GENETIC TESTINGVolume 3, Number 1, 1999Mary Ann Liebert, Inc.

The Application of Medical Decision Analysis to GeneticTesting: An Introduction

LAURA M. McCONNELL1 and MARY KANE GOLDSTEIN2

ABSTRACT

The availability of genetic tests to diagnose or predict Alzheimer disease (AD) causes a shift in the way peo-ple think about the condition and how they assess the options available to them. Decision analysis is a quan-titative approach for dealing with the uncertainties inherent in many medical decisions, including decisionsabout genetic testing. Decision analysis does not guarantee a good outcome, but aims to yield better overallaverage results by providing a framework for people to evaluate their options and minimize cognitive biases.We provide an overview of the decision analysis process, including the terms and tools commonly associatedwith it. We also use a recent example to demonstrate one way decision analysis has been applied to geneticsin the medical literature. This paper is an introduction to subsequent papers that explore the specific ques-tion of whether decision analysis is a helpful tool for understanding the uncertainty inherent in probabilisticinformation about genetic risk for AD.

INTRODUCTION

The availability of genetic tests that may help diagnoseor predict Alzheimer disease (AD) causes a shift in the way

people think about the condition and how they assess the op-tions available to them. This leads to a number of potential de-cisions:

• Patients: Should I be tested? What are my values and pref-erences regarding learning about my or my family's geneticrisk for AD? (strategy decisions)

• Health care managers: What procedures should our organi-zation follow when we administer a genetic test for AD? Howand to whom should we report the results? What is the bestway to counsel patients? (operational decisions)

• Government and industry policymakers: Should health care

organizations offer genetic testing for AD? Should health in-surance plans pay for genetic testing for AD? How does thevalue per unit cost for genetic testing compare with that forother health care procedures? (policy decisions)

These decisions, as with many medical decisions, have sev-

eral characteristics that make them particularly complex. First,they involve inherent uncertainty. Information resulting from

predictive apolipoprotein E (APOE) genotyping, for example,will be inconclusive regarding whether and when the personwill develop AD. Second, decisions often demand tradeoffsamong multiple goals. Genetic testing for AD requires that theperson considering testing weigh the possibility of psycholog-ical relief or better-informed life planning against potential fi-nancial, psychological, and social costs. Third, medical deci-sions may entail a number of steps, and the proper sequence ofactions is not clear. For instance, questions remain regarding atwhat point in a clinical work-up APOE genotyping would addvalue, particularly relative to expensive elements such as neu-

roimaging or referral to specialists (McConnell et al., 1999).Finally, medical decisions frequently include input from a va-

riety of perspectives, such as that of medical specialists andfamily members, which may lead to different conclusions aboutthe best course of action.

Decision analysis is one approach to dealing with these kindsof uncertainty. Based on a set of mathematical principles, de-cision analysis is intended to guide people to make consistentlydecisions that are more effective at achieving their desiredgoals. It is an explicit, quantitative technique that is used to

identify a best alternative in a given situation, where the "bestalternative" is understood to be the one that maximizes thechance of a good outcome. Decision analysis is normative,

'Stanford Program in Genomics, Ethics, and Society, Palo Alto, CA 94304.2Health Services Research and Policy, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, and Stanford University School

of Medicine, Palo Alto, CA 94305.

65

66 McCONNELL AND GOLDSTEIN

rather than descriptive, meaning that it does not purport to de-scribe or explain how people actually make decisions. In fact,the purpose of decision analysis is to overcome many of thecognitive biases characteristic of human decision making. Theseinclude (Tversky and Kahneman, 1974):

• Representativeness: People typically evaluate the probabilitythat event A originates from process B, for example, by thedegree to which A resembles B. This neglects the background,or prior probability of the event.

• Availability: People tend to assess the probability of an eventby the ease with which instances or occurrences can bebrought to mind. However, ease of recalling an event may bedue to its severity, rather than to its probability.

