- Email: [email protected]
Pretreatment factors significantly influence quality of life in cancer patients: A Radiation Therapy Oncology Group (RTOG) analysis
Int. J. Radiation Oncology Biol. Phys., Vol. 65, No. 3, pp. 830 – 835, 2006 Copyright © 2006 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/06/$–see front matter
doi:10.1016/j.ijrobp.2006.01.004
CLINICAL INVESTIGATION
Quality of Life
PRETREATMENT FACTORS SIGNIFICANTLY INFLUENCE QUALITY OF LIFE IN CANCER PATIENTS: A RADIATION THERAPY ONCOLOGY GROUP (RTOG) ANALYSIS BENJAMIN MOVSAS, M.D.,* CHARLES SCOTT, PH.D.,†
AND
DEBORAH WATKINS-BRUNER, PH.D.‡
*Radiation Oncology, Henry Ford Health System, Detroit, MI, †RTOG Headquarters, Philadelphia, PA, ‡Fox Chase Cancer Center, Philadelphia, PA Purpose: The purpose of this analysis was to assess the impact of pretreatment factors on quality of life (QOL) in cancer patients. Methods and Materials: Pretreatment QOL (via Functional Assessment of Cancer Therapy [FACT], version 2) was obtained in 1,428 patients in several prospective Radiation Therapy Oncology Group (RTOG) trials including nonmetastatic head-and-neck (n ⴝ 1139), esophageal (n ⴝ 174), lung (n ⴝ 51), rectal (n ⴝ 47), and prostate (n ⴝ 17) cancer patients. Clinically meaningful differences between groups were defined as a difference of 1 standard error of measurement (SEM). Results: The mean FACT score for all patients was 86 (20.7–112) with SEM of 5.3. Statistically significant differences in QOL were observed based on age, race, Karnofsky Performance Status, marital status, education level, income level, and employment status, but not by gender or primary site. Using the SEM, there were clinically meaningful differences between patients <50 years vs. >65 years. Hispanics had worse QOL than whites. FACT increased linearly with higher Karnofsky Performance Status and income levels. Married patients (or live-in relationships) had a better QOL than single, divorced, or widowed patients. College graduates had better QOL than those with less education. Conclusion: Most pretreatment factors meaningfully influenced baseline QOL. The potentially devastating impact of a cancer diagnosis, particularly in young and minority patients, must be addressed. © 2006 Elsevier Inc. Quality of life, Cancer, Pretreatment factors.
INTRODUCTION
with more than 1,400 patients, most of whom were male and had head-and-neck cancer, was analyzed to determine the impact of pretreatment factors on QOL in cancer patients.
Over the last few decades, quality of life (QOL) has become recognized as a critical component of clinical oncology trials (1). Studies have demonstrated that pretreatment QOL is itself a powerful prognostic factor for survival (2). QOL provides direct, patient-derived feedback, beyond the traditional clinical outcomes, that can aid in interpreting the results of a study. Indeed, in 1988, the National Cancer Institute declared in its mission statement that “research aimed at improving survival and QOL for persons with cancer is of the highest priority to the cancer therapy evaluation program” (1). Although multiple studies have documented the reliability and validity of various QOL instruments in oncology trials, few reports have analyzed the factors that influence QOL itself in cancer patients. This is critical information, because QOL outcomes may be confounded by pretreatment factors, which should then be factored into the design of QOL studies. The Radiation Therapy Oncology Group (RTOG) Functional Assessment of Cancer Therapy (FACT) database
Pretreatment QOL (via FACT, version 2) was obtained in 1,428 (nonmetastatic) cancer patients in six prospective RTOG trials: head-and-neck cancer (n ⫽ 1,139 from RTOG 9003 and RTOG 9111), esophageal cancer (n ⫽ 174 from RTOG 9405), lung cancer (n ⫽ 51 from RTOG 8901), rectal cancer (n ⫽ 47 from RTOG 9401) and prostate cancer (n ⫽ 17 from RTOG 9020). In all cases, the baseline QOL questionnaire was obtained before initiation of protocol treatment, with the vast majority filled out within 1–2 weeks before initiation of therapy. RTOG 9003 (3) was a randomized trial to compare various radiation fractionation schema with standard once-daily fractionation for squamous cell carcinoma of the head and neck, including the oral cavity, oropharynx, hypopharynx, and supraglottic larynx. Eligibility stipulated that patients had to have Stage III or IV disease (except for base of tongue or hypopharynx primaries, for which Stage II was eligible) and a Karnofsky Performance Status (KPS) score ⱖ60. RTOG 9111 (4)
Reprint requests to: Benjamin Movsas, M.D., Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Boulevard, Detroit, MI 48202. Tel: (313) 916-5188; Fax: (313)
916-3235; E-mail: [email protected] Received Aug 5, 2005, and in revised form Jan 10, 2006. Accepted for publication Jan 11, 2006.
