Logistic Regression Analyses: the 13 Measures Related to the Decision-Maker Variables Of

Supplemental Materials for Why Older Adults Make More Immediate

Treatment Decisions About Cancer Than Younger Adults

The Supplemental Materials include a series of logistic regressions analyses for Study 1 and Study 2. These are followed by situations 1 and 2 of the prostate cancer scenario used in

Study 1 and situation 2 of the breast cancer scenario used in Study 2.

Logistic Regression Analyses

A series of logistic regression analyses were conducted on the data from both studies and provided good confirmation of the decision tree results. The analyses allowed for investigation of the effects of numerous knowledge, interest, and resource variables as well as their combined ability to account for age effects in the tendency to make immediate treatment decisions. For each study the best knowledge, interest, and resource predictor variables were stepped into a final logistic regression equation in the order predicted by the model for decision making about cancer treatments displayed in Figure 1 of the Psychology and Aging article.

Logistic Regression Analyses for Study 1 about Prostate Cancer

Logistic regression was used to examine the 13 measures previously examined with decision tree analysis and related to the decision-maker variables of knowledge, resources, or interests in our model (see Figure 1). The same three measures of knowledge about cancer or prostate cancer entered as predictors in the decision tree analysis were entered in a logistic regression analysis to predict immediate versus delayed treatment decisions. Treatment knowledge solely for prostate cancer (Wald = 4.56, df = 1, p < .033) contributed significant predictive power, while two other knowledge measures (total knowledge for prostate cancer; total knowledge for diagnosis and treatment for prostate cancer and other types of cancer) did not contribute significantly. The more prostate cancer treatment knowledge possessed by a - 2 - participant, the more likely an immediate decision about a treatment was made when presented the diagnosis and treatment options by the physician in the scenario. The statistical model correctly predicted 89.9% of immediate decisions and 25.5% of delayed decisions; Nagelkerke’s

R2= .06 (Nagelkerke, 1991). Both the decision tree analysis and the logistic regression pointed to greater knowledge of prostate cancer treatments leading to immediate treatment decisions, lending support to our model (see Figure 1).

A second logistic regression looked at the six interest predictors and found that interest in the scenario prior to situation 2 (Wald = 6.78, df = 1, p = .009), but not the more trait-like interest measures of the MBSS (Miller, 1987) or the PSDM (Deber et al, 1996), predicted timing of the decision with higher interest exhibited in the scenario related to delaying the decision. The statistical model with interest correctly predicted 81.2% of immediate decisions and 40.4% of delayed decisions (Nagelkerke’s [1991] R2= .14).

The third logistic regression looked at the three cognitive resources predictors of education, working memory and vocabulary. Vocabulary (Wald = 3.66, df = 1, p = .056) added some predictive power, while education (Wald = .12, df = 1, p = .72) and working memory

(Wald = .46, df = 1, p = .50) did not approach significance. The statistical model with these predictors correctly predicted 81.2% of immediate decisions and 25.5% of delayed decisions

(Nagelkerke’s [1991] R2= .06). The higher the vocabulary the more likely a delayed treatment decision was made; this is the same relationship observed at nodes 3 and 4 of the AnswerTree cluster analysis seen in Figure 2. A logistic regression with only age group as a predictor showed age group to be a significant predictor of immediate versus delayed treatment decisions (Wald =

4.61, df = 1, p = .032). The statistical model with age group correctly predicted 82.6% of immediate decisions and 23.4% of delayed decisions (Nagelkerke’s [1991] R2= .06). - 3 -

Finally, the significant predictors from the above logistic regression analyses plus other predictors identified in the decision tree analysis were entered in one analysis and stepped into the equation in the order predicted by our model (knowledge, interest, cognitive resources) and followed by age to see if they could explain differences in decision time attributed to age. The findings are displayed in Table 1. There was a trend for more prior treatment knowledge relating to immediate decisions about prostate cancer treatment. The knowledge predictors shown in

Table 1 yielded a model with Nagelkerke’s (1991) R2= .04; knowledge could predict about 4% of the variance and not as much as anticipated by the model in Figure 1.

The state measure of interest was the only significant interest variable. As shown in

Table 1, the addition of the interest variables to the knowledge predictors improved the prediction of the model so that it could predict about 11% more variance (Nagelkerke’s [1991]

R2= .15). The addition of the cognitive resource variable of vocabulary also improved the prediction of the model (Nagelkerke’s R2= .20). The direction of relationships of these knowledge, interest, and cognitive resource variables are compatible with our model (see Figure

1). As seen in Table 1 these variables could account for all of the age effects. In the final logistic regression model 21% of the variance in immediacy of treatment decision-making could be explained (Nagelkerke’s R2= .21).