• Anchoring and adjustments: People often make estimates bystarting from an initial value, which may be influenced bythe formulation of the problem or the result of a partial com-

putation, and then make insufficient adjustments to yield afinal answer.

For a detailed discussion of these and other factors that affectthe perception and interpretation of uncertain information, see

(Koenig and Silverberg, 1999).Note that there is an important distinction between a good

decision and a good outcome. A good decision process maxi-mizes the chance of a good outcome, but cannot guarantee it.A good outcome is one the decision maker hopes to have, butsince the outcome is not under her control, it is possible to haveeither a good or bad outcome follow a good decision.

The purpose of this paper is to provide a foundation for un-

derstanding the decision analysis and AD case studies presentedby Holtzman and colleagues in this issue of Genetic Testing(Holtzman et al, 1999). We provide an overview of the deci-sion analysis process, including the terms and tools commonlyassociated with it. A more detailed discussion of the applica-tion of decision analysis to medical decision making can befound in a number of textbooks (Clemen, 1991; Owens andSox, 1990; Sox et al, 1988a). We also use a recent example todemonstrate one way decision analysis has been applied to ge-netics in the medical literature.

WHAT IS DECISION ANALYSIS?

Step 1: Identify the decision problemThe first step in decision analysis is to identify the decision

problem—a step that requires that the decision maker also beclearly identified. If a decision is defined as "the commitmentto allocate irrevocably valuable resources," the decision makeris the individual who has the power to commit those resources

(Seiver and Holtzman, 1989).Decision analysis in medical contexts often focuses on the

decision problems faced by the clinician. Many published arti-cles on medical decision analysis contain phrases such as,"... strategies available to the physician" (Pauker and Kassirer,1987), "... uncertain and often ambiguous clinical problemsfacing primary care providers" (Hagen, 1992), and "... deci-sions faced by clinicians in the course of patient care" (Richard-son and Detsky, 1995). Patient input is also mentioned, although

usually as one of many sources informing the clinician's deci-sion. For example, one article describing a clinical servicespecifically for decision consultation on individual patient prob-lems states, "When a consultation request is received, the per-son who will function as the analyst reviews the patient's prob-lem with the attending physician, the house staff, and otherconsultants already involved in the care of the patient. If nec-

essary, the patient also is interviewed" (Plante et al, 1986).Other approaches to decision analysis identify the patient as

the decision maker, under the assumption that "each person,and only that person, has the right to make or to delegate de-cisions about risks to his life or well-being" (Howard, 1984).When framed in this way, the clinician's input is viewed as one

of many sources informing the patient's decision.Identifying the patient as the decision maker respects the eth-

ical principle of autonomy. However, because medical re-

sources are limited, respect for individual autonomy must bebalanced with the ethical principle of justice, which supportsdecisions that are fair and best overall for society. For exam-

ple, decisions on a societal level about the use of genetic testsfor AD require the tradeoff of fixed resources among diagno-sis, treatment, research, and caregiving. Thus, the patient as de-cision maker can select among alternatives that society, oftenacting through the physician, makes available, but he cannothave the right to demand any and all care he wishes, regardlessof medical indication. The model of shared decision making be-tween physician and patient allows for collaboration. The physi-cian can supply expert knowledge about disease processes andthe probabilities of various outcomes given various treatments,while the patient is an expert about his own preferences andconcerns in evaluating the alternatives.

The identity of the decision maker can have profound im-pact on how a decision problem is structured. The initial set ofalternatives considered by either the patient or the physicianmay be unnecessarily narrow (Keeney, 1992). For example,physicians tend to think of alternatives in terms of acceptedmedical practice, while patients may focus on concerns outsideconventional medical care. The process of discussion and col-laboration in structuring the decision model may reveal new op-tions and opportunities that had not previously been considered.