METHODS AND MATERIALS
830
Quality of life in cancer patients
was a randomized trial comparing concurrent chemotherapy and radiation for preservation of the larynx in locally advanced laryngeal cancer. The trial compared induction chemotherapy and radiation vs. concurrent chemoradiation vs. radiation therapy alone. The eligibility criteria included Stage III or IV squamous cell carcinoma of the glottic or supraglottic larynx, the surgical treatment of which would require total laryngectomy, and a KPS of ⱖ60. RTOG 9405 (5) studied combined modality therapy for carcinoma of the esophagus and randomized patients to high dose vs. conventional dose radiation therapy (64.8 Gy vs. 50.4 Gy). Eligibility required squamous cell or adenocarcinoma of the esophagus with no tumor extension within 2 cm proximal to the stomach and KPS ⱖ60. RTOG 89-01 (6) was a randomized trial in patients with non–small-cell lung cancer with mediastinal nodal involvement (pathologic Stage IIIA disease), in which all patients received induction chemotherapy, and were then randomized to surgery or radiation therapy, followed in all patients by consolidation chemotherapy. RTOG 94-01 was an intergroup randomized trial of preoperative vs. postoperative combined modality therapy for resectable adenocarcinoma of the rectum. Eligibility mandated KPS ⱖ60 and that the distal border of the tumor be ⱕ12 cm from the anal verge. RTOG 9020 (7) was a Phase II trial of radiation with Etanidazole (SR-2508), a hypoxic cell sensitizer, for the treatment of locally advanced prostate cancer. Patients with biopsy proven adenocarcinoma of the prostate with locally advanced T2B-T4 tumors were eligible for this study. The FACT-General (FACT-G) is a reliable and valid instrument to measure QOL in cancer patients (8). FACT-G consists of five dimensions. Emotional well-being (EWB) contains five items (range, 0 –20) that pertain to psychologic distress or symptoms, such as depression or anxiety. Relationship with physician contains two items (range, 0 – 8) that relate to the doctor’s interpersonal skills and competence. The other three dimensions each contain seven items (range, 0 –28). Physical well-being (PWB) describes the presence of physical symptoms (such as fatigue, pain or nausea), functional well-being (FWB) measures one’s mobility and ability to perform daily activities and social well-being (SWB) refers to participation in family relationships, sexuality, and social interactions. Each of these variables, as well as the overall QOL score (an aggregate score consisting of 28 items, range, 0 –112), was calculated according to the scoring instructions for FACT (9). Briefly, Likert’s method for summated rating scores was used to measure responses (10). After reversing the scoring of negatively worded items (so that a higher score always indicated a favorable response), item responses were summed. The average value of the items for a subscale was computed for missing values, as long as ⬎50% of the questions in the subscale were answered. Patients were analyzed by the following pretreatment factors: age, KPS, gender, race, primary disease site, marital status, education level, income level, and employment status. This information was derived from the RTOG pretreatment and demographic forms. Age and KPS were analyzed as continuous variables. The other covariates were divided up as shown in Table 1.