Logistic Regression Analyses for Study 2 About Breast Cancer

Logistic regression was used to examine the 19 measures initially examined with decision tree analysis and related to the decision-maker variables of knowledge, resources, or interests in our model (see Figure 1). The same seven measures of knowledge about cancer or breast cancer entered as predictors in the decision tree analysis were entered in a logistic regression analysis to predict immediate versus delayed treatment decisions. Prior knowledge of breast cancer - 4 - treatments (Wald = 19.16, df = 1, p < .0005) and misconceptions about breast cancer (Wald =

7.80, df = 1, p = .005) as measured by two subtests of the Vaeth’s (1993) test for knowledge of breast cancer and self-reported ratings of knowledge of cancer (Wald = 6.67, df = 1, p = .010) added significant predictive power, while self-reported knowledge of breast cancer and the other scores from the Vaeth test did not. The more treatment knowledge possessed by a participant and the fewer the misconceptions about breast cancer, the more likely an immediate decision about a treatment was made when presented the diagnosis and treatment options by the surgeon in the scenario. The statistical model with these significant predictors correctly predicted 68.2% of immediate decisions and 67.1% of delayed decisions; Nagelkerke’s R2= .22 (Nagelkerke, 1991).

Both the decision tree analysis and the logistic regression pointed to greater prior knowledge about breast cancer treatments leading to immediate treatment decisions, lending support to our model (see Figure 1).

A second logistic regression looked at the interest predictors and found that self-reported ratings of interest for cancer (Wald = 4.263, df = 1, p = .039), but not for breast cancer (Wald = .

43, df = 1, p = .51) predicted decision time with higher interest in cancer related to delaying the decision. The direction of the relationship is the same as that noted for high knowledge, older women in the cluster analysis. The statistical model with interest in cancer as a predictor correctly predicted 47.2% of immediate decisions and 64.8% of delayed decisions (Nagelkerke’s

[1991] R2= .03).

The third logistic regression looked at the nine cognitive resources predictors.

Vocabulary (Wald = 5.04, df = 1, p = .025) and forward digit span (Wald = 4.63, df = 1, p = .03) added significant predictive power, while reading skills (Wald = 3.31, df = 1, p = .069), education (Wald = .04, df = 1, p = .84), reaction time (Wald = .49, df = 1, p = .48), MMSE (Wald - 5 -

= .73, df = 1, p = .39), digits backward (Wald = .30, df = 1, p = .59), RSPAN (Wald = .75, df = 1, p = .39), and CSPAN (Wald = 1.68, df = 1, p = .20) did not. The statistical model with these predictors correctly predicted 66.7% of immediate decisions and 66.3% of delayed decisions

(Nagelkerke’s [1991] R2= .15). The higher the vocabulary the more likely an immediate treatment decision was made; the older adults scored higher on the vocabulary test than the younger, but the variability in the performances among the older group was also larger (Old: M =

56.23, SD = 21.17; Young: M = 43.17, SD = 11.66). The more diminished adults’ memory performance (i.e., digits forward) and lower their reading comprehension skills (i.e., Davis

Reading Test), the more likely immediate decisions were made. Meyer et al. (1995) found that types of treatments selected were related to prose processing; poor memory and reading skills can handicap treatment decision-making. Both the decision tree and the logistic regression analyses pointed to cognitive resources variables impacting decision time, but the specific variables differed. Due to the linear constraint of logistic regression, the education variable would have been missed without the earlier subgroup analyses since its relationship to decision time appears to be curvilinear.

A logistic regression with only age group as a predictor showed age group to be a significant predictor of immediate versus delayed treatment decisions (Wald = 18.57, df = 1, p

< .0005). The statistical model with age group correctly predicted 64.6% of immediate decisions and 67.4% of delayed decisions (Nagelkerke’s [1991] R2= .13).

Finally, the significant predictors from the above logistic regression analyses plus other linear predictors identified in the decision tree analysis were entered in one analysis and stepped into the equation in the order predicted by our model (knowledge, interest, cognitive resources) and followed by age group to see if they could explain differences in decision time attributed to - 6 - age. The findings are displayed in Table 2. The more treatment knowledge possessed by participants, the more likely they made an immediate decision about breast cancer treatment.

