Psychological and Physical Adjustment to Breast Cancer Over 4 Years: Identifying Distinct Trajectories of Change

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1 Health Psychology Copyright 2004 by the American Psychological Association, Inc. 2004, Vol. 23, No. 1, /04/$12.00 DOI: / Psychological and Physical Adjustment to Breast Cancer Over 4 Years: Identifying Distinct Trajectories of Change Vicki S. Helgeson, Pamela Snyder, and Howard Seltman Carnegie Mellon University The goal of this study was to identify distinct trajectories of adjustment to breast cancer over 4 years as well as to distinguish among the different trajectories. The mental and physical functioning of 287 women with breast cancer who remained alive and disease free through 4 years of follow-up were examined. The majority of women showed slight and steady improvement in functioning with time, but subgroups of women were identified who showed marked improvement and marked deteriorations over time. Age successfully distinguished different trajectories of physical functioning. Indices of personal resources (i.e., self-image, optimism, perceived control) and social resources (i.e., social support) successfully distinguished different courses of mental and physical functioning. Key words: adjustment to breast cancer, quality of life, personal resources, social resources A great deal of research in the area of chronic illness, including cancer, has focused on how people adjust to disease. Most of these studies, however, have focused on the initial adjustment period. In the area of cancer, the majority of people now survive the disease, with survival defined as being disease free for 5 years (American Cancer Society, 2002). Women with breast cancer are one of the largest groups of cancer survivors. Most breast cancers are diagnosed at an early stage, and 96% of those with localized disease and 78% of those with regional disease survive 5 years (American Cancer Society, 2002). Despite this large and growing population of cancer survivors, the majority of studies of people with cancer, including breast cancer, focus exclusively on the initial adjustment period. Far fewer studies have examined predictors of long-term adjustment or have examined the experience of surviving cancer. In recent years, several methodologically strong studies of cancer survivorship have emerged that examine the long-term effects (1 9 years) of having cancer on quality of life (Bower et al., 2000; Dorval, Maunsell, Deschenes, Brisson, & Masse, 1998; Ganz et al., 1996; Ganz, Rowland, Desmond, Meyerowitz, & Wyatt, 1998; Meyerowitz, Desmond, Rowland, Wyatt, & Ganz, 1999). Overall, these studies have concluded that there are no major differences in general quality of life between cancer survivors and healthy controls. With one exception, these studies have examined a single period of time or only two periods of time. One longitudinal study examined adjustment to breast cancer over a 3 5-year period and identified different stages of the illness that were associated with Vicki S. Helgeson and Pamela Snyder, Department of Psychology, Carnegie Mellon University; Howard Seltman, Department of Statistics, Carnegie Mellon University. We are grateful to Bobby Jones for his assistance with using and understanding the SAS trajectory program. Correspondence concerning this article should be addressed to Vicki S. Helgeson, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA vh2e@andrew.cmu.edu lower distress (e.g., during chemotherapy) and higher distress (e.g., during relapse; Heim, Valach, & Schaffner, 1997). However, none of these studies has identified distinct patterns of adjustment over time. The present study attempts to fill this gap by examining patterns of adjustment to breast cancer over a 4-year period. It is expected that the majority of people will respond to the diagnosis and treatment for breast cancer with some distress. The question is do some people remain distressed for long periods of time whereas others regain some normalcy in their lives after a period of time? Thus, the first goal of the study is to identify distinct trajectories of adjustment to breast cancer over 4 years. The second goal is to determine whether we can distinguish among people who follow different trajectories. We especially wanted to distinguish between those who remain distressed and those who rebound to more healthy functioning. What factors might differentiate groups of women who show distinct trajectories of mental and physical functioning? According to stress and coping theory, the experience of stress, and presumably psychological distress, results when demands exceed resources (Lazarus & Folkman, 1984). Two major sets of resources have been discussed: internal or personal (Taylor, 1983; Taylor & Brown, 1988) and external or social (Cohen & Wills, 1985; Helgeson & Cohen, 1996). We examine the implications that these two sets of resources might have for women s trajectories of psychological and physical functioning over 4 years following the diagnosis of breast cancer. Although previous research has not examined the implications of personal and social resources for trajectories of functioning, research has examined their implications for adjustment at a single point in time. Thus, we briefly review these literatures. Social Resources Social resources include the different kinds of support received from family and friends. According to the stress-buffering hypothesis, social support reduces one s vulnerability to psychological 3

2 4 HELGESON, SNYDER, AND SELTMAN distress during times of stress (Cohen & Wills, 1985). Numerous studies have examined the impact of social relationships during times of stress, in particular the threat from breast cancer (Alferi, Carver, Antoni, Weiss, & Durán, 2001; for a review see Blanchard, Albrecht, Ruckdeschel, Grant, & Hemmick, 1995; Heim et al., 1997; for reviews, see Helgeson & Cohen, 1996; Irvine, Brown, Crooks, Roberts, & Browne, 1991; Molassiotis, Van Den Akker, & Boughton, 1997; Rowland, 1989). These studies generally conclude that social support from family and friends is associated with better adjustment to disease. The majority of these studies are cross-sectional, however. Some researchers have recognized that relationships are not only sources of support but also can be sources of distress for people with cancer (Dakof & Taylor, 1990; Peters-Golden, 1982; Wortman & Dunkel-Schetter, 1987). Thus, research has examined the impact of negative interactions on adjustment to cancer and found that the negative aspects of close relationships show stronger links to adjustment than the positive aspects of close relationships (Butler, Koopman, Classen, & Spiegel, 1999; Manne, Taylor, Dougherty, & Kemeny, 1997). Personal Resources Personal resources include attributes of the person that one brings to bear on a stressor such as cancer. One personal resource is perceptions of personal control. In cross-sectional studies of people with cancer, personal control has been associated with less psychological distress and fewer role limitations (Bremer, Moore, Bourbon, Hess, & Bremer, 1997; Ell, Mantell, Hamovitch, & Nishimoto, 1989; Taylor, Lichtman, & Wood, 1984). A related concept, self-efficacy, has been shown to be a positive correlate of good adjustment among men with cancer (Beckham, Burker, Lytle, Feldman, & Costakis, 1997). Another personal resource is a sense of understanding of the experience. The reverse, illness uncertainty or confusion about what is happening with respect to the illness and the treatment, has been associated with greater anxiety following hospital discharge for mastectomy (Wong & Bramwell, 1992). Illness uncertainty also has been associated with poor adjustment to gynecologic cancer (Mishel, Hostetter, King, & Graham, 1984). Optimism is a personal resource that has been linked to lower levels of distress prior to breast cancer biopsy (Stanton & Snider, 1993), to positive adjustment to bone marrow transplant (Curbow, Somerfield, Baker, Wingard, & Legro, 1993), and to generally good adjustment among people with cancer (Mishel et al., 1984). In a longitudinal study, optimism predicted positive adjustment to breast cancer (Carver et al., 1993). Self-esteem also has been associated with positive adjustment to breast cancer (for a review, see Irvine et al., 1991). Other Predictors of Adjustment Aside from personal and social resources, what other characteristics of the person and the stressful event might distinguish trajectories of functioning? Again, because this specific question with regard to patterns of change has not been addressed by previous research, we examine the literature on predictors of adjustment to breast cancer (for a review, see Glanz & Lerman, 1992). Most of these studies focus on the initial adjustment period. Among the demographic variables, education and income seem to be unrelated to distress (Alferi et al., 2001; Maunsell, Brisson, & Deschenes, 1992). Age is inversely related to psychological distress among healthy populations (Blazer, 1989), but the relations of age to adjustment to breast cancer are contradictory (Glanz & Lerman, 1992). One study showed no relation of age to distress up to 18 months following breast cancer (Maunsell et al., 1992), but several recent studies have shown that older people adjust better to cancer (Williamson & Schulz, 1995), including breast cancer (Stanton & Snider, 1993; Williamson, 2000). One reason that older people may be less distressed is that they are more likely than younger people to expect declines in their health. There are aspects of the disease that may have implications for adjustment to cancer. More severe illnesses are associated with reduced physical functioning and increased psychological distress (for a review, see Glanz & Lerman, 1992; Hagedoorn et al., 2000; Williamson, 2000; Williamson & Schulz, 1992). Among women with breast cancer, the nature of the surgery has been examined as a predictor of adjustment. In general, there seem to be no differences in levels of distress or disease adjustment between women who have lumpectomies or mastectomies (for a review, see Kiebert, de Haes, & van de Velde, 1991). The only dimensions on which women with lumpectomies fare better compared with women with mastectomies are body image and sexual functioning (Ganz, Schag, Lee, Polinsky, & Tan, 1992; for a review, see Kiebert et al., 1991). Another factor that may have implications for adjustment to breast cancer is finding meaning in the experience, which has also been referred to as posttraumatic growth and benefit finding. It is not clear if this can be construed as a personal resource. Meaning has been associated with adjustment to breast cancer, but the direction of the relations are contradictory. In two cross-sectional studies, posttraumatic growth was unrelated to distress among breast cancer survivors (Cordova, Cunningham, Carlson, & Andrykowski, 2001) and bone marrow transplant survivors (Fromm, Andrykowski, & Hunt, 1996). In another cross-sectional study, positive growth was unrelated to mood but was related to greater life satisfaction (Curbow et al., 1993). In one longitudinal study, benefit finding was associated with increases in psychological distress over time, especially for women with more advanced disease (Tomich & Helgeson, 2002). Finally, there is a large and growing literature on the effects of psychosocial interventions on adjustment to cancer (for reviews, see Helgeson & Cohen, 1996, and Meyer & Mark, 1995). The literature shows that interventions clearly influence quality of life, although all interventions are not equally effective. Some of the women in the present study participated in a psychosocial group intervention. One of the intervention components, education, focused on providing women with information and enhancing their control over the illness experience. The education intervention had positive effects on mental and physical functioning immediately after it ended and 6 months later (Helgeson, Cohen, Schulz, & Yasko, 1999). The peer discussion component focused on the sharing of experiences and illness discussion with the goal of providing emotional support and enhancing self-esteem. It had no benefits and hints of some adverse effects on functioning shortly after it ended. Some of the benefits of the educational intervention remained 3 years later, and others disappeared with time (Helgeson, Cohen, Schulz, & Yasko, 2001). There were no long-term

