy would reducethe risk of ovarian cancer but would have no effecton the risk of breast cancer. However, our panel ofexperts estimated that oophorectomy might reducethe risk of breast cancer by as much as 9-25%.There are other structural differences between thetwo models, but despite structural differences, whenwe substituted their assumed parameter values intoour model, we were able to closely approximatetheir results. When using their parameters, ourmodel indicates that carriers stand to gain 4.3, 1.2,or 5.7 years of life with mastectomy, oophorectomy,and mastectomy/oophorectomy, respectively. In a

second comparison, we found that our conclusionsregarding improvement in QALYs from testingwould have been largely the same had we used theparameter values of Schrag et al.s This suggests thatour overall conclusions are robust to the particularcombination of parameter values that we employed.

Finally, we found that the value of testing is quitesensitive to family history. This can be seen in thedifferent results for different pretest risks of a mu-tation. We found that women with no history of earlybreast or ovarian cancer in their families wouldhave low pretest risks of a mutation, and thus wouldbe unlikely to benefit from testing. Women withmany cases of early breast and/or ovarian cancersin their families would have high pretest risks of amutation, and they would be likely to gain roughly0.5 QALY, but they might gain up to 2 QALYs de-pending on some of the factors discussed above.

Given the relatively modest gains reported here,the value of testing must be weighed against otherfactors not considered in this model. One key factornot considered here is cost. Testing is currently paidfor out-of-pocket and can cost as much as $2,400.However, once a mutation has been identified, at-risk family members can obtain testing for around$400. From the societal perspective, it is importantto determine whether testing is cost-effective, takinginto account not only the cost of testing, but also thecosts of prophylactic measures and the avoidedcosts of treating cancer. Work on the cost-effective-ness of testing is in progress.&dquo;An important limitation in this analysis is that we

did not consider the &dquo;reassurance value&dquo; of testing.Women at high risk of a mutation might live withdaily anxiety about their genetic status, and a nega-tive test result could help to relieve this anxiety. Al-ternatively, some women, upon testing negative,might experience feelings of &dquo;survivor guilt,&dquo; partic-ularly if one or more of their family members testedpositive. It is also unclear what the effect of a positive

test result might be. Some women might find thatthe knowledge that they carried a mutation, whichwould make it almost certain that they would de-velop cancer in their lifetimes, decreased the qualityof life. Others might find it reassuring to be relievedof the uncertainty of not knowing and empoweredto do something about their risk. Still others mighthave mixed responses. Measuring the &dquo;reassurancevalue&dquo; of testing is still in its infancy, so we have notattempted it here, although this is an important areafor further study.Our analysis reveals that testing for BRCA1 and

BRCA2 may improve quality-adjusted survival forsome women. The ideal candidate for testing issomeone of moderate to high risk who has no morethan moderate concern about the quality-of-life im-plications of prophylactic surgery. Testing is not rec-ommended for a woman of average risk, as it is veryunlikely to lead to measures that will substantiallyimprove her survival and quality of life.

The authors gratefully acknowledge the assistance of thosebreast cancer experts affiliated with NCI-sponsored SPOREsnationwide who anonymously provided key estimates.

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Corrections

Two errors appeared in the article, &dquo;Predicting the Cost of Illness: a Comparison ofAlternative Models Applied to Stroke&dquo; (Med Decis Making 1998;18 suppl :S39-S56), by Lipscomb et al. In equation 4a (page S42), the right-hand bracket in thenumerator was misplaced. Equation 4a should read

On page S47, in the first paragraph of the subsection &dquo;Within the Entire Test Sam-

ple,&dquo; the size of the test-sample bootstrap sample for computing the standard errorfor each validation statistic in table 2 is not 10, but rather 15-as noted earlier on

page S43.

These changes affect none of the calculations reported in the paper, nor any ofthe theoretical or empirical conclusions.

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