This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Armstrong, K. Articles by Schwartz, J. S. Search for Related Content PubMed PubMed Citation Articles by Armstrong, K. Articles by Schwartz, J. S. Social Bookmarking                            What's this?

Individualized Survival Curves Improve Satisfaction With Cancer Risk Management Decisions in Women With BRCA1/2 Mutations

From the Department of Medicine; Abramson Family Cancer Research Institute; and Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and Veterans Affairs Health Services Research and Development Center for Practice Management and Outcomes Research, Veterans Affairs Ann Arbor Health Care System, Ann Arbor, MI

Address reprint requests to Katrina Armstrong, MD, MSCE, 1204 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104-6021; e-mail: karmstro{at}mail.med.upenn.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
PURPOSE: Women with BRCA1/2 mutations are faced with complex decisions about breast and ovarian cancer risk management. This study was conducted to determine the effect of a tailored decision support system (DSS) that provides individualized survival and cancer incidence curves specific to expected outcomes of alternative management strategies.

PATIENTS AND METHODS: This was a double-blind, randomized controlled trial of 32 women with BRCA1/2 mutations. Primary outcome measures were decision satisfaction, cancer anxiety, perceptions of cancer risk given alternative management strategies, and management decisions.

RESULTS: Twenty-seven women completed a 6-week follow-up. Women in the intervention arm (n = 13) reported significantly higher decision satisfaction at follow-up than women in the control arm (n = 14; adjusted mean difference, 9.7; P < .0005). The effect of the DSS was greater among women with low cancer anxiety at baseline than women with high cancer anxiety at baseline (P = .01 for interaction). However, the DSS did not significantly alter cancer anxiety at follow-up, perceptions of cancer risk given alternative management strategies, or management decisions.

CONCLUSION: The presentation of individualized survival and incidence curves for alternative management options improves satisfaction about cancer risk management decisions among women with BRCA1/2 mutations without increasing anxiety or changing management decisions. The benefit of the DSS is greatest among women with relatively low cancer-related anxiety at baseline.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
Women with germline BRCA1/2 mutations are at significantly increased risk of breast and ovarian cancer.¹ Although several options are available for managing this risk, each involves significant trade-offs. For example, prophylactic mastectomy reduces the risk of breast cancer by more than 90% but requires a major surgical procedure, with many women opting for reconstructive surgery as well. Use of tamoxifen for 5 years reduces the incidence of breast cancer by as much as 49%² but increases the risk of endometrial cancer and venous thromboembolism by two-fold or more.^3,4 Some of these options may have additional competing effects on health risks; premenopausal prophylactic oophorectomy reduces the risk of ovarian cancer but may increase the risk of osteoporosis and heart disease, and hormone replacement therapy after oophorectomy reduces the risk of osteoporosis but may increase the risk of breast cancer.^5-11 Furthermore, the absolute effect of any intervention depends on the size of the competing cancer risks and varies over time. Because of these trade-offs and competing effects, decisions about cancer risk reduction are difficult for many women with BRCA1/2 mutations.

The use of decision support systems (DSSs) has been proposed as a promising method for improving medical decision making, particularly in settings with complex information and important trade-offs. DSSs vary in the amount of information that is provided, the inclusion of patient utilities or preferences for health outcomes, the level of individual tailoring, and the overall complexity of the system.¹² On one end of the spectrum, complex and comprehensive CD-ROM programs have been developed and tested for decisions such as prostate-specific testing.¹³ In general, these programs have focused on using computer technology to provide extensive information in a comprehensible format rather than developing novel methods of presenting or organizing risk information. On the other end of the spectrum, relatively simple, noncomputerized information presentation systems have been developed for decisions such as adjuvant chemotherapy for breast cancer.¹⁴ In general, these interventions have focused on testing novel methods for presenting trade-offs or risk information to improve decision making rather than providing comprehensive information. Each of these approaches has been shown to improve decision satisfaction in selected settings.^15,16