3 ADJUSTMENT TO BREAST CANCER 5 effects, positive or negative, of the peer discussion intervention. Again, none of these findings address the issue of whether the interventions influenced trajectories of functioning. Thus, we examined whether the education and peer discussion interventions distinguished different patterns of psychological and physical functioning. The Present Study The goal of the research was twofold. First, we sought to determine if we could identify distinct trajectories or patterns of mental and physical functioning over 4 years following breast cancer. We expected to identify at least one group of women whose initial response to the illness would be impaired functioning but who would rebound to better psychological and physical functioning with time. We expected that the majority of women would show an upward trend toward improved functioning with time. We also expected to identify one group of women who would show sustained low psychological and physical functioning over the course of the study. To the extent that distinct trajectories could be identified, our second goal was to examine whether we could distinguish the different patterns of adjustment based on demographic variables, disease-related variables, and psychosocial variables. We expected that personal resources, social resources, and stage of disease would distinguish among the trajectories. Because the effect of the education intervention had dissipated with time (Helgeson et al., 2001), we were uncertain as to whether it would distinguish the trajectories. Participants Method Participants were 363 women diagnosed with Stage 1 (n 91), Stage 2 (n 250), and Stage 3 (n 22) breast cancer. All women were treated with surgery followed by adjuvant chemotherapy. Over two thirds (68%) of women had lumpectomies rather than mastectomies (32%), which is consistent with the norms for the Pittsburgh area. The majority of women were married (69%). Ages ranged from 27 to 75, with a mean of 48.3 (SD 9.79). Participants were Caucasian (93%), African American (7%), and Hispanic (1%). Education was 4% less than high school, 33% high school graduate, 26% some college education, 23% college graduate, and 14% postgraduate training. Procedure Women were recruited for the study from the offices of more than 40 medical oncologists shortly after they began chemotherapy. In other words, once a woman had surgery and made an appointment with a medical oncologist for chemotherapy, she was eligible for the study. During one of the first appointments with a medical oncologist, nurses provided the names and phone numbers of patients who were interested in hearing about the present study. Of 445 women contacted by phone, 364 (82%) agreed to participate in the study, but only 312 (70%) agreed to be randomized to a support group intervention (see Helgeson et al., 1999). That is, a subset of the participants who refused randomization agreed to complete the assessment portion of the study and are included in these analyses (n 52). Briefly, there were no differences in quality of life between respondents who did and did not agree to randomization, but those who declined randomization were less interested in health issues and less educated (Helgeson et al., 1999). The women who agreed to participate were interviewed in their homes for about 90 min. All instruments were administered orally, with the aid of response cards (e.g., 1 never; 5 always). Quality of life was measured before the intervention, on average 4 months postdiagnosis (Time 1). By this time, 20% of the women had recently completed chemotherapy. Quality of life was measured 7 months postdiagnosis (Time 2; 1 2 weeks after the intervention), at which time 80% of the women had completed chemotherapy. We continued to measure quality of life 13 months postdiagnosis (Time 3), 19 months postdiagnosis (Time 4), 31 months postdiagnosis (Time 5), 43 months postdiagnosis (Time 6), and 55 months postdiagnosis (Time 7). The only women included in the present analyses are those who were alive and had remained disease free (i.e., no recurrence) throughout the duration of follow-up. We omitted women who had sustained recurrences from the analyses (n 76) because our goal was to identify the different trajectories of adjustment to the initial diagnosis of and treatment for breast cancer. Undoubtedly, a recurrence of breast cancer would disrupt quality of life and have significant influences on adjustment trajectories depending on the time of the recurrence. Thus, the present analyses are based on 287 women who were alive and disease free by the final follow-up (Time 7), which was about 4.5 years since diagnosis. The number of women who completed each follow-up assessment is as follows: Time 1, 287; Time 2, 279; Time 3, 266; Time 4, 265; Time 5, 269; Time 6, 273; and Time 7, 271. Although not all women completed all waves of assessment, 94% of the sample completed the last assessment (Time 7). In the randomized trial, women were randomly assigned to one of four conditions: peer group discussion group, education group, education plus peer group discussion, or control group. There were seven separate groups, each consisting of 8 12 women, in all four conditions. The intervention groups met once a week for 8 weeks. Quality of Life Health-related quality of life was measured at all waves with the SF-36 (Ware, Snow, Kosinski, & Gandek, 1993). This instrument has excellent reliability and validity and has been used successfully to evaluate functional status in more than 20,000 depressed, chronically ill, and healthy patients (Wells et al., 1989). It contains eight multi-item scales: General Health Perception, Physical Functioning, Role Limitations Due to Physical Problems, Bodily Pain, Social Functioning, Role Limitations Due to Emotional Problems, Energy/Vitality, and General Mental Health. In this study, principal