However, the more general cancer knowledge they possessed as measured by self-reported cancer knowledge or total scores overall about breast cancer, the more likely they were to delay their treatment decision. Perhaps participants’ general knowledge pointed out the seriousness of a cancer diagnosis and the need to delay their decision to find out more about this particular type of cancer and its various options for treatment. The significant knowledge predictors shown in

Table 2 yielded a model with Nagelkerke’s (1991) R2= .20.

Both self-rated knowledge about cancer and interest in cancer, rather than specific knowledge about breast cancer, were related to waiting to make a treatment decision. This suggests that participants with interest or knowledge about cancer in general preferred to wait to make a decision so that they could gather information related to this specific type of cancer, breast cancer. As shown in Table 2, the addition of the interest variable to the three knowledge predictors slightly improved the prediction of the model (Nagelkerke’s [1991] R2= .22).

The addition of the cognitive resource variables also improved the prediction of the model (Nagelkerke’s [1991] R2= .25). As can be noted in Table 2, only CSPAN was a significant predictor. The higher the working memory of the participant, the more likely they were to delay their treatment decision. The direction and magnitude of relationships of these knowledge, interest, and cognitive resource variables are compatible with our model (see Figure 1). As seen in Table 2, these variables could account for most but not all of the age group effects; age group was still a significant predictor of timing for treatment decisions and its addition to the model increased the Nagelkerke’s R2 from .25 to .28. - 7 -

Table 1

Summary of Logistic Regression with Predictor Variables Stepped into the Equation in the

Order of Prior Knowledgea, Interestb, and Cognitive Resourcesc Variables

Variables B SE B Wald df p

Step 1– Knowledge

PCa Tx -.14 .08 2.89 1 .09 All cancer D & Tx .11 .67 2.55 1 .11 Step 2 – Interest PCa Tx -.09 .09 1.61 1 .28 All cancer D & Tx .07 .07 .88 1 .35 Blunter .06 .10 .31 1 .58 Scenario interest .18 .08 4.98 1 .03 PSDM problem .58 .41 2.05 1 .15 PSDM decision .22 27 .64 1 .43 Step 3 – Resources PCa Tx -.12 .09 1.67 1 .20 All cancer D & Tx .05 .07 .50 1 .48 Blunter .04 .10 .18 1 .67 Scenario Interest .17 .08 4.72 1 .03 PSDM problem .62 .42 2.20 1 .14 PSDM decision .15 .28 .284 1 .60 Vocabulary .03 .02 4.09 1 .04 Step 4 – Age* PCa Tx -.10 .09 1.09 1 .30 All cancer D & Tx .05 .07 .50 1 .48 Blunter .02 .11 .03 1 .87 Scenario interest .16 .08 3.74 1 .05 PSDM problem .57 .42 1.87 1 .17 PSDM decision .06 .29 .04 1 .84 Vocabulary .03 .02 4.46 1 .04 Age -.01 .01 .89 1 .35 Constant -.3.55 1.79 3.96 1 .05

*Nagelkerke’s (1991) R2= .21; Correctly predicts 82.6% immediate decisions and 48.9% delayed decisions. aPCa Tx = treatment knowledge of prostate cancer; All cancer D & Tx = diagnosis and treatment knowledge of prostate cancer and other cancers. - 8 - bBlunter = score from the blunting subscale of the Monitor/Blunter Style Scale (Miller, 1987);

Scenario interest = amount of interest shown via search depth in the opening situation of the prostate cancer scenario; PSDM problem = score from the problem-solving subscale of the

Problem-Solving Decision-Making Scale (Deber et al., 1996) and PSDM decision = score from the decision-making subscale. cVocabulary = score from the Quick Word Test (Borgatta & Corsini, 1964). - 9 -

Table 2

Summary of Logistic Regression with Predictor Variables Stepped into the Equation in the

Order of Prior Knowledgea, Interestb, and Cognitive Resources Variablesc

Variables B SE B Wald df p

Step 1– Knowledge

Treatments .45 .10 16.81 1 .000 Breast cancer total -.07 .03 6.50 1 .011 Ratings for cancer -.38 .16 5.84 1 .016 Misconceptions Out .274 Step 2 – Interest Treatments .46 .11 18.00 1 .000 Breast cancer total -.07 .03 6.45 1 .011 Ratings for cancer -.32 .16 4.16 1 .042 Interest in cancer -.21 .13 2.80 1 094 Step 3 – Resources Treatments .47 .11 18.58 1 .000 Breast cancer total -.07 .03 6.51 1 .011 Ratings for cancer -.36 .16 4.95 1 .026 Interest in cancer -.21 .13 2.86 1 .091 CSPAN -.27 .13 4.51 1 .034 Digits forward Out .497 Digits back Out .715 Vocabulary Out .130 Reading skill Out .889 Step 4 – Age* Treatments .42 .11 13.61 1 .000 Breast cancer total -.06 .03 4.99 1 .025 Ratings for cancer -.37 .16 5.14 1 .023 Interest in cancer -.21 .13 2.71 1 .100 CSPAN -.17 .14 1.51 1 .219 Age group .77 .39 3.82 1 .051 Constant 1.54 1.33 1.34 1 .248 *Nagelkerke’s (1991) R2= .28; Correctly predicts 72% immediate & delayed decisions a Treatments = Vaeth’s Breast Cancer Treatment Knowledge (Vaeth, 1993); Misconceptions =

Vaeth’s Breast Cancer Misconception Knowledge; Breast cancer total = Vaeth’s Breast Cancer

Knowledge Total Number Correct; Ratings for cancer = Self-rated knowledge of cancer - 10 - bInterest in cancer = Self-rated interest in cancer cDigits forward and Digits backward from WAIS-R (Wechsler, 1981); CSPAN = Computational

Span (Babcock & Salthouse, 1990); Vocabulary = score from the Quick Word Test (Borgatta &

Corsini, 1964); Reading skill = score derived from the Davis Reading Test

(Davis, 1944) - 11 -

Prostrate Cancer Scenario Used in Study 1: Situations 1 & 21

Situation 1 – The Decision Task Format

The scenario is set within a hypertext environment so that you may access new information any time you see blue or purple underlined hyperlink. One of the beauties of hyperlinks is that you do not need to double click on them. Simply click once with the left mouse button and you will be taken to the corresponding page. If you are accustomed to "surfing," we ask that you not use the browser's navigation bar located at the top of the page. Instead, please use the navigation tools (hyperlinks) provided for you within each page. All pages have links at the bottom that you are instructed to click when you are finished with the information provided on the page.

Following these links will enable you to access other information or other parts of the decision scenario. If you get confused about where you are in the scenario or in the web pages, you may use the back button at the top of the page. However, this should only be done if you can see no other means of finding where you are.

Ready To Begin: Your New Identity

Now that you are ready to begin, we would like to ask you to assume a new identity. Please imagine that you are an active, married man. You just turned 60 and are very healthy. Try to keep this in mind as you make this medical decision. However, remember that even though your age may have changed, we want to know how you make decisions, so be true to your way of doing things.

Just so that we know you are ready to begin, turn to the experimenter and explain to him what you are supposed to do for this decision task. - 12 -

News From Your Latest Physical

During your last routine physical exam your doctor noticed an unusual growth in your prostate.

You were referred to a urologist after your initial PSA (prostate specific antigen) test came back higher than normal for a man your age. The urologist performed a series of tests (including another digital rectal exam (DRE), PSA tests and a trans-rectal ultrasound (TRUS)) to confirm the original results. All of the tests confirmed that something was abnormal with your prostate.

What Is It?

The urologist explained that there are a couple of conditions that can produce these results. The most common is called benign prostatic hyperplasia (BPH). This is a non-cancerous growth of the prostate. He explained that this growth is a normal part of the aging process and that when the prostate grows faster than expected it can produce elevated PSA levels. The other condition that could be causing the results is a cancerous growth within the prostate. Prostatic carcinoma is the second leading type of cancer among men (skin cancer is the most common). In addition, it is the second leading cause of cancer deaths among men (behind lung cancer).

The urologist explained that the TRUS results indicate that the PSA level is really higher than would be expected given the size of your prostate (high PSA density), and that it appears as though a small cluster of cells are responsible for the elevated PSA. He said "Now it could still be a benign growth in your prostate that is localized in this area. However, I need to tell you that it is more probable that we are talking about a malignant growth. The reason I say this is because of the results of the free-PSA test. - 13 -

"This test looks for the proportion of PSA floating in your bloodstream unaccompanied by other blood proteins. The accepted cutoff for normal levels of free PSA is 25%. Less than that usually signifies cancer. Your percentage free-PSA is right around 22. By itself, this count isn't that far off normal, but in conjunction with the abnormal DRE, the elevated PSA, and the high PSA density it looks like the possibility of cancer is pretty high."