Here, we report the results of a randomized trial of a new type of DSS. This DSS uses computer technology to present complex risk information about the expected outcomes of alternative management options in the format of individualized survival and cancer incidence curves. These curves present extensive information about risk over time and can be used to combine information about competing risks into an overall assessment of the effect of any intervention on mortality. The ability to include multiple cancer risks and multiple interventions may be particularly important for complex, multifaceted decisions such as those faced by women with BRCA1/2 mutations. Furthermore, survival and cancer incidence curves present risk information graphically rather than numerically, which may overcome patients' unfamiliarity with numeric information and increase satisfaction and comprehension. We have previously demonstrated that survival and cancer incidence curves can be relatively easily understood by individuals in the general population.^17,18 In this study, we hypothesized that providing individualized information about the expected outcomes of cancer risk management interventions using survival and incidence curves would be associated with improved decision satisfaction without changing management decisions. In addition, because of concerns that visual images of expected mortality may create anxiety that would interfere with decision making, we hypothesized that the impact of the decision support intervention would be lowest (and potentially even negative) among women with high cancer anxiety at baseline.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
We conducted a randomized controlled trial of a DSS for women with BRCA1/2 mutations. The study protocol was approved by the Institutional Review Board at the University of Pennsylvania.

Intervention Development of BRCA DSS
The BRCA DSS used a Markov model (DATA 4.0; Treeage Software, Williamstown, MA) outputted to an Excel spreadsheet to calculate and graphically display individualized survival and cancer incidence curves. The model includes 1,256 health states representing all possible combinations of the primary health outcomes affected by the interventions in question.¹⁹ The probability of transition between health states was obtained from the medical literature and is individualized based on the participant's age, coronary heart disease risk factors, and medical history. The following six alternative management options were included: (1) breast cancer screening only; (2) prophylactic mastectomy; (3) prophylactic oophorectomy; (4) tamoxifen for 5 years; (5) continuous raloxifene after menopause (surgical or nonsurgical); and (6) hormone replacement therapy after menopause (surgical or nonsurgical). Hormone replacement therapy was assumed to contain estrogen and progesterone in women who had not undergone hysterectomy and estrogen only in women who had undergone hysterectomy. However, because this study was conducted before the recent publication of the estrogen-only arm of the Women's Health Initiative, these combinations were assumed to have equal effects on breast cancer, endometrial cancer, and coronary heart disease.^20-22 Combinations of management options were included, except for the option of undergoing prophylactic mastectomy and taking tamoxifen. Transition probabilities were updated three times over the course of the trial because of the following new data: effects of prophylactic oophorectomy on breast cancer risk,²³ effect of prophylactic oophorectomy on ovarian cancer risk,⁵ and effect of hormone replacement therapy on cardiovascular risk.¹¹

The complete BRCA DSS included a survival and cancer incidence curve comprehension exercise, individualized overall survival curves, and individualized breast cancer incidence curves for alternative management options and combinations of options. All information was assembled in a binder for the participant to keep. Survival and cancer incidence curves were printed on translucent paper so they could be overlaid on the baseline curve (breast cancer screening only) and compared with each other. For example, a woman interested in the difference in survival between prophylactic mastectomy, tamoxifen, and breast cancer screening could overlay the prophylactic mastectomy and tamoxifen curves on the baseline curve. The content of the DSS was tailored to the participant's past medical history by adjusting the transition probabilities in the Markov model and by presenting only the survival and incidence curves for options that remained relevant. For example, a woman who had already undergone bilateral oophorectomy would have a lower risk of breast and ovarian cancer, and the option of a future prophylactic oophorectomy would not be presented in the DSS. An example of the survival curves for a hypothetical patient is shown in Figure 1. The full contents of the DSS are available from the authors.

View larger version (18K): [in this window] [in a new window]   Fig 1. Hypothetical example of survival curves in a decision support system. The example is for a hypothetical, 49-year-old, nonsmoking patient without a history of diabetes, hypercholesterolemia, hypertension, heart disease, or breast or ovarian cancer. (A) Baseline; (B) prophylactic mastectomy; (C) prophylactic oophorectomy; (D) prophylactic oophorectomy and prophylactic mastectomy; (E) raloxifene.