components analysis, followed by varimax rotation of the 36 items revealed eight factors with eigenvalues greater than 1. Items loaded on their respective eight scales. The authors have derived factor weights for the eight scales, so that a Mental Health Component Score (MCS) and a Physical Health Component Score (PCS) can be created (Ware, Kosinski, & Keller, 1994). Weights are assigned to all eight scales to create MCS and PCS. The scales with the highest loading on PCS (in order from highest to lowest) are Physical Functioning, Role Limitations Due to Physical Problems, Bodily Pain, and General Health Perceptions. The variables with the highest loading on MCS are Mental Health, Role Limitations Due to Emotional Problems, Social Functioning, and Energy/Vitality. Cronbach s alphas were high for each of the eight scales (above.80) at all times of measurement. Higher numbers indicate better functioning. Thus, our primary outcomes are the MCS and PCS indices. Chemotherapy Treatment Receipt Treatment with chemotherapy was an eligibility requirement for the study. We measured the extent to which women received the treatment initially prescribed by their physicians. Dosages of treatment were often reduced or treatment cycles delayed because of drug toxicity. The extent to which a woman is able to receive the full dosage of treatment prescribed on time predicts recurrence and survival (Bonadonna & Valagussa, 1981). For

4 6 HELGESON, SNYDER, AND SELTMAN each drug prescribed, we calculated the amount of the drug received and divided this by the amount of the drug prescribed. We then took the mean of the percentages to be our index of chemotherapy treatment receipt. Psychological Predictors of Trajectories of Functioning All of the following instruments were administered during the baseline interview. Social resources. First, we developed a 15-item support scale based on Vaux, Riedel, and Stewart s (1987) measure of perceived availability of support. Patients were provided with a list of ways that people help each other and asked whether their friends and family would be able to provide each kind of help. Responses were made on a 4-point scale: 1 would not, 2 might, 3 probably, and 4 certainly. Five items assessed emotional support (e.g., How likely would this person be to comfort you if you were upset? ), five items assessed informational support (e.g., How likely would this person be to give you advice about what to do? ), and five items assessed instrumental support (e.g., How likely would this person be to run an errand for you? ). During the initial interview, we administered all three subscales with respect to the woman s friends and family and also her husband or partner. Three fourths (75%) of the women had partners. Because the three kinds of support were highly correlated with one another, we averaged across the items to compute partner support and family and friend support indices. The internal consistencies of the two support scales were good (.87 and.70, respectively). Second, we developed a 10-item measure of negative interactions with network members, based on research on failed support attempts (Dakof & Taylor, 1990; Wortman & Lehman, 1985). Patients were told that people may have good intentions but sometimes say or do something that upsets them. Items included not understanding the situation, acting uncomfortable when talking about the illness, changing the subject when trying to discuss the illness, and trivializing problems. Women indicated the frequency with which their family and friends engaged in each item on a 5-point scale (1 never; 5 very often). Women with partners also responded to this scale with respect to their husbands or partners. The internal consistency of the two scales was.81 for partner and.85 for family and friends. Many of these items can be construed as the failure to provide emotional support (Helgeson & Cohen, 1996). The support scales were highly related to the negative interactions scales (r.75, p.001, for partner; r.47, p.001, for family and friends). To create a more stable variable and reduce capitalizing on chance, we created a social resources index by taking the mean of four variables: support from family and friends and partner and reverse-scored negative interactions for family and friends and partner. The average of two variables was used for women without partners. Personal resources. We measured four personal resources: selfesteem, body image, personal control, and illness uncertainty (reverse weighted). We used the Rosenberg Self-Esteem Scale (Rosenberg, 1965) to measure global self-esteem and developed a 14-item Body Image Scale based on the Cancer Rehabilitation Evaluation System (Schag & Heinrich, 1988). Internal consistencies were high (.83 and.89, respectively). We measured perceived personal control over the illness experience with three items that have been used in previous studies with chronically ill populations (Campbell, Dunkel-Schetter, & Peplau, 1991; Collins, Taylor, & Skokan, 1990; Helgeson, 1992). The three items tapped the domains of future course of illness, day-to-day illness symptoms, and emotions related to illness (.70). We administered an abbreviated form of the Illness Ambiguity subscale from Mishel s (1981) Uncertainty About Illness Scale (.71). The nine items chosen had the highest loading on Mishel s factor analysis. Because all four of these scales loaded on a single principal component following factor analysis and only one factor could be extracted, we developed a composite index that represented personal resources. Because the response scales for the four indices differed, we standardized the four scores before summing them to create the index. Benefit finding. We identified a number of ways that cancer could have had a positive impact on people s lives. Positive growth domains that were represented included personal priorities (e.g., more grateful for each day), daily activities (e.g., interest in participating in different activities), family (e.g., more sensitive to family issues), world views (e.g., more aware and concerned for the future of humankind), relationships (e.g., brought my family closer together), career (e.g., inspired me to improve my job skills), and religion (e.g., confirmed my faith in God). These items were adapted from Behr, Murphy, and Summers s (1991) Positive Contributions Scale used with parents of disabled children. Participants rated the extent to which 21 attitudes and behaviors had changed as a result of their stressful experience (1 not at all; 5 very much). The internal consistency of the scale was.95. To make sure that benefit finding was distinct from the four personal resources, we factor analyzed the four personal resources along with benefit finding to see if a common factor could be extracted. The four personal resources loaded highly on the first principal component (.51 to.86); benefit finding loaded.20. Following varimax rotation, two factors with eigenvalues greater than 1.0 emerged. The four personal resource variables loaded on one factor, and benefit finding loaded on a separate factor. Overview of the Analysis We used a new SAS procedure called TRAJ that Jones, Nagin, and Roeder (2001) created to identify developmental trajectories of behavior. Briefly, outcomes are treated as sensored normal data following a polynomial time course, given a discrete latent class assignment. TRAJ isolates distinct trajectories (one for each latent class) and fits a mixture model to calculate the probability of membership in each latent class for each participant. The majority of people clearly falls in a single class. Effects of covariates on class membership can then be modeled. The first part of the analysis was aimed at identifying distinct trajectories of mental and physical functioning. That is, we had to determine the optimal number of trajectories that best represented distinct patterns of change. We used the Bayesian Information Criterion (BIC) for determining the number of groups. The BIC is a method analogous to the adjusted R 2 in that it balances model complexity and model fit. Higher numbers indicate a better fit. We also examined whether each trajectory was characterized by a linear, quadratic, or function. To determine the best function, we examined the significance of all three parameters (linear, quadratic, ) and dropped the ones that were nonsignificant. That is, we started by including linear, quadratic, and parameters for each trajectory. If the parameter was not significant, we dropped it and tested for a quadratic trajectory. If this was not significant, we dropped it and tested for a linear trajectory. We retained the linear parameter even if this was not significant. After each change we compared the results using the BIC criteria. Finally, we examined whether allowing the intercept and slope of each trajectory to be a random effect improved the model fit. For example, a random intercept would mean that all participants in a group have a similar trajectory but can shift up and down on the y axis relative to the group average. We began by allowing the intercept and slope to be random parameters and examined their significance levels. Nonsignificant random parameters were replaced with fixed parameters. Again, we compared models and selected the best model based on the BIC criteria. Results Model Selection: Identifying Distinct Trajectories MCS. Table 1 shows that four distinct trajectories best represented the data for MCS. The BIC increased as the number of trajectories increased up to four. In general, the more complex model is justified if its BIC is at least three higher than the simpler

5 ADJUSTMENT TO BREAST CANCER 7 Table 1 Criteria for Selecting the Number of Trajectories Score and number of groups BIC Null model Change in BIC MCS 1 6, , , , , PCS 1 6, , , , , , , Note. BIC Bayesian Information Criterion; MCS Mental Health Component Score; PCS Physical Health Component Score. model. The change from four to five groups led to a decrease rather than an increase in BIC. The four distinct trajectories are shown in Figure 1. The solid lines represent the observed data; the broken lines represent the predicted trajectory. Table 2 displays model selection for the shape of the trajectories. For each of the four trajectories, we started by including linear, quadratic, and parameters and then dropped the nonsignificant ones. The Model 1 (M1) columns show that the parameter was only significant for Trajectories (Traj) 1 and 2. Thus, in Model 2 (M2), the parameters were retained for and but dropped from and. Finally, in Model 3 (M3), we dropped the quadratic parameter for. Model 3 (M3) has the highest BIC and is therefore the preferred model. and were, was linear, and was quadratic. The parameters in the model form the equation for each trajectory. For example, the equation for in Model 3 (M3) is: (3.22 time) ( 5.10 time 2 ) (3.02 time 3 ). The top half of Table 3 shows which parameters were allowed to be random for each trajectory. We began with a random effect for both the intercept and slope for each of the four trajectories and dropped nonsignificant random parameters. The last column of Table 3, Model 6 (M6), shows that the intercept and the slope were allowed to be random for and ; has a random intercept and a fixed slope, and has a fixed slope and a fixed intercept. As shown in Figure 1, reveals the highest levels of mental functioning or the lowest levels of distress, and these levels are fairly consistent throughout the duration of the study. There is some improvement in functioning early on and then a slight deterioration toward the end of the study, which is supported by the quadratic function. This trajectory characterizes the largest percentage of people (43%)., characteristic of 18% of the sample, shows minor ups and downs throughout the duration of the study, but this variation was not captured by a, quadratic, or linear function. The levels of mental functioning for are somewhat lower than, showing a slight upward trend over time. and reveal the most interesting patterns of Figure 1. Four trajectories of mental functioning from 4 to 55 months (mos) after breast cancer diagnosis. MCS Mental Health Component Score; T time postdiagnosis.