Step 2: Structure an analytic model

The second step in decision analysis is to construct an ana-

lytic model of the decision problem that depicts the decisionmaker's range of options and the possible outcomes of each.An analytic model can be structured in several ways. A deci-sion tree, as shown in Fig. 1, is one common format. A deci-sion tree contains a decision node, usually shown as a square,from which extend branches for each alternative strategy. Achance node, generally shown as a circle, indicates subsequentevents that are not under the control of the decision maker. Thebranches extending from a chance node depict all relevantevents, and ultimately the final outcomes, which could occur

from that point in the model. For example, a decision nodemight depict a decision to test or not test for a particular ge-netic variation. A chance node would depict the probability ofeach possible test result. The test results would be intermedi-ate outcomes, from which the decision maker might then facefurther choices and chances. A list of health states and other

MEDICAL DECISION ANALYSIS 67

Strategy A

Event 1

I Event 2

Outcome 1

Outcome 2

MedicalDecision

Strategy B

Event 3 |

| Event 4 |

Outcome 3

Outcome 4

FIG. 1. A decision tree.

outcomes of importance to the decision maker (e.g., lifestylechoices) would be modeled as the final outcomes.

Another format for structuring an analytic model is an in-fluence diagram. Influence diagrams provide a graphical rep-resentation of a decision problem, depicting decisions as

squares, chance events as ovals, and values as octagons. Theseshapes are connected with dashed or solid arrows showing therelationships among them. A number of influence diagrams are

illustrated in the genetic testing and AD case studies presentedby Holtzman and colleagues.

Influence diagrams have the advantage of growing linearlywith the size of the problem, as compared to decision trees thatgrow exponentially. They can also show more intricate rela-tionships in a visual form that may be more easily understoodby those without mathematical training. On the other hand, de-cision trees can show a greater level of detail. Influence dia-grams can be converted into decision trees, and vice versa, so

both can be used in analyzing the same problem.A methodological tool sometimes used in conjunction with

these analytic models is called a Markov model. Decision mod-els that represent clinical events that occur over time quicklybecome unmanageably large. For example, decisions abouttreatments for cancer may involve risks and benefits that occur

over the patient's lifetime. A Markov model is used to simu-late the transition of theoretical cohorts of patients from onehealth state to another, at different rates, over an extended pe-riod of time (Kassirer et al, 1987).

Step 3: Assign numeric estimates

The third step in decision analysis is to assess the probabil-ity of each chance event, and the utility of each outcome. In thecase of genetic testing for AD, this could mean quantifying boththe chance of finding a particular genetic variation, as well as

the value to the decision maker of being in one of the modeledoutcome states.

Probabilities can be assigned in a number of ways, oftenstarting with a search of published literature. This effort wouldideally uncover randomized controlled trials of patients and sit-uations the same as those being analyzed. However, becausethis is seldom (if ever) the case, gaps in data are filled in by

observational studies, consultation with experts, or the clini-cian's own experience. In addition, existing data can be revisedto more closely reflect the situation of a particular patient.Bayes' theorem is an algebraic formula that combines prior andconditional probabilities, such as disease prevalence, the pa-tient's other medical circumstances, and specific test results, tocalculate a new, or "posterior," probability (Sox et al, 1988b).

A number must also be assigned to each outcome, repre-senting a quantitative measurement of its value to the decisionmaker. Because most people are concerned about how long theywill live, length of life is a common outcome measure. Life ex-

pectancy is estimated from survival curves, and then adjustedfor the patient's specific age and disease. Another common out-come measure is quality of life. Such ratings can come frompublished studies based on groups of patients, or the patienthimself when decision analysis is applied in the case of a sin-gle individual. After all, "when all available knowledge hasbeen applied, the problem is reduced to one of preference"(Matheson and Howard, 1968)—and the patient is the ultimateauthority on preferences, such as length of life, personal andwork life, overall well-being, and economic concerns (Seiverand Holtzman, 1989).