Random Assignment and Blinding
Random assignment was conducted using sequential sealed envelopes containing randomly generated group assignments. These envelopes were kept by the study statistician. At the time a patient agreed to participate, the statistician was contacted, the next envelope was opened, and the randomization code was assigned. Because of the nature of the intervention, the research coordinator who generated and administered the DSS for each participant was not blinded to group assignment. However, the participants, other study investigators, and the interviewer who conducted the outcome assessment were blinded.

Delivery of the Intervention
Participants in both the intervention and control arms had a one-on-one meeting with the research study coordinator that included a structured review of an educational booklet and completion of several questionnaires. The educational booklet was developed for the trial and reviewed general information about the cancer risks associated with BRCA1/2 mutations and the alternative management options. In addition, for participants randomly assigned to the intervention arm, the BRCA DSS was given to the participant and briefly reviewed by the research coordinator. Any clinical questions that arose at the time of the meeting were referred to the clinical staff at the Cancer Risk Evaluation Program. Meetings were scheduled as soon as possible after disclosure of test results. Women were reimbursed $20 for their participation at the time of attendance.

Data Collection
Data were collected at three time points. At the time of enrollment, a brief mailed survey collected information that was necessary for tailoring the DSS, including coronary heart disease risk factors, prior cancer history, prior surgical history, and level of interest in alternative management options. At the beginning of the meeting with the research coordinator, a second self-administered survey was administered to collect baseline characteristics. Outcomes were assessed 6 weeks after participation by telephone interview because our clinical experience suggested that this time period would capture the short-term impact of the DSS.

Baseline Characteristics
Sociodemographic characteristics, cancer anxiety, information-seeking preference, decision-making preference, and perceptions of cancer risk with and without intervention were measured at baseline. Cancer anxiety was measured using the Intrusion Subscale of the Revised Impact of Event Scale (RIES), which has been used in prior studies of women from hereditary breast cancer families.^24,25 Information-seeking preference and decision-making preference were measured using the two subscales of the Autonomy Preference Index.²⁶ Perception of personal cancer risk given alternative management strategies was measured by asking participants to rate their lifetime risk of developing breast and ovarian cancer between 0% and 100% for a series of scenarios representing different combinations of management strategies.

Outcomes
The primary outcome of the trial was decision satisfaction. Secondary outcomes included perceptions of cancer risk, anxiety and depression, and behavior and behavioral intentions. Decision satisfaction was measured with a 12-item scale that combined items from the Decisional Conflict Scale with the Satisfaction With Decision Scale.^27,28 These items were selected to avoid redundancy while including constructs of decisional conflict and decision satisfaction. The revised scale had high internal consistency (Cronbach's = .86). In addition, using principal components analysis, we identified three components with Eigen values of 5.38, 1.92, and 1.70. Each item had a rotated loading of more than 0.50 on one of the components (Table 1). On the basis of these loadings, we constructed the following three subscales: Decision Resolution (Cronbach's = .88), Decision Certainty (Cronbach's = .86), and Informed Decisions (Cronbach's = .74). The Decision Certainty subscale corresponded to the Decision Uncertainty subscale of the Decisional Conflict Scale. We also calculated separate scores for the Satisfaction With Decision Scale and the seven items from the Decisional Conflict Scale, which had reasonable internal consistency in our sample (Cronbach's = .76 for the Satisfaction With Decision Scale and = .79 for the Decisional Conflict Scale).

Sample Size and Power
A sample size of 14 participants in each arm was determined to provide 80% power for detecting a 5-point difference in the decision satisfaction score (range, 0 to 48), assuming a standard deviation of 4.5 and a type I error of 0.05.

Statistical Analysis
Baseline characteristics of the two arms were compared using Fisher's exact test for categoric variables and t test for continuous variables. The effects of the intervention were assessed by comparing follow-up measures between the two arms using t tests for measures of decision satisfaction, cancer anxiety, and cancer risk perception and Fisher's exact test for decisions about each management alternatives. Multivariate linear regression was conducted to adjust the effect of the DSS on decision satisfaction for baseline differences between groups. Variables were entered into the model if they differed between the arms (P < .10) or were associated with decision satisfaction in univariate analyses (P < .10). In addition, secondary analyses were conducted to investigate the effect of the intervention on the domains of decision satisfaction identified by principal components analysis. Scores on each of the three domains (decision certainty, decision resolution, and informed decision making) were standardized to a mean of 10 and a standard deviation of 1 to facilitate comparison of effect sizes.

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
Thirty-two women were enrolled onto the trial, and 27 women completed follow-up (13 women in the DSS arm and 14 women in the control arm). Three women dropped out because they were either unable to travel to the site or commit to a time for the research coordinator to travel to them; one woman was no longer eligible for the study because she underwent a prophylactic mastectomy and oophorectomy before the initial interview; and one woman was not able to be reached to complete the follow-up questionnaire. Patient characteristics are listed in Table 2. None of the women reported having diabetes, angina, high blood pressure, or a prior myocardial infarction or were taking a medication for high cholesterol or heart disease. Almost half of the participants had been diagnosed with breast cancer before undergoing BRCA1/2 testing, of whom three had undergone mastectomy and four had taken tamoxifen before enrollment. Approximately 40% of women had undergone bilateral oophorectomy before enrollment.

Cancer Risk Perception
Women in the DSS arm did not differ significantly from women in the control arm in their estimates of cancer risk in the alternative management scenarios, although there was a trend towards women in the DSS arm reporting higher estimates of cancer risk (Table 5). These differences remained after adjustment for prior cancer diagnosis, prior prophylactic surgery, and baseline estimates of the effect of interventions on cancer risk.

Management Decisions
Women in the DSS arm did not differ from women in the control arm in their decisions about cancer risk management strategies. Thirty-three percent of women in the intervention arm and 53% of women in the control arm were considering undergoing prophylactic mastectomy in the future. One woman in each arm had had or had scheduled a prophylactic mastectomy. Among the women who had not undergone oophorectomy before enrollment, 50% of women in the intervention arm and 38% of women in the control arm had undergone or had scheduled a prophylactic oophorectomy at the time of follow-up. All remaining women in both arms were planning to undergo oophorectomy in the future. Among women who were not on tamoxifen at the time of enrollment, 30% of women in the DSS arm and 9% of women in the control arm were planning to take tamoxifen in the future. None of these differences reached statistical significance (all P > .20).

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
This study demonstrates that the use of an individualized DSS that uses survival curves to provide information about expected outcomes of alternative management strategies is associated with increased decision satisfaction without increased cancer anxiety among women with BRCA1/2 mutations. Furthermore, the DSS had the greatest benefit among women with relatively low cancer anxiety at baseline. These findings extend the current literature about decision support in several ways.

First, because cancer risk management decisions for BRCA1/2 mutation carriers present some relatively unique challenges, the finding that providing individualized survival and incidence curves can improve decision satisfaction among women with BRCA1/2 mutations is both important and reassuring. Multiple studies have examined decision support for cancer screening, prevention, and treatment decisions, generally demonstrating that decision support increases knowledge and decision satisfaction in these settings.^30-34 However, the decisions about cancer risk management faced by women with BRCA1/2 mutations are more complex than deciding about a single intervention that affects a single cancer risk. Germline mutations in BRCA1/2 are associated with an increased risk of several different cancers,³⁵ which increases the number of decisions that must be made and complicates the assessment of the benefit of any one intervention. Many women who are found to have a BRCA1/2 mutation may have been previously diagnosed with cancer and may have undergone mastectomy and/or oophorectomy, which are events that will modify the absolute benefit of other preventive interventions. In addition, because the level of urgency is far lower than with a cancer diagnosis, decisions may evolve over time, making it difficult to determine the appropriate time period for assessing the effect of any DSS. However, as with cancer treatment decisions, decisions about risk reduction in BRCA1/2 carriers are crucial because the lifetime risk of developing cancer is high (75% to 90% by age 70 years).^36,37 In the only other published trial of decision support for women with BRCA1/2 mutations, there were no differences in measures of decision process at 1 month, but small differences in the strength of preference and agreement with having weighed treatment choices at 7 months.³⁸ However, this study was conducted in a Dutch population and used bar graphs to present a single value of quality-adjusted life expectancy for each management option. It is possible that the greater early effect of decision support in the current study derives from the use of survival and incidence curves to present risk information graphically, but it may also represent chance variation or variation in populations and outcome measures.

Second, the differential effect of the DSS on the domains of decision satisfaction provides new information about the sensitivity of these components to intervention. Using the established scales of decisional conflict and decision satisfaction, the decision support intervention had a positive effect on decisional conflict but not on decision satisfaction. This finding is supported by a published systematic review of 17 trials, which found that decision aids significantly reduced decisional conflict scores (weighted mean difference, –0.3 out of 5; 95% CI, 0.1 to 0.4), but did not significantly affect decision satisfaction scores (weighted mean difference, 0.6 out of 100; 95% CI, –3 to 4).¹² Using novel decision satisfaction domains identified in the current study, we found that the DSS had a larger effect on measures of decision resolution and decision certainty than on measures assessing knowledge or perceptions of being informed. There are several possible reasons for this pattern of results. The DSS used in this trial did not focus on providing comprehensive information but on a novel method of presenting complex, individualized information. These results may reflect this focus. However, medical information is increasingly available to the lay community,³⁹ and the lack of effect on perceptions of being informed might be attributable to greater access to relevant information from sources other than the doctor or the DSS. This may be particularly true for high-stakes decisions where patients are more likely to actively seek information.

Third, to our knowledge, this is the first study to demonstrate that the benefit of a decision support intervention varied according to the patient's baseline level of anxiety. The greater benefit among women with lower levels of cancer anxiety is consistent with our a priori hypothesis that survival curves may be less beneficial in women with more anxiety about their risk of dying from cancer. Importantly, although concerns were raised that the DSS might even be detrimental in women with high levels of cancer anxiety, the decision support intervention was associated with increased decision satisfaction among all quartiles of cancer anxiety. This interaction also highlights the growing recognition that different patients may respond to different types of decision support and that one type of intervention, even when the content is individualized, is unlikely to be optimal for all individuals. Although we focused on cancer anxiety as a modifying factor, characteristics, such as numeracy, literacy, and cultural background, may also identify subgroups for whom certain types of decision support are more or less beneficial.⁴⁰ Understanding these interactions and how and when to tailor the use of decision support to individual characteristics is an important next step in research in this area.

Fourth, this study highlights several general methodologic issues related to the development and implementation of decision support. In general, evidence changes rapidly in controversial, high-stakes areas in medical care, which are the same areas that often are appropriate for decision support. Thus, even when the ability to incorporate new evidence is planned into the DSS, as was done in this trial, the perceived utility of decision support may change substantially over time as new evidence emerges that affects the level of uncertainty about the optimal decision. For instance, in the current trial, the publication of evidence demonstrating a greater than 95% reduction in ovarian cancer risk with prophylactic oophorectomy greatly reduced the sense of uncertainty about the use of prophylactic oophorectomy for women with BRCA1/2 mutations and reduced the level of interest in decision support among women with BRCA1/2 mutations. This effect has been particularly evident in trials of decision support for decisions about postmenopausal hormone replacement therapy, where the publication of the Women's Health Initiative quickly led to recommendations against the use of combined hormone replacement therapy and prevented the completion of decision support trials. Importantly, this effect occurs even when the content in the DSS can be updated to include the new information. Thus, randomized controlled trials of traditional, content-based approaches to decision support are likely to be high risk because the perceived need for decision support may change substantially over the course of the trial. New decision support strategies are needed, potentially including the development of more generalizable methods to help patients value trade-offs, understand risk, or anticipate emotional reactions outside of any specific content area. Such strategies may be able to be moved quickly from one area of controversial, high-stakes decision making to another, as dictated by the available evidence. Studies of the effectiveness of this type of decision support would not require focusing on a single decision or clinical situation but could include a range of appropriate situations that might even change over time according to the clinical need. As demonstrated by this study, the use of survival curves to present risk information over time is a promising strategy that should be tested in this new model.

This study had several limitations. Because of the focus on women with BRCA1/2 mutations who were considering alternative cancer risk reduction interventions, the number of patients who were eligible for the trial was small. The small sample size provided relatively little power to examine differences in management choices or anxiety, resulting in several trends that did not meet statistical significance. In addition, a significant proportion of the participants had already undergone bilateral oophorectomy, limiting the decisions that were relevant for their situation. However, although we did find that having undergone an oophorectomy was associated with lower decision satisfaction scores at follow-up, there was no interaction between the DSS and prior oophorectomy, suggesting that these women found the DSS useful for their breast cancer risk reduction decisions. We examined multiple outcomes, making it possible that some findings reached statistical significance by chance. Although this is less likely for our primary outcome, it is an important consideration in interpretation of the interaction with baseline anxiety.⁴¹ The decision satisfaction measure was developed from the Decisional Conflict Scale or the Satisfaction With Decision Scale and had high internal consistency. However, because it was a novel measure, there are fewer data supporting its validity than for the two existing scales. Our sample was all white and relatively well educated, limiting the generalizability of our findings to other racial or socioeconomic groups. Furthermore, we studied a specific decision support intervention in a specific clinical setting. As suggested by the Dutch study, the benefits of this DSS may not generalize to forms of decision support for women with BRCA1/2 mutations. Similarly, this type of decision support may not prove beneficial in other clinical settings. However, we believe that the key element of this BRCA DSS (ie, the use of an individualized Markov model to generate survival and cancer incidence curves) is particularly suited to the management of genetic susceptibility and has great potential to improve decision satisfaction in this important and growing area.

Women with BRCA1/2 mutations face difficult decisions about cancer risk reduction. Demonstrating that decision support improves decision satisfaction among women with BRCA1/2 mutations is a small step towards helping this situation. Questions about the optimal characteristics of decision support remain, particularly for women with higher levels of anxiety at baseline. Even if these questions can be resolved, making decision support available to all women who are found to carry a mutation in BRCA1/2 will be challenging. This study provides a foundation for these important next steps.

	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
The authors indicated no potential conflicts of interest.

	NOTES

 
Supported by Department of Defense Breast Cancer Research Program DAMD17-98-1-8234.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
1. Warmuth MA, Sutton LM, Winer EP: A review of hereditary breast cancer: From screening to risk factor modification. Am J Med 102:407-415, 1997[CrossRef][Medline]

2. Fisher B, Costantino JP, Wickerham D, et al: Tamoxifen for prevention of breast cancer: Report of the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 Study. J Natl Cancer Inst 90:1371-1388, 1998[Abstract/Free Full Text]

3. Fisher B, Costantino JP, Redmond CK, et al: Endometrial cancer in tamoxifen-treated breast cancer patients: Findings from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14. J Natl Cancer Inst 86:527-537, 1994[Abstract/Free Full Text]

4. Meier CR, Jick H: Tamoxifen and risk of idiopathic venous thromboembolism. Br J Clin Pharmacol 45:608-612, 1998[CrossRef][Medline]

5. Rebbeck TR, Lynch H, Neuhausen SL, et al: Prophylactic oophorectomy in carriers of BRCA1 or BRCA2 mutations. N Engl J Med 346:1616-1622, 2002[Abstract/Free Full Text]

6. Chen CL, Weiss NS, Newcomb P, et al: Hormone replacement therapy in relation to breast cancer. JAMA 287:734-741, 2002[Abstract/Free Full Text]

7. Theriault RL, Sellin RV: Estrogen-replacement therapy in younger women with breast cancer. J Natl Cancer Inst Monogr 16:149-152, 1994[Medline]

8. Struewing JP, Watson P, Easton DF, et al: Prophylactic oophorectomy in inherited breast/ovarian cancer families. J Natl Cancer Inst Monogr 17:33-35, 1995[Medline]

9. Grundsell H, Ekman G, Gullberg B, et al: Some aspects of prophylactic oophorectomy and ovarian carcinoma. Ann Chir Gynaecol 70:36-42, 1981[Medline]

10. Torgerson DJ, Bell-Syer SE: Hormone replacement therapy and prevention of nonvertebral fractures: A meta-analysis of randomized trials. JAMA 285:2891-2897, 2001[Abstract/Free Full Text]

11. Writing Group for the Women’s Health Initiative Investigators: Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the Women's Health Initiative randomized controlled trial. JAMA 288:321-333, 2002[Abstract/Free Full Text]

12. O'Connor AM, Rostom A, Fiset V, et al: Decision aids for patients facing health treatment or screening decisions: Systematic review. BMJ 319:731-734, 1999[Abstract/Free Full Text]

13. Frosch DL, Kaplan RM, Felitti VJ: A randomized controlled trial comparing internet and video to facilitate patient education for men considering the prostate specific antigen test. J Gen Intern Med 18:781-787, 2003[CrossRef][Medline]

14. Levine MN, Gafni A, Markham B, et al: A bedside decision instrument to elicit a patient's preference concerning adjuvant chemotherapy for breast cancer. Ann Intern Med 117:53-58, 1992[Abstract/Free Full Text]

15. Gustafson DH, Hawkins R, Pingree S, et al: Effect of computer support on younger women with breast cancer. J Gen Intern Med 16:435-445, 2001[CrossRef][Medline]

16. O'Connor AM, Tugwell P, Wells GA, et al: Randomized trial of a portable, self-administered decision aid for postmenopausal women considering long-term preventive hormone therapy. Med Decis Making 18:295-303, 1998[Abstract/Free Full Text]

17. Armstrong K, FitzGerald G, Schwartz JS, et al: Using survival curve comparisons to inform patient decision making: Can a practice exercise improve understanding? J Gen Intern Med 16:482-485, 2001[CrossRef][Medline]

18. Armstrong K, Schwartz JS, Fitzgerald G, et al: Effect of framing as gain versus loss on understanding and hypothetical treatment choices: Survival and mortality curves. Med Decis Making 22:76-83, 2002[Abstract/Free Full Text]

19. Armstrong K, Schwartz JS, Randall T, et al: Hormone replacement therapy and life expectancy after prophylactic oophorectomy in women with BRCA1/2 mutations: A decision analysis. J Clin Oncol 22:1045-1054, 2004[Abstract/Free Full Text]

20. Collaborative Group on Hormonal Factors in Breast Cancer: Breast cancer and hormone replacement therapy: Collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Lancet 350:1047-1059, 1997[CrossRef][Medline]

21. Hulley S, Grady D, Bush T, et al: Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women: Heart and Estrogen/Progestin Replacement Study (HERS) Research Group. JAMA 280:605-613, 1998[Abstract/Free Full Text]

22. Rossouw JE, Anderson GL, Prentice RL, et al: Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the Women's Health Initiative randomized controlled trial. JAMA 288:321-333, 2002[Abstract/Free Full Text]

23. Rebbeck TR, Levin AM, Eisen A, et al: Breast cancer risk after bilateral prophylactic oophorectomy in BRCA1 mutation carriers. J Natl Cancer Inst 91:1475-1479, 1999[Abstract/Free Full Text]

24. Thewes B, Meiser B, Hickie IB: Psychometric properties of the Impact of Event Scale amongst women at increased risk of hereditary breast cancer. Psychooncology 10:459-468, 2001[Medline]

25. Weiss DS, Marmar CR: The Impact of Event Scale–Revised, in Wilson JP, KT, Solomon SD (eds): Assessing Psychological Trauma and PTSD. New York, NY, Guilford Press, 1997, pp 399-411

26. Ende J, Lewis K, Ash A, et al: Measuring patients' desire for autonomy: Decision making and information-seeking preferences among medical patients. J Gen Intern Med 4:23-30, 1989[Medline]

27. Holmes-Rovner M, Kroll J, Schmitt N, et al: Patient satisfaction with health care decisions: The satisfaction with decision scale. Med Decis Making 16:58-64, 1996[Abstract/Free Full Text]

28. O'Connor AM: Validation of a decisional conflict scale. Med Decis Making 15:25-30, 1995[Medline]

29. Mattsson NB, Williams HV, Rickels K, et al: Dimensions of symptom distress in anxious neurotic outpatients. Psychopharmacol Bull 5:19-32, 1969[Medline]

30. Skinner CS, Schildkraut JM, Berry D, et al: Pre-counseling education materials for BRCA testing: Does tailoring make a difference? Genet Test 6:93-105, 2002[CrossRef][Medline]

31. Stacey DO, Connor A, DeGrasse C, et al: Development and evaluation of a breast cancer prevention decision aid for higher-risk women. Health Expect 6:3-18, 2003[CrossRef][Medline]

32. Green MJ, Peterson SK, Baker MW, et al: Effect of a computer-based decision aid on knowledge, perceptions, and intentions about genetic testing for breast cancer susceptibility: A randomized controlled trial. JAMA 292:442-452, 2004[Abstract/Free Full Text]

33. Molenaar S, Sprangers M, Rutgers EJ, et al: Decision support for patients with early stage breast cancer: Effects of an interactive breast cancer CDROM on treatment decision, satisfaction and quality of life. J Clin Oncol 19:1676-1687, 2001[Abstract/Free Full Text]

34. Whelan T, Levine M, Willan A, et al: Effect of a decision aid on knowledge and treatment decision making for breast cancer surgery: A randomized trial. JAMA 292:435-441, 2004[Abstract/Free Full Text]

35. Martin AM, Blackwood MA, Antin-Ozerkis D, et al: Germline mutations in BRCA1 and BRCA2 in breast-ovarian families from a breast cancer risk evaluation clinic. J Clin Oncol 19:2247-2253, 2001[Abstract/Free Full Text]

36. Brose MS, Rebbeck TR, Calzone KA, et al: Cancer risk estimates for BRCA1 mutation carriers identified in a risk evaluation program. J Natl Cancer Inst 94:1365-1372, 2002[Abstract/Free Full Text]

37. Easton DF, Ford D, Bishop DT: Breast and ovarian cancer incidence in BRCA1 mutation carriers. Am J Hum Genet 56:265-271, 1995[Medline]

38. van Roosmalen MS, Stalmeier PF, Verhoef LC, et al: Randomized trial of a shared decision-making intervention consisting of trade-offs and individualized treatment information for BRCA1/2 mutation carriers. J Clin Oncol 22:3293-3301, 2004[Abstract/Free Full Text]

39. Gallagher SM: Rethinking access in an information age. Ostomy Wound Manage 45:12-14, 1999[Medline]

40. Schwartz LM, Woloshin S, Black WC, et al: The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med 127:966-972, 1997[Abstract/Free Full Text]

41. Assmann SF, Pocock SJ, Enos LE, et al: Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 355:1064-1069, 2000[CrossRef][Medline]

Submitted June 16, 2004; accepted October 12, 2005.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Facebook    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. A. O'Brien, T. J. Whelan, M. Villasis-Keever, A. Gafni, C. Charles, R. Roberts, S. Schiff, and W. Cai Are Cancer-Related Decision Aids Effective? A Systematic Review and Meta-Analysis J. Clin. Oncol., February 20, 2009; 27(6): 974 - 985. [Abstract] [Full Text] [PDF]

				J. S. Ancker, Y. Senathirajah, R. Kukafka, and J. B. Starren Design Features of Graphs in Health Risk Communication: A Systematic Review J. Am. Med. Inform. Assoc., November 1, 2006; 13(6): 608 - 618. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Armstrong, K. Articles by Schwartz, J. S. Search for Related Content PubMed PubMed Citation Articles by Armstrong, K. Articles by Schwartz, J. S. Social Bookmarking                            What's this?