6 8 HELGESON, SNYDER, AND SELTMAN Table 2 Model Selection for Mental Health Component Score Model 1 (M1) Model 2 (M2) Model 3 (M3) Traj group and parameter quad quad linear quad 1 Intercept Linear 2.52* 3.05** 3.22** Quad 5.07*** 4.71*** 5.10*** Cubic 3.34*** 3.02*** 3.02** 2 Intercept Linear Quad 3.57*** 3.58*** 3.59*** Cubic 2.64** 2.66** 2.67** 3 Intercept Linear Quad Cubic Intercept Linear 2.94** 6.45*** 6.46*** Quad 2.79** 3.87*** 4.04*** Cubic 0.45 BIC 6, BIC 6, BIC 6, Note. Groups in bold were the models with the best fit. M linear, quadratic, and parameters of the Mental Health Component Score; Traj trajectory; Quad quadratic; BIC Bayesian Information Criterion. * p.05. ** p.01. *** p.001. change. Both revealed functions. These two groups start out with the lowest levels of mental functioning, but immediately and substantially improves over time with some deterioration toward the end of the study, whereas shows an immediate and substantial decline over time with a modest improvement toward the end of the study. characterizes 27% of the sample, and characterizes 12% of the sample. The crossover in mental functioning between and occurs early on in the study, within 6 months of diagnosis. By 13 months, the mental functioning of and are indistinguishable. PCS. Table 1 shows that four distinct trajectories best represent changes in PCS over time. The BIC increased up to four groups and then decreased with the addition of a fifth group. There was also an additional increase in BIC with six groups, however. When we reviewed the six trajectories, we could not see that they added useful information beyond that contained in a fourtrajectory model. In addition, the number of people captured by each trajectory was becoming quite small with six trajectories (i.e., three of six trajectories contained less than 8% of the people). Thus, for parsimony, we retained the four-trajectory model. Table 4 shows model selection for the shape of each of the four trajectories. Again, we began by testing the linear, quadratic, and parameters for all four trajectories. The parameters in the Model 1 (P1) columns were only significant for and. Thus, the parameters were dropped from and and the quadratic parameters were tested in Model 2 (P2). The final model, displayed in the Model 4 (P4) column, has the highest BIC: and 2 are linear and and 4 are. The bottom half of Table 3 shows the test of random effects. Model 6 (P6) was the best model: The intercept and the slope were allowed to be random for and ; the intercept was fixed and the slope was random for ; and the intercept was random and the slope was fixed for. As shown in Figure 2, showed the best physical functioning at the start of the study, improved slightly with time, and remained consistently high throughout the duration of the study. This trajectory captures the majority of the people, 55%. The slight changes that are captured by the function are difficult to see. By contrast, characterizes the smallest percentage of the sample (2%), showed the worst physical functioning at the start of the study, and slowly deteriorated over time. (20%) and Traj 3 (23%) both began with an intermediate level of physical functioning. s physical functioning does not appear to change over time (linear function), whereas shows an immediate improvement that is sustained throughout most of the study with some deterioration toward the end ( function). Identifying Variables That Distinguish Trajectories MCS. Our goal was to distinguish, the group who deteriorated over time, from all other trajectories. We were especially interested in distinguishing from, the group who started at the same low level of mental functioning but improved with time. First, we examined demographic and diseaserelated variables as risk factors for group assignment: age, education, stage of disease, number of positive lymph nodes, estrogenreceptor status, and surgery. The estimates that we report are the change in log odds (lo) ratio of being in one trajectory versus another, given a one-unit increase in the risk factor. For example, if the coefficient for distinguishing from for no

7 ADJUSTMENT TO BREAST CANCER 9 Table 3 Random Effects Parameters by Trajectory (Traj) Group Traj group and parameter MCS Model 4 (M4) Model 5 (M5) Model 6 (M6) 1 Intercept 5.83*** 5.60*** 5.60*** Slope 4.62*** 5.62*** 4.63*** 2 Intercept 6.77*** 6.77*** 6.79*** Slope 8.03*** 8.04*** 8.10*** 3 Intercept 2.10* 2.20* 2.36* Slope Intercept Slope 0.00 BIC 6, BIC 6, BIC 6, Model 5 (P5) PCS Model 6 (P6) 1 Intercept 4.72*** 4.73*** Slope 6.55*** 6.54*** 2 Intercept Slope 2.21* 2.21* 3 Intercept 3.70*** 3.73*** Slope 5.55*** 5.55*** 4 Intercept 5.77*** 5.80*** Slope 0.01 BIC 5, BIC 5, Note. M linear, quadratic, and parameters of Mental Health Component Score (MCS); BIC Bayesian Information Criterion; P linear, quadratic, and parameters of Physical Health Component Score (PCS). * p.05. *** p.001. college (coded 0) versus college education (coded 1) is 1.0, then the odds of being in versus is exp(1.0), or 2.7 as high if a participant attended college. Among the demographic variables, age was marginally significant in distinguishing from (lo 0.05, p.06), such that the women characterized by were younger. Among the disease-related variables, type of surgery distinguished from (lo 1.36, p.05), such that women in were more likely to have a mastectomy than were women in. Chemotherapy receipt distinguished from (lo 0.66, p.05) and (lo 1.06, p.06), such that had a lower level of receipt than the other two groups. Next, we examined the extent to which the two interventions (education, peer discussion) distinguished the trajectories. The peer discussion intervention distinguished from (lo 1.18, p.05), such that people in were more likely to have been assigned to a peer discussion intervention. The education intervention did not distinguish the trajectories. Finally, we examined our psychosocial variables: personal resources, social resources, and benefit finding. Personal resources distinguished from (lo 1.86, p.005) and (lo 3.93, p.001). tended to have fewer personal resources than and. Social resources distinguished from (lo 2.90, p.001). tended to have fewer social resources than. Benefit finding also distinguished from (lo 0.66, p.05), such that Traj 1 reported greater benefit finding. Thus, we entered the significant risk factors into the final model to see which ones emerged as independent risk factors: age, type of surgery, peer discussion intervention, compliance with chemotherapy, personal resources, social resources, and benefit finding. 1 The only variable that distinguished from was type of surgery, and it was marginal (lo 1.29, p.07). The peer discussion intervention distinguished from (lo 1.40, p.05), whereas social resources was marginal (lo 0.99, p.06). was distinguished from by both personal resources (lo 3.26, p.001) and social resources (lo 1.74, p.001). PCS. Here, our goal was to distinguish from all other trajectories, especially, because although the two started out with a similar level of functioning, improved but did not. We decided not to focus on distinguishing, the group with the lowest levels of physical functioning, from all other trajectories because it characterized only 2% of the sample. In the course of focusing on, we would distinguish it from. Among the demographic and disease-related variables, age distinguished from (lo 0.09, p.001) and (lo 0.08, p.005), such that older people were in. Number of positive lymph nodes distinguished from (lo 0.44, p.05) and (lo 0.43, p.01), such that Traj 1 had fewer positive lymph nodes. Chemotherapy receipt, benefit finding, and the interventions did not distinguish the trajectories. Personal resources, however, distinguished from (lo 1.68, p.005) and (lo 1.02, p.001), such that had more personal resources than but fewer personal resources than. Social resources distinguished from (lo 1.12, p.005), such that had higher social resources than. In the final model, we entered the significant risk factors simultaneously: age, positive lymph nodes, personal resources, and social resources. 2 Age marginally distinguished from (lo 0.10, p.07) and significantly distinguished from (lo 0.07, p.05) and (lo 0.10, p.001). That is, was older than and, but younger than. Number of positive lymph nodes marginally distinguished from (lo 0.34, p.09) and significantly distinguished from (lo 0.49, p.01). Personal resources significantly distinguished from (lo 2.67, p.01), (lo 0.81, p.05) and (lo 1.41, p.001). Social resources did not significantly distinguish the groups. 1 Although the education intervention did not significantly distinguish the trajectories, we reran this final model with all of the risk factors and included it. The education intervention was nonsignificant and did not alter the results. 2 Although the education intervention and the peer discussion interventions did not distinguish the trajectories, we reran the final model with their inclusion. Inclusion of the interventions did not alter the results.

8 10 HELGESON, SNYDER, AND SELTMAN Table 4 Model Selection for Physical Health Component Score Model 1 (P1) Model 2 (P2) Model 3 (P3) Model 4 (P4) Traj group and parameter quad quad quad linear linear linear Intercept Linear 2.33* 3.18** 3.16** 4.72*** Quad Cubic 1.08 Intercept Linear Quad Cubic 0.74 Intercept Linear 3.24** 3.31*** 3.30*** 3.30*** Quad 8.12*** 8.09*** 8.12*** 8.12*** Cubic 3.19** 3.15** 3.14** 3.14** Intercept Linear Quad 5.60*** 5.57*** 5.61*** 5.60*** Cubic 3.56*** 3.56*** 3.57*** 3.56*** BIC 6, BIC 6, BIC 6, BIC 6, Note. Groups in bold were the models with the best fit. P linear, quadratic, and parameters of Physical Health Component Score; Traj trajectory; Quad quadratic; BIC Bayesian Information Criterion. * p.05. ** p.01. *** p.001.

9 ADJUSTMENT TO BREAST CANCER 11 Figure 2. Four trajectories of physical functioning from 4 to 55 months (mos) after breast cancer diagnosis. PCS Physical Health Component Score; T time postdiagnosis. Overlap Between Trajectories We examined the overlap in the trajectories for MCS and PCS. We classified people into their most likely trajectory group and conducted a chi-square analysis. The chi-square was significant, 2 (9, N 287) 25.50, p.01, suggesting that the MCS groupings were not distributed equally across the PCS groupings (or that PCS groupings were not distributed equally across MCS groupings). The results are shown in Table 5, which displays the number of people in each cell (MCS by PCS grouping); the percentage of people in each cell is indicated in parentheses. The greatest overlap occurred for PCS and MCS, the groups who started out with the highest level of functioning and showed a slight improvement that was maintained over time. Of the people classified in MCS, 67% of those were classified in PCS. Of the people classified in PCS, 53% of those Table 5 Number (and Percentage) of People in MCS and PCS Trajectories MCS trajectory group PCS trajectory group (2.8) 3 (1.0) 9 (3.1) 15 (5.2) 2 19 (6.6) 1 (0.3) 21 (7.3) 35 (12.2) 3 15 (5.2) 2 (0.7) 10 (3.5) 25 (8.7) 4 14 (4.9) 27 (9.4) 83 (23.3) Note. Dash represents no participants in this group. MCS Mental Health Component Score; PCS Physical Health Component Score. were classified in MCS. There was much less overlap when MCS and PCS s were compared, which is not surprising because the two trajectories are shaped differently. It is difficult to compare the last two trajectories. We compared MCS (those who began the study with poor psychological functioning that quickly improved with time) with PCS (those who began the study with intermediate physical functioning that did not change over time) and found that 25% of the people in MCS were classified in PCS and 34% of those classified in PCS Traj 1 were classified in MCS. Finally, we compared MCS Traj 1 with PCS those characterized by the poorest functioning, which deteriorated over time. Of those classified in MCS, only 9% were classified in PCS. Of those classified in PCS, which was a very small percentage of the sample, 50% were classified in MCS. There were noteworthy sections of nonoverlap. People categorized in any of the MCS trajectories were fairly equally represented in PCS (the highest level of physical functioning): 43% of those in MCS, 46% of those in MCS, 48% of those in MCS, and 67% of those in MCS. The same can be said of PCS (intermediate level of physical functioning): between 19% and 26% of each MCS trajectory were found in PCS. We also examined whether baseline levels of MCS could distinguish the PCS trajectories and whether baseline levels of PCS could distinguish the MCS trajectories. Baseline PCS only distinguished MCS from. Similarly, baseline MCS only distinguished PCS from. Controlling for these baseline values did not alter the significance of the risk factors previously reported in the final equations.

10 12 HELGESON, SNYDER, AND SELTMAN Thus, there was some overlap between the two trajectories, but most of the overlap occurred in the highest functioning groups. People who had the highest level of psychological functioning over the course of the study were quite likely to be classified as high in physical functioning over the course of the study. However, people who had a very high level of physical functioning over the course of the study had a range of patterns of psychological functioning. The small number of people who had sustained poor physical functioning were typically classified as low in psychological functioning. These findings suggest that good physical functioning is a necessary but not sufficient prerequisite for good psychological functioning. Discussion The results from the present study showed that there are indeed distinct patterns of change in mental and physical functioning following the diagnosis and treatment of breast cancer. Heim et al. (1997) found that psychosocial adjustment to breast cancer remained fairly stable over a 3 5 year period, but they did not examine their data to see whether there were distinct patterns of change. There are distinct courses of adjustment to breast cancer. In other words, time alone is not a sufficient predictor of adjustment to breast cancer, which is somewhat contrary to previous research that has suggested time is the most critical factor in predicting adjustment (Irvine et al., 1991; Meyerowitz, 1980). Certainly, time was an important factor for some groups of women but not all. The mental functioning of 43% of the women started out very high and only showed a modest change throughout the 4 years of follow-up. Similarly, the physical functioning of 55% of the women started out very high and showed very little change throughout the 4 years of follow-up. Thus, for these groups of women, time bore only a modest relation to their mental and physical functioning. There were other groups of women, however, who showed declines or improvements in mental and physical functioning over time. For mental functioning, a group representing just less than 20% of the sample showed a small but steady improvement over time. Two other groups, however, showed extremely different trajectories of adjustment. These two groups started out at equally low levels of mental functioning, but the larger group (27%) rapidly improved by 13 months and then remained steady over the duration of the study, whereas the smaller group (12%) showed a steady decline that plateaued at about 19 months. For physical functioning, a very small percentage of women (2%) showed a low level of physical functioning at the start of the study and steadily deteriorated over the duration of follow-up. Another 20% of women with a moderate level of physical functioning showed no change over the duration of the study. Finally, a group of 23% of women began with a moderate level of physical functioning, improved dramatically by 13 months, and then remained high over the duration of the study. Was there a period of time during which most of the change occurred? Averaging across women with breast cancer, Heim et al. (1997) showed that the greatest change in adjustment to breast cancer took place between hospitalization and the subsequent 3 months. We cannot directly test this hypothesis because our initial interviews of women took place, on average, 4 months following diagnosis. However, we did find that the greatest change took place within the 4 13-month window for the women who showed substantial change. For mental functioning, there was not a dramatic change at any point in the follow-up for about 60% of the sample. Among the other 40%, some showed a sharp improvement and some showed a sharp deterioration within the first 13 months of diagnosis. For physical functioning, the greatest change also took place within the 13 months following diagnosis. Although 20% of the sample showed no change, slightly more than half showed a steady improvement, 23% showed a more dramatic improvement, and an extremely small percentage (2%) showed a deterioration. Although four distinct trajectories were identified for both mental and physical functioning, there was less overlap between the trajectories than might be expected. The greatest overlap occurred for the highest functioning groups. In addition, physical functioning was more strongly related to mental functioning than vice versa. That is, two thirds of the highest physical functioning group had the highest mental functioning, and half of the lowest physical functioning group had the lowest mental functioning. However, knowing a woman s mental functioning trajectory did not provide as much information about her physical functioning trajectory. A second purpose of this study was to see whether we could distinguish among these different patterns of adjustment to breast cancer over time. Demographic variables and disease variables did very little in terms of differentiating the trajectories, with the exception of age. Age was an important predictor of the trajectories of physical functioning. The group with the lowest level of physical functioning that deteriorated over time was the oldest. The group with the highest physical functioning throughout the study and the group with moderate physical functioning who showed substantial improvement were the youngest groups. It is surprising that stage of disease showed very little relation to the patterns of functioning over time. This may be due to the fact that we had a relatively homogenous group with respect to prognosis women who had largely Stage 1 and 2 disease who did not recur over the duration of the study. Consistent with stress and coping theory, the personal and social resources that women had at the beginning of the study best distinguished the different trajectories of mental and physical functioning. When personal and social resources were considered simultaneously, both distinguished trajectories of mental functioning, but only personal resources emerged as an independent predictor of physical functioning. Personal resources may have stronger effects than social resources because personal resources can affect social resources (Hobfoll, 1989). Those with personal resources know when to call upon social resources and are more likely to have social resources available. The interventions played a relatively minor role in distinguishing trajectories of functioning. In previous work, Helgeson et al. (1999, 2001) found benefits of the education intervention on quality of life. These benefits appeared immediately after the intervention; some disappeared with time and some were maintained over time. In the present study, we found no evidence that the education intervention distinguished trajectories of functioning. Thus, the education intervention was not powerful enough to predict the shape of women s functioning over time. In that earlier work Helgeson et al. (1999, 2001) found hints of early short-term adverse effects of the peer discussion intervention but no significant effects either in the short term or the long term. In the present