The measure of value of, or preference for, an outcome thatis best grounded in decision theory is known as utility. Utili-ties regarding quality of life—the tradeoff between risk andwell-being—are often assessed using a standard gamble or ref-erence lottery. The individual is presented with a choice be-tween an intermediate sure outcome and a gamble involving thebest and worst outcomes. The probability of winning is adjusteduntil the person is indifferent, or unable to decide between tak-ing the gamble or settling for the sure thing. Again, a detailedillustration of this process can be found in a number of text-books. It is important to note that attitude toward risk variesfrom person to person, and for each person, depending on whatis at stake. A person may be willing to undergo APOE geno-typing for cardiovascular risk assessment, for example, but haveno interest in learning about her risk of developing AD later inlife.

Quantity of life and quality of life are both fundamentallyimportant attributes. They are interrelated in that most peopleare willing to sacrifice some quantity of life to gain quality oflife, or vice versa, as illustrated by the multiple risks people are

willing to take in the course of daily life. A unit of measure

that combines the two is the quality-adjusted life year (QALY).For example, if one clinical strategy under consideration resultsin a life expectancy of 10 years, but those 10 years are lived ata quality of life rated by the patient to have a utility of 0.6 (ona scale of 0 to 1.0), the quality-adjusted life expectancy wouldbe 10 X 0.6, or 6 QALYs.

Step 4: Solve the decision model

The fourth step in decision analysis is to solve the decisionmodel mathematically. A decision tree is solved by a processcalled folding back, as shown in Fig. 2. The utility of the out-come at the end of each branch is multiplied by the probabil-ity ofthat outcome occurring. The products for all branches em-

anating from a chance node are added together to get theprobability-weighted expected utility of that node. The processis then repeated, until an expected utility is arrived at for each

McCONNELL AND GOLDSTEIN

Medical Treatment

TDie from Medical Tx

TreatmentDecision

Probability = 0.0

Survive Medical Tx

Surgery ^^^^T

Probability = 1 -0.0= 1

Die from SurgeryProbability = 0.10

Survive SurgeryProbability = 1-0.10 = 0.9

0 Years

5 Years

0 Years

10 Years

FIG. 2. Solving the decision model.

chance node. When a decision node is reached, the pathwaywith the highest expected utility is assumed to be the one cho-sen, and that value is assigned to the node.

Step 5: Evaluate the decision modelThe fifth step in decision analysis is to evaluate the model

to identify the optimal decision. This may be as simple as look-ing to "the answer" provided by solving the decision tree: Therational decision maker should choose the alternative that yieldsthe greatest expected utility.

This answer can be tested by sensitivity analysis, which in-volves solving the decision tree a number of times, substitut-ing the plausible range of values for different variables. If theoptimal decision changes when a different value is used for one

of the variables, the analysis is said to be "sensitive" to thatvariable. This information provides significant insight intowhich variables are most crucial to the decision and thus war-

rant further exploration. If the optimal decision does not change,the analysis is said to be "robust."

Sensitivity analysis is useful because, in addition to the un-

certainty inherent in the decision being analyzed, there is un-

certainty in the data used to model the decision. Published ev-

idence can be imprecise, often with wide confidence intervals.For example, the association between certain genetic variationsand AD in people from different ethnic and cultural back-grounds is not well understood (Tobin et al, 1999). Perhaps no

published evidence is available and a best guess is used. Util-ity estimates are also subject to uncertainty. Sensitivity analy-sis is a tool used to answer "what if' questions: What if the ge-netic test cannot detect all possible mutations? What if AD isreally more likely than currently thought for people with thepatient's genotype and family history? What if the patient's riskfor AD is really lower than research seems to show?

Step 6: Feedback loopMany decision analyses, particularly those found in pub-

lished medical literature, end with the sensitivity analysis. Theyare intended as references for generic classes of patients, or theydeal with cost-effectiveness or efficacy of one treatment over

another, without reference to a specific individual. However,other approaches to decision analysis, which are philosophi-cally oriented toward the patient, use the answer provided bysolving the decision model and conducting a sensitivity analy-sis as the starting point of a feedback loop. This approach, some-

times referred to as the "Stanford School," is associated withthe Engineering-Economic Systems and Operations ResearchDepartment at Stanford University. During this "appraisalphase," the decision maker will often disagree with the recom-

mendation resulting from the formal decision analysis because"formally analyzing the decision exposes many of the incon-sistencies and lack of focus that made the decision difficult inthe first place" (Seiver and Holtzman, 1989). However, such

Model

Formulate

Recommendation

Appraise

Refine

FIG. 3. Decision analysis cycle (Seiver and Holtzman, 1989).

MEDICAL DECISION ANALYSIS 69

disagreement is beneficial, because it exposes inconsistenciesin either the decision maker's understanding of his decision or

his logic (Seiver and Holtzman, 1989). Allowing the decisionmaker to react to the formal prescription creates a closed loop,called a decision analysis cycle, as shown in Fig. 3. By work-ing through this cycle, it is intended that the decision makerwill either gain enough insight to agree with the recommendedstrategy, or will explore with the decision analyst the most sen-

sitive issues to produce increasingly refined models.Thus, the decision analysis cycle is an iterative process meant

to transform decision analysis from an "answer engine" into a

framework for learning and insight. In the matter of genetictesting, for example, many people will not have pre-existingpreferences. The cyclical process allows them to explore theoutcomes of different decisions. Rather than simply providinga numeric answer, it aims to focus attention and provide insightinto the uncertainties, objectives, and tradeoffs involved in thesituation. The ultimate goal is to achieve clarity of action nec-

essary to bring about commitment to action.

DECISION ANALYSIS AND GENETICS INMEDICAL LITERATURE

Medical decision analyses are conducted for individual pa-tients, for generic classes of patients, and in response to healthpolicy questions. Several types of decision problems have beenexplored in the medical literature, such as the value of preven-tion (Zalkind and Shachtman, 1980), whether or not to treat (El-stein et al., 1986; van Crevel et al, 1986; Hillner and Smith,1991), choosing among different treatments (Johnson et al,1992; Renting et al, 1993; Brundage et al., 1997), and cost ef-fectiveness (Gage et al., 1995; Owens et al, 1995; Owens etal., 1997). Below, we summarize a recent article that addresseda decision problem in genetics. This example illustrates thecomplexities of some of the steps in the decision analyticprocess—particularly when little or no clinical data are avail-able, which may often be the case in the context of genetics—and the resulting limitations of the model.

A 1997 study used a Markov model to estimate the effect ofprophylactic surgery on life expectancy among women whocarry mutations in the breast cancer genes BRCA1 or BRCA2(Schräg et al., 1997). The results of this analysis were that, on

average, 30-year-old women who carry BRCA1 or BRCA2 mu-

tations gain from 2.9 to 5.3 years of life expectancy from pro-phylactic mastectomy, and from 0.3 to 1.7 years of life ex-

pectancy from prophylactic oophorectomy.The option of prophylactic surgery is an example of a deci-

sion that must be made in face of extreme uncertainty. The au-

thors describe the intricacy of decision:

The magnitude of the potential benefit depends on the riskof cancer associated with specific mutations, the progno-sis of the tumors in carriers of the mutations, and the ex-

tent to which relief of anxiety could result from surgicalprophylaxis. These benefits must be weighed against an

array of potential costs including surgical complicationsand the impact of mastectomy or oophorectomy on

women's self-image, as well as on their sexual and repro-ductive function (Schräg et al., 1997).

The investigators modeled a range of estimates for the inci-dence of cancer, because most data so far come only from fam-ilies with a striking history of cancer. Very few studies havebeen done on the efficacy of prophylactic surgery, particularlyamong women with genetic mutations, and studies that havebeen done include women with different indications for surgery,and women who underwent different kinds of surgical proce-dures. Therefore, the investigators estimated the risk reductionachieved by prophylactic mastectomy by consultation with ex-

pert clinicians, and for prophylactic oophorectomy by one studyof women carrying BRCA1 mutations. They assumed that car-

riers of BRCA1 or BRCA2 mutations are likely to undergo reg-ular mammography and frequent physical exams. They also as-

sumed, due to lack of evidence to the contrary, that theprognosis for carriers with breast cancer was the same, stagefor stage, as that for women at usual risk.

This study is not a full decision analysis, and is intended onlyto provide a starting point for personal decisions by estimatingthe effects of prophylactic surgery on life expectancy. An in-dividual woman would need to work with clinicians familiarwith her circumstances to tailor the probability of each chanceevent. She would also need to add her own alternatives, as wellas her own values and preferences concerning loss of breastsor ovaries versus possible psychological relief and prolonga-tion of life. It is crucial that physicians and patients understandthese limitations and the theoretical nature of the underlying as-

sumptions used to construct this Markov model.

CONCLUSION

Medical decision analysis is a quantitative approach for deal-ing with uncertainty. These approaches do not guarantee a goodoutcome, but aim to yield better overall average results by pro-viding a framework for people to evaluate their options andminimize cognitive biases. Here we have provided an overviewof the decision analysis process, as well as presented an exam-

ple of one way it has been applied in the realm of genetics. Inthe next two papers, Holtzman et al. and Kaplan explore thespecific question of whether decision analysis is a helpful toolfor understanding the uncertainty inherent in probabilistic in-formation about genetic risk for AD.

ACKNOWLEDGMENTS

The authors wish to thank Eric Bickell of the Decisions andEthics Center at Stanford University, for supplying informationabout the "Stanford School" of decision analysis.

REFERENCES

BRUNDAGE, M.D., GROÓME, P.A., FELDMAN-STEWART, D., etal. (1997). Decision analysis in locally advanced non-small-cell lungcancer: Is it useful? J. Clin. Oncol. 15, 873-883.

CLEMEN, R.T. (1991). Making Hard Decisions: An Introduction toMedical Decision Analysis. (Duxbury, Belmont, CA).

ELSTEIN, A.S., HOLZMAN, G.B., RAVITCH, M.M., et al. (1986).Comparison of physicians' decisions regarding estrogen replacement

70 McCONNELL AND GOLDSTEIN

therapy for menopausal women and decisions derived from a deci-sion analytic model. Am. J. Med. 80, 246-258.

FLEMING, C, WASSON, J.H., ALBERTSEN, P.C., et al. (1993). Adecision analysis of alternative treatment strategies for clinically lo-calized prostate cancer. J. Am. Med. Assn. 269, 2650-2658.

GAGE, B.F., CARDINALLI, A.B., ALBERS, G.W., and OWENS,D.K. (1995). Cost-effectiveness of warfarin and aspirin for prophy-laxis of stroke in patients with nonvalvular atrial fibrillation. J. Am.Med. Assn. 274, 1839-1845.

HAGEN, M.D. (1992). Decision analysis: A review. Fam. Med. 24,349-354.

HILLNER, B.E., and SMITH, T.J. (1991). Efficacy and cost effec-tiveness of adjuvant chemotherapy in women with node-negativebreast cancer: A decision-analysis model. N. Engl. J. Med. 324,160-168.

HOLTZMAN, S., OZANNE, E., CARONE, B., et al. (1999). Decisionanalysis and Alzheimer disease: three case studies. Genet. Testing 3,71-83.

HOWARD, R.A. (1984). On fates comparable to death. Manage. Sei.30, 547-573.

JOHNSON, N., LILFORD, R.J., JONES, S.E., et al. (1992). Using de-cision analysis to calculate the optimum treatment for microinvasivecervical cancer. Br. J. Cancer. 65, 717-722.

KASSIRER, S.J., MOSKOWITZ, A.J., LAU, J., and PAUKER, S.G.(1987). Decision analysis: A progress report. Ann. Int. Med. 106,275-291.

KEENEY, R.L. (1992). Value-Focused Thinking: A Path to CreativeDecisionmaking. (Harvard University Press, Cambridge, MA).

KOENIG, B.K., and SILVERBERG, H.L. (1999). Understanding prob-abilistic risk in predisposition genetic testing for Alzheimer disease.Genet. Testing 3, 55-63.

MATHESON, J.E., and HOWARD, R.A. (1968). An Introduction toDecision Analysis. (Stanford University, Department of Engineering-Economic Systems).

McCONNELL, L.M., SANDERS, G., and OWENS, D.K. (1999). Eval-uation of genetic tests: APOE genotyping for the diagnosis ofAlzheimer disease. Genet. Testing 3, 47-53.

OWENS, D.K., and SOX, H.C. (1990). Medical decision-making: prob-abilistic medical reasoning. In Medical Informatics: Computer Ap-plications in Health Care. E.H. Shortliffe and L.E. Perreault (eds.).(Addison-Wesley, Menlo Park, CA) pp. 70-116.

OWENS, D.K., HARRIS, R.A., SCOTT, P.M., and NEASE, R.F.(1995). Screening surgeons for HIV infections. Ann. Intern. Med.122, 641-652.

OWENS, D.K., SANDERS, G.D., HARRIS, R.A., et al (1997). Cost-effectiveness of implantable cardioverter defibrillators relative toamiodarone for prevention of sudden cardiac death. Ann. Intern.Med. 126, 1-12.

PAUKER, S.G., and KASSIRER, J.P. (1987). Decision analysis. N.Engl. J. Med. 316, 250-257.

PLANTE, D.A., KASSIRER, J.P., ZARIN, D.A., and PAUKER, S.G.(1986). Clinical decision consultation service. Am. J. Med. 80,1169-1176.

RICHARDSON, W.S., and DETSKY, A.S. (1995). Users' guides tothe medical literature. VII. How to use a clinical decision analysis.A. Are the results of the study valid? J. Am. Med. Assn. 273,1292-1295.

SCHRÄG, D., KUNTZ, K.M., GARBER, J.E., and WEEKS, J.C.(1997). Decision analysis—effects of prophylactic mastectomy andoophorectomy on life expectancy among women with BRCA1 or

BRCA2 mutations. N. Engl. J. Med. 336, 1465-1471.SEIVER, A., and HOLTZMAN, S. (1989). Decision analysis: A frame-

work for critical care decision assistance. Int. J. Clin. Monit. Com-put. 6, 137-156.

SOX, H.C, BLATT, M.A., HIGGINS, M.C., and MARTON, K.I.(1988a). Medical Decision Making. (Butterworths, Boston, MA).

SOX, H.C, BLATT, M.A., HIGGINS, M.C., and MARTON, K.I.(1988b). Understanding new information: Bayes' theorem. In Med-ical Decision Making. (Butterworths, Boston, MA) pp. 67-101.

TOBIN, S.L., CHUN, N., POWELL, T, and McConnell, L.M. (1999).The genetics of Alzheimer disease and the appreciation of molecu-lar tests. Genet. Testing 3, 37^t5.

TVERSKY, A., and KAHNEMAN, D. (1974). Judgement under un-

certainty: Heuristics and biases. Science 185, 1124-1130.VAN CREVEL, H., HABBEMA, J.D., and BRAAKMAN, R. (1986).

Decision analysis of the management of incidental intracranial sac-

cular aneurysms. Neurology 36, 1335-1339.ZALKIND, D.L., and SHACHTMAN, R.H. (1980). A decision analy-

sis approach to the swine influenza vaccination decision for an in-dividual. Med. Care 18, 59-72.

Address reprint requests to:Attn: Dr. Barbara Koenig

Stanford Program in Genomics, Ethics, and Society701 Welch Road, Suite 1105

Palo Alto, CA 94304