He insisted that even with all of the test results they have, it is impossible to know what your condition is without a biopsy. He explained that the only way to make a firm diagnosis of your condition was to have a pathologist analyze cells from the growth and the surrounding prostate tissue. He performs biopsies in his office. The procedure is very short and can be done with or without a local anesthetic. He would like to set up the biopsy as early as schedules will allow.

What would you do?

1) Schedule the biopsy

2) Delay the scheduling of the biopsy

(Next Page for those who scheduled biopsy) - 14 -

You have scheduled your biopsy for next week. What would you like to do until then?

1) Nothing, try not to think about it

2) Seek information about biopsies. (Information available: newspaper, 2 Internet sites, 1 video)

3) Talk to your spouse

4) Talk to other family members and close friends.

5) Seek spiritual counsel (from clergy or others)

Situation 2 – Biopsy Results

Your biopsy went fairly well. You were amazed at how much pain could be generated by a single needle (you opted for the localized anesthesia and, boy, are you ever thankful you did!).

You waited four days for the doctor's office to call with the results of the biopsy. The news was not what you wanted to hear, prostatic carcinoma. The urologist scheduled a consultation appointment.

At the consultation the urologist explained that the growth was indeed malignant. He explained that cancer cells are rated in a couple of different ways. The most commonly referred to rating scale is the one used to indicate the stage of the cancer. Stage I indicates that the cancer cells are confined to the prostate and that they are too small to be detected by any means other than a biopsy. Stage II is cancer that is still confined to the prostate, but is large enough to be felt during a DRE. Cancer at either of these two stages is called "early stage cancer." These cancers are the most treatable because the cancer has not progressed outside of the host site. - 15 -

Stage III indicates cancer that has invaded surrounding areas. Stage IV cancer is the latest stage of cancer. It entails the movement of cancer cells out of the area and into the bloodstream. At this stage cancer is referred to as having metastasized. Once the cancer has progressed to this stage it grows in other regions of the body.

The urologist explained that there is no evidence that the cancer has spread outside of the prostate. While the tumor is small, it is considered stage II because it was found through the

DRE. He further explained that the other commonly used way of describing cancer cells is through "grading" using a Gleason score. The Gleason score describes how different the cells are from normal cells. The scale goes from 2 to 10. More abnormal cells receive higher grades.

Your biopsy was graded as a 4. This is still considered low.

He explained that this was a great sign, because it meant that the cancer was not aggressive. However, there is no way of knowing how long it will take to progress into a more dangerous form of the disease. At this point, the doctor explained the options you have for treatment.

Treatment Options

The urologist explained that there are three main types of treatment for early stage prostate cancer. These include radical prostatectomy, radiation therapy, and expectant therapy (watchful waiting). He explained that all three options had arguments for and against them. Each one had the potential for side effects and none could guarantee the elimination of the cancer.

Radical Prostatectomy Radical prostatectomy is the most invasive means of treatment. He explained that this treatment involves a one and a half to three hour surgery. During surgery he would first take a biopsy of the lymph nodes in the pelvic region. Assuming the pathology report says the lymph nodes are - 16 - cancer free, he will take out the prostate gland while trying to preserve as much of the soft tissue and nerves surrounding the prostate as possible. Since the tumor is small, the chances of eliminating the cancer are very good. Since the treatment requires a major surgery, there is a slight chance (.5% to 3%) of death. However, the main side effects for this treatment are incontinence and impotence. While most men experience short-term problems with both, the long-term impotence occurs in 30% to 90% of patients. The large range mainly depends on how much of the nerve is spared and how much pretreatment difficulty a patient has in achieving an erection. Incontinence is universal for a short period after the catheter is removed. However, only 32% of men report any further incontinence, and only 7% of men report complete incontinence.

Radiation Therapy Radiation therapy, on the other hand, has a lower risk of death with comparable or greater risk of side effects such as impotence and incontinence. Specifically, he explained, there is a 0.2% chance of death from the treatment. The rate of impotence is around 40%. Sixty percent of men report some degree of incontinence, while only 1% report complete control loss.

Of the other potential side effects, blockage of the urethra (called a urinary stricture) and rectal injury are the most common. Both treatment options involve risk of these effects. Surgery causes urinary stricture in 12% to 20% of patients, and causes rectal injury in approximately

30%. However, he noted, radical prostatectomy done with the retropubic (coming in from the abdomen region) method entails lower risk of rectal injury. Radiation therapy results in urinary stricture in about 5% of patients. Rectal injury occurs in approximately 11% of patients.

The doctor hands you a few pieces of literature which detail the numbers he explained to you. One piece, Understanding Treatment Choices for Prostate Cancer, also contains - 17 - information about other aspects of the treatments. He then explained that the research is still unclear about which treatment is best for your type of prostate cancer. However, he cited research that suggests that if you are going to live longer than 10 to 15 years, the probability of reoccurrence is lower when a radical prostatectomy is chosen. He also reaffirmed his belief that because of the small nature of the tumor, there was a good likelihood that he would be able to spare half of the nerves that allow erections (the half located on the side of the prostate that is tumor free).

Watchful Waiting He then explained that there was a third option, watchful waiting. This is essentially a gamble that the cancer will progress slowly. If this option is chosen, the object will be to monitor the

PSA levels so that if there is any indication that the tumor has grown, an alternate treatment can be selected. Of course, there are two big problems with this approach.

First, there is no way of knowing how long it will take for the cancer to grow or progress. The cancer might be very slow growing and might allow for decades of normal living prior to causing any difficulty. However, it might spread so fast that the window of treat-ability could be missed. In essence, he explained, this is a gamble. On the other hand, you have the benefits of avoiding all of the potential side effects that are caused by the other treatments. That is, you avoid them until you need to choose an alternative treatment option.

The main reason that watchful waiting is a plausible treatment option is that there is a lack of research findings indicating an increase in survival rate between treatment and non- treatment of prostate cancer. The reality is that the decision is a difficult one. You need to weigh the potential risks and benefits of each option and decide what way you want to treat this disease. - 18 -

Would you like to choose an option, now?

Yes, I am ready to choose an option.

No, I need more time

1From How much information do men really want? Information search behavior and decision rationale in a medical decision-making task for men (pp. 110-113; 116-118) by A. P. Talbot,

2004, Unpublished doctoral dissertation, The Pennsylvania State University, University Park.

Breast Cancer Scenario Used in Study 2: Situation 22

You opted for the lumpectomy immediately, and the lump was not cancerous. However, when on the operating table the surgeon had probed the area with her fingers and felt a smaller lump that had not appeared on the mammogram or the ultrasound. This other 8-mm (1/3 in.) lump was removed. This small lump was intraductal carcinoma insitu; that is, with the light microscope and the cells sampled there was no evidence of the cancer moving out of the mammary ducts and into surrounding breast tissue. (Intraductal carcinoma means that the cancer is in the milk ducts and in situ means that the pathologist did not see evidence for the spread of the cancer outside the duct membrane.) On one side of this cancerous lump within the duct, slightly less than 1mm (0.04 in.) of surrounding noncancerous tissue was available for analysis, perhaps this side of the lump faced the other lump of tissue removed, but perhaps this side faced the tissue still in your breast. However, in the sampled cells from the 1mm available to examine no cancer cells were seen outside of the cancerous duct. - 19 -

The surgeon tells you that she does not know what she would do in your situation. She thinks that she would either have a mastectomy (breast removal) or do nothing else but have frequent mammograms and checkups. The options she gave you are listed on the next page. She explains that she will be leaving town tomorrow for 2 weeks andthat you will need to make your own decision. She will be happy to have her secretarial staff set up any appointments with cancer specialists, and so on, if you are interested in speaking to them. She explains that experts disagree about what to do with the kind of cancer you have. She explains that they used to do mastectomies routinely for the spreading (invasive) type of cancer, but recent research has shown equal success rates for lumpectomy and radiation. With the in situ cancer, they used to routinely do mastectomies too, but there hasn't been much research on in situ cancer because it is less common. The experts do not have an answer for your health problem, and you will have to find your own personal answer. Here are the options she gave you:

1. Removal of both breasts.

2. Removal of the left breast.

3. Having another lumpectomy (reincision) to see if there is any more cancer in the area where the small cancerous lump was found.

4. Radiation to sterilize the area where the lump was found.

5. Random assignment to a national study of intraductal carcinoma in situ: half of the people are assigned to radiation and half are assigned to no treatment after both groups had the initial lumpectomy when the cancer was found.

6. Nothing but 6-month mammograms and checkups for the rest of your life.

Decision: Would you make a decision at this point? ______

Why? ______- 20 -

______

If not, what would you do next? ______

______

What kinds of information would you try to get to help you make this decision? ______

______

Write your plan for what you would do next? ______

______

2From “Discourse comprehension and problem solving: Decision about the treatment of breast cancer by women across the life span.” By B. J. F. Meyer, C. Russo, and A. Talbot, 1995,

Reprinted with permission.