Statistical analysis Means and standard deviations were calculated. Tests between two groups were performed using a t test. Tests between multiple groups were performed using analysis of variance. Multivariate analysis was performed using a stepwise linear regression model. As a “significant” p value (p ⬍ 0.05) is not necessarily clinically meaningful, the standard error of measurement (SEM) was used as an indicator of clinically meaningful differences. SEM is calcu-
● B. MOVSAS et al.
831
lated as the baseline standard deviation of all scores multiplied by the square root of 1 minus the reliability (1). The reliability was obtained from Cella et al. (8).
RESULTS Table 1 shows the breakdown of the patient population by the pretreatment characteristics. Overall, 51% of patients were ⱕ60 years of age. The majority of the patients had head-andneck cancer (80%), were male (79%), white (75%), and had a KPS of 80 –100 (90%). Thirty-five percent were full-time employees and 36% were retired. Sixty-eight percent of patients had an income level ⬍$25,000. Patients who were married or had a live-in relationship comprised 56% of the population. Thirty-five percent were college graduates. Table 2 lists the mean, range, standard deviation, and SEM for the overall FACT-G scores and its five components. Overall, the mean FACT-G score for the 1428 patients was 86.0 with a SEM of 5.3. Statistically significant differences in the overall FACT-G scores were observed based on age, race, KPS, marital status, education level, income level, and employment status, but not by gender or primary disease site (Table 1). For each pretreatment factor showing a statistically significant difference, an arrow has been placed to indicate clinically meaningful break points. For example, based on the SEM of 5.3, a clinically meaningful difference was found between patients ⱕ50 years (mean FACT-G ⱕ83.3) and patients ⱖ65 years (mean FACT-G ⱖ89). Similarly, an arrow delineates a clinically meaningful difference in QOL between whites (FACT-G 87.5) and Hispanics (FACT-G 78.3). The difference between whites and blacks is borderline. There is a relative linear increase in FACT-G with increasing KPS (p ⬍ 0.0001), with a clinically meaningful cutoff at KPS 80. Patients who were married/live-in relationship had a better QOL than single/widowed/divorced patients. College graduates had the best overall QOL (p ⬍ 0.0001), with a clinically meaningful difference between college graduates and those with some high school education. Both employment status and income level showed clinically meaningful different cutoffs in overall QOL. Overall, there was no difference in FACT-G based on gender. However, in lung cancer, a clinically meaningful difference in pretreatment QOL was observed between males (FACT 87.3) and females (FACT 78.6). Table 3 summarizes which pretreatment factors were associated with clinically meaningful differences (based on the SEM) for the overall FACT-G scores, as well as its five dimensions. Because of space limitations, the data for the separate dimensions could not be shown. Overall, the findings for PWB and FWB mostly paralleled those for the overall FACT-G score. However, the only pretreatment factor positively influencing SWB was increased income and the only factor positively influencing EWB was increased age. Table 4 shows the results of the multivariate analysis. KPS and age were analyzed as continuous variables with parameter estimates of 0.395 and 0.197, respectively. Thus, as KPS increased by 10 points, FACT-G increased by 3.95
832
I. J. Radiation Oncology
● Biology ● Physics
Volume 65, Number 3, 2006
Table 1. Pretreatment FACT scores (n ⫽ 1,428)
Abbreviations: FACT ⫽ Functional Assessment of Cancer Therapy; NS ⫽ not significant; SD ⫽ standard deviation. Arrows indicate clinically meaningful changes based on a SEM of 5.3.
Quality of life in cancer patients
● B. MOVSAS et al.
Table 2. Results: pretreatment FACT scores (n ⫽ 1,428)
Overall FACT-G score Physical well-being Social-family well-being Relationship with doctor Emotional well-being Functional well-being
833
Table 4. Multivariate analysis
Mean
Standard deviation
Range
SEM
86.0 22.5 22.8 7.4 15.2 18.1
15.9 5.2 25.1 1.2 3.8 6.8
20.7–112 0–28 4.7–28 0–8 0–20 0–28
5.3 2.2 2.8 0.7 1.9 3.0
KPS Age Marital status Race (Whites vs. Hispanics) Education (⬍HS vs. College) Income (⬍$8,000 vs. ⬎$50,000)
DISCUSSION A challenge with all statistical analyses is that a “significant” p value does not necessarily correlate with a clinically meaningful change (11). This is particularly an issue in QOL research, in which many data points are generated. To overcome this problem, two basic strategies for QOL analyses have been proposed: anchor-based and distributionbased (1). Of the distribution methods, using the SEM as an indicator of clinically meaningful differences has the advantage of being sample independent. Indeed, the SEM is one distribution-based method that is emerging as an instrument that could add information on the clinical importance of QOL changes. For example, in several recent studies ranging from patients with heart disease, chronic obstructive pulmonary disease, and asthma to lung cancer, the SEM has consistently corresponded to anchor-based measures of minimal clini-
Table 3. Clinically meaningful differences (based on SEM)
Overall PWB FWB SWB EWB
* * *
Race
KPS
* * *
* * *
0.0001 0.0001 0.0001 0.0006 0.004 0.006
cally important differences in QOL endpoints (12–15). It remains unclear, however, whether the minimal clinically important differences standard can be reliably extrapolated to other diseases (14, 15). In a lung cancer study, Cella et al. reported clinically meaningful changes using both clinical anchors and distribution-based criteria (including the SEM) to establish thresholds for the FACT lung instrument (12). Interestingly, using the Global Rating of Change scale as an anchor, Cella et al. recently proposed that a clinically meaningful change corresponds to a “total FACT-G raw score change in the range of 5–7 points” (16). This value corresponds well to the SEM (for FACT-G) in this study of 5.3. Nevertheless, in the words of Wyrwich et al., “there is no universally accepted approach for determining the clinical significance of QOL data” (11). Several prior studies analyzed the impact of pre-treatment factors on QOL with mixed findings. Jordhoy et al. studied 396 patients with advanced cancer utilizing the European Organization for Research and Treatment of Cancer’s Quality of Life Questionnaire-C30 instrument (17). Similar to the results of this study, they reported significantly improved emotional functioning with increasing age. KPS was significantly associated with most functioning scores and only minor differences in QOL were based on gender. However, Jordhoy et al. (17) found no clear impact of living with a partner or of a higher education. This discrepancy may be due to their smaller sample size or that their study population comprised severely ill and incurable patients with an overall median survival of only 13 weeks. Wan et al. studied the influence of clinical and patient factors, including personal expectations, on QOL (via FACT-G) in 466 cancer patients (18). On multivariate analysis, they found that five variables were associated with sta-
points (p ⬍ 0.0001). As age increased per year, FACT-G increased by 0.2 (p ⬍ 0.0001). For marital status, the parameter estimate was ⫺2.04 (p ⬍ 0.0001). Single people had a reduction of 2.04 points in overall QOL. The difference in pretreatment QOL between Hispanics and whites was significant in the model, but only borderline for blacks vs. whites. Hispanics had a 6.93-point drop in FACT-G compared with whites (p ⫽ 0.0006). Lower income (⬍$8,000) was associated with a 2.83-point drop in FACT-G compared with higher income (⬎$50,000), p ⫽ 0.006. Lower education (⬍ high school) was associated with a 3.2 drop in global QOL compared with college graduates (p ⫽ 0.004).
Gender
⬍ ⬍ ⬍ ⫽ ⫽ ⫽
Abbreviations: HS ⫽ high school; KPS ⫽ Karnofsky performance status.
Abbreviations: FACT ⫽ Functional Assessment of Cancer Therapy; SEM ⫽ standard error of measurement.
Age
p p p p p p
Type of primary *
Marital status
Educ. level
Income level
Employment status
*
*
* * * *
* * *
*
Abbreviations: PWB ⫽ physical well-being; FWB ⫽ functional well-being; SWB ⫽ social well-being; EWB ⫽ emotional well-being; KPS ⫽ Karnofsky performance status. * Clinically meaningful differences (based on SEM).
834
I. J. Radiation Oncology
● Biology ● Physics
tistically improved QOL: (1) older age, (2) increased performance status, (3) living with others, (4) managed care enrollment, and (5) positive expectations rating. The first three factors are consistent with this study; the latter two were not part of this analysis. Why do studies consistently demonstrate that older cancer patients have improved QOL compared with younger patients? Based on normative data, older people report decreased functioning and increased symptoms (19). This finding is likely related to increased comorbidities in the elderly (20). As in this analysis, however, population-based normative studies have also reported that older age correlates with improved emotional functioning (20, 21). In light of this, it is not too surprising that the devastating impact of a cancer diagnosis at a young age is so emotionally overbearing that younger patients with cancer have decreased QOL compared with older cancer patients. Indeed, QOL has been defined as the gap between an individual’s expectations and actual experience (22). Thus a young person who is “not supposed to get cancer” would likely report a lower QOL compared with an elderly patient. In this study, the only factor positively influencing EWB was increased age. Similarly, in Jordhoy et al.’s analysis, they found that older cancer patients had significantly improved emotional functioning (17). Mor et al. also reported that older patients with cancer have less psychosocial problems compared with younger cancer patients (23). In a separate analysis, Wan et al. studied the impact of sociocultural and clinical factors on QOL (via FACT) among 761 Hispanic and African-American patients (24). They found that performance status and spiritual beliefs were consistent predictors of overall QOL. However, among these patients, there was no clear effect of socioeconomic status (SES), gender, age, living arrangement, or insurance status. In light of the findings in the present analysis, showing differences in QOL based on race, SES, and living arrangement, it is important that further studies be done to better understand these phenomena. Prior studies have shown conflicting results regarding the impact of race and SES on outcome. For example, Axtell et al. reported decreased survival in blacks compared with whites (25). Based on Surveillance Epidemiology and End Results (SEER) data, McWhorter et al. found that race differences were largely due to the underlying SES (26). More recently, Cella et al. reported that low annual income (⬍$5,000) and low education level (grade school only), but not race, predicted for poor survival
Volume 65, Number 3, 2006
among cancer patients (27). However, few studies have directly studied the issue of QOL as it relates to race. Several caveats must be kept in mind when interpreting this study. This study assessed only the baseline (pretreatment) QOL. Because QOL likely changes over time, a longitudinal study is necessary to analyze such temporal changes in QOL. Indeed, “response shifts” may occur when there are changes over time in patient’s internal standards regarding QOL (28). Patients may “get used to” certain toxicities related to treatment over time and view these as having less impact on their QOL. Recently, methods to assess the “response shift” phenomenon are emerging (29). Moreover, this study mostly included head-and-neck cancer patients, such that potential differences between disease sites may not have been discernible. As well, relatively few females (20%) and minority patients (e.g., only 4% Hispanics) were part of this analysis. Thus it should not be assumed that these findings can be generalized to other cancer patient populations. Finally, there is ongoing controversy related to the interpretation of a distribution-based statistical approach (e.g., based on SEM). This methodology has been used to analyze QOL changes in the longitudinal evaluation of patients (12–15), yet its role in assessing interindividual QOL changes remains to be determined. In conclusion, most pretreatment factors meaningfully influenced the baseline QOL in cancer patients. The fact that QOL outcomes may be confounded by pretreatment variables should be considered in designing QOL studies and statistical analyses. The improved QOL in married patients, college graduates, and those with higher income may reflect better support mechanisms. Other studies in the general population previously demonstrated that having a higher education or being married (rather than living alone) had a positive impact on QOL (20, 21). Accrual of minority patients, especially Hispanics, to clinical trials must be increased. Of particular concern is that the Hispanics that did enroll to this trial were found to have particularly low pretreatment QOL. An Intercultural Cancer Council has been set up by the National Cancer Institute to review challenges specific to minorities, the medically underserved and cancer patients (30). Similarly, RTOG has established a minority recruitment task force to enhance enrollment of minority patients. The potentially devastating impact of a cancer diagnosis, particularly in young and minority patients, must be addressed.
REFERENCES 1. Movsas B. Quality of life in oncology trials: A clinical guide. Semin Radiat Oncol 2003;13:235–247. 2. Coates A, Porzsolt F, Osoba D. Quality of life in oncology practice: Prognostic value of EORTC QLQ-C30 scores in patients with advanced malignancy. Eur J Cancer 1997;33: 1025–1030. 3. Fu KK, Pajak TF, Trotti A, et al. A Radiation Therapy Oncology Group (RTOG) Phase III Randomized study to
compare hyperfractionation and two variants of accelerated fractionation to standard fractionation radiotherapy for head and neck squamous cell carcinomas: First report of RTOG 9003. Int J Radiat Oncol Biol Phys 2000;48:7–16. 4. Forastiere AA, Goepfert H, Maor M, et al. Concurrent chemotherapy and radiotherapy for organ preservation in advanced laryngeal cancer. N Engl J Med 2003;349:2091–2098. 5. Minsky BD, Pajak TF, Ginsberg RJ, et al. INT 0123 (Radia-
Quality of life in cancer patients
6.
7.
8. 9. 10. 11. 12.
13.
14.
15.
16.
tion Therapy Oncology Group 94-05) phase III trial of combined-modality therapy for esophageal cancer: High-dose versus standard-dose radiation therapy. J Clin Oncol 2002;20:1167–1174. Johnstone DW, Byhardt RW, Ettinger D, et al. Phase III study comparing chemotherapy and radiotherapy with preoperative chemotherapy and surgical resection in patients with nonsmall-cell lung cancer with spread to mediastinal lymph nodes (N2); final report of RTOG 89-01. Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys 2002;54:365–369. Lawton CA, Coleman CN, Buzydlowski JW, et al. Results of a phase II trial of external beam radiation with etanidazole (SR 2508) for the treatment of locally advanced prostate cancer (RTOG Protocol 90-20). Int J Radiat Oncol Biol Phys 1996;36: 673– 680. Cella DF, Tulsky DS, Gray G, et al. The functional assessment of cancer therapy scale: Development and validation of the general measure. J Clin Oncol 1993;11:570 –579. Cella DF. Manual for the functional assessment of cancer therapy scales, version I. Chicago: Rush-Presbyterian-St. Luke’s Medical Center, 1988. Likert RA. A technique for the measurement of attitudes. Arch Psychol 1932;140:5. Wyrwich K, Bullinger M, Aaronson N, et al. Estimating clinically significant differences in quality of life outcomes. Qual Life Res 2005;14:285–295. Cella DF, Eton D, Fairclough D, et al. What is Clinically meaningful change (CMC) on the Functional Assessment of Cancer Therapy–Lung (FACT-L) Questionaire? An analysis of date from ECOG 5592. J Clin Epidemiol 2002;55:286 –295. Wyrwich K, Nienaber N, Tierney W, et al. Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life. Med Care 1999;37:469 – 478. Wyrwich K, Tierney W, Wolinsky F. Further evidence supporting a SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. J Clin Epidemiol 1999;52:861– 873. Wyrwich K, Tierney W, Wollinsky F. Using the standard error of measurement to identify important intra-individual change on the Asthma Quality of Life Questionnaire. Qual Life Res 2002;11:1–7. Cella DF, Hahn EA, Dineen K. Meaningful change in cancerspecific quality of life scores: Differences between improvement and worsening. Qual Life Res 2002;11:207–221.
● B. MOVSAS et al.
835
17. Jordhoy MS, Fayers P, Loge JH, et al. Quality of life in advanced cancer patients: The impact of sociodemographic and medical characteristics. Br J Cancer 2001;85:1478 –1485. 18. Wan G, Counte M, Cella DF. The influence of personal expectations on cancer patients’ reports of health-related quality of life. Psycho-Oncology 1997;6:1–11. 19. Hjermstad M, Fayers P, Bjordal K, et al. Health-related quality of life in the general Norwegian population assessed by the EORTC core Quality of Life Questionnaire—the QLQ-C30 (⫹3). J Clin Oncol 1998;16:1188 –1196. 20. Michelson H, Bolund C, Nilsson B, et al. Health-related quality of life measured by the EORTC QLQ-C30 —reference values from a large sample of Swedish population. Acta Oncol 2000;39:477– 484. 21. Klee M, Groenvold M, Machin D. Quality of life of Danish women; population-based norms for the EORTC QLQ-C30. Qual Life Res 1997;6:27–34. 22. Calman KC. Quality of life in cancer patient—An hypothesis. J Med Ethics 1984;10:124 –127. 23. Mor V, Allen S, Malin M. The psychosocial impact of cancer on older versus younger patients and their families. Cancer 1994;74:2118 –2127. 24. Wan G, Counte M, Cella DF, et al. The impact of sociocultural and clinical factors on health-related quality of life reports among Hispanic and African-American cancer patients. J Outcome Measurements 1999;3:200 –215. 25. Axtell LM, Myers MH. Contrasts in survival of black and white cancer patients, 1960 –73. J Natl Cancer Inst 1978;60: 1209 –1215. 26. McWhorter WP, Schatzkin AG, et al. Contribution of socioeconomic status to black/white differences in cancer incidence. Cancer 1989;63:982–987. 27. Cella DF, Orav EJ, Kornblith AB, et al. Socioeconomic status and cancer survival. J Clin Oncol 1991;9:1500 –1509. 28. Sprangers M, Schwartz C. The challenge of response shift for quality-of-life-based clinical oncology research. Ann Oncol 1999;10:747–749. 29. O’Boyle C, McGee H, Browne J. Measuring response shift using the schedule for evaluation of individual quality of life. In: Schwartz C, Sprangers M, editors. Adaptation to changing health: Response shift in quality-of-life research. Washington, DC: American Psychological Association; 2000. pp. 123–136. 30. Leffall LS. 7th biennial symposium on minorities, the medically underserved and cancer. Foreword. Cancer 2001;91(Suppl. 1): 189 –192.
doi:10.1016/j.ijrobp.2006.01.004
CLINICAL INVESTIGATION
Quality of Life
PRETREATMENT FACTORS SIGNIFICANTLY INFLUENCE QUALITY OF LIFE IN CANCER PATIENTS: A RADIATION THERAPY ONCOLOGY GROUP (RTOG) ANALYSIS BENJAMIN MOVSAS, M.D.,* CHARLES SCOTT, PH.D.,†
AND
DEBORAH WATKINS-BRUNER, PH.D.‡
*Radiation Oncology, Henry Ford Health System, Detroit, MI, †RTOG Headquarters, Philadelphia, PA, ‡Fox Chase Cancer Center, Philadelphia, PA Purpose: The purpose of this analysis was to assess the impact of pretreatment factors on quality of life (QOL) in cancer patients. Methods and Materials: Pretreatment QOL (via Functional Assessment of Cancer Therapy [FACT], version 2) was obtained in 1,428 patients in several prospective Radiation Therapy Oncology Group (RTOG) trials including nonmetastatic head-and-neck (n ⴝ 1139), esophageal (n ⴝ 174), lung (n ⴝ 51), rectal (n ⴝ 47), and prostate (n ⴝ 17) cancer patients. Clinically meaningful differences between groups were defined as a difference of 1 standard error of measurement (SEM). Results: The mean FACT score for all patients was 86 (20.7–112) with SEM of 5.3. Statistically significant differences in QOL were observed based on age, race, Karnofsky Performance Status, marital status, education level, income level, and employment status, but not by gender or primary site. Using the SEM, there were clinically meaningful differences between patients <50 years vs. >65 years. Hispanics had worse QOL than whites. FACT increased linearly with higher Karnofsky Performance Status and income levels. Married patients (or live-in relationships) had a better QOL than single, divorced, or widowed patients. College graduates had better QOL than those with less education. Conclusion: Most pretreatment factors meaningfully influenced baseline QOL. The potentially devastating impact of a cancer diagnosis, particularly in young and minority patients, must be addressed. © 2006 Elsevier Inc. Quality of life, Cancer, Pretreatment factors.
INTRODUCTION
with more than 1,400 patients, most of whom were male and had head-and-neck cancer, was analyzed to determine the impact of pretreatment factors on QOL in cancer patients.
Over the last few decades, quality of life (QOL) has become recognized as a critical component of clinical oncology trials (1). Studies have demonstrated that pretreatment QOL is itself a powerful prognostic factor for survival (2). QOL provides direct, patient-derived feedback, beyond the traditional clinical outcomes, that can aid in interpreting the results of a study. Indeed, in 1988, the National Cancer Institute declared in its mission statement that “research aimed at improving survival and QOL for persons with cancer is of the highest priority to the cancer therapy evaluation program” (1). Although multiple studies have documented the reliability and validity of various QOL instruments in oncology trials, few reports have analyzed the factors that influence QOL itself in cancer patients. This is critical information, because QOL outcomes may be confounded by pretreatment factors, which should then be factored into the design of QOL studies. The Radiation Therapy Oncology Group (RTOG) Functional Assessment of Cancer Therapy (FACT) database
Pretreatment QOL (via FACT, version 2) was obtained in 1,428 (nonmetastatic) cancer patients in six prospective RTOG trials: head-and-neck cancer (n ⫽ 1,139 from RTOG 9003 and RTOG 9111), esophageal cancer (n ⫽ 174 from RTOG 9405), lung cancer (n ⫽ 51 from RTOG 8901), rectal cancer (n ⫽ 47 from RTOG 9401) and prostate cancer (n ⫽ 17 from RTOG 9020). In all cases, the baseline QOL questionnaire was obtained before initiation of protocol treatment, with the vast majority filled out within 1–2 weeks before initiation of therapy. RTOG 9003 (3) was a randomized trial to compare various radiation fractionation schema with standard once-daily fractionation for squamous cell carcinoma of the head and neck, including the oral cavity, oropharynx, hypopharynx, and supraglottic larynx. Eligibility stipulated that patients had to have Stage III or IV disease (except for base of tongue or hypopharynx primaries, for which Stage II was eligible) and a Karnofsky Performance Status (KPS) score ⱖ60. RTOG 9111 (4)
Reprint requests to: Benjamin Movsas, M.D., Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Boulevard, Detroit, MI 48202. Tel: (313) 916-5188; Fax: (313)
916-3235; E-mail: [email protected] Received Aug 5, 2005, and in revised form Jan 10, 2006. Accepted for publication Jan 11, 2006.
METHODS AND MATERIALS
830
Quality of life in cancer patients
was a randomized trial comparing concurrent chemotherapy and radiation for preservation of the larynx in locally advanced laryngeal cancer. The trial compared induction chemotherapy and radiation vs. concurrent chemoradiation vs. radiation therapy alone. The eligibility criteria included Stage III or IV squamous cell carcinoma of the glottic or supraglottic larynx, the surgical treatment of which would require total laryngectomy, and a KPS of ⱖ60. RTOG 9405 (5) studied combined modality therapy for carcinoma of the esophagus and randomized patients to high dose vs. conventional dose radiation therapy (64.8 Gy vs. 50.4 Gy). Eligibility required squamous cell or adenocarcinoma of the esophagus with no tumor extension within 2 cm proximal to the stomach and KPS ⱖ60. RTOG 89-01 (6) was a randomized trial in patients with non–small-cell lung cancer with mediastinal nodal involvement (pathologic Stage IIIA disease), in which all patients received induction chemotherapy, and were then randomized to surgery or radiation therapy, followed in all patients by consolidation chemotherapy. RTOG 94-01 was an intergroup randomized trial of preoperative vs. postoperative combined modality therapy for resectable adenocarcinoma of the rectum. Eligibility mandated KPS ⱖ60 and that the distal border of the tumor be ⱕ12 cm from the anal verge. RTOG 9020 (7) was a Phase II trial of radiation with Etanidazole (SR-2508), a hypoxic cell sensitizer, for the treatment of locally advanced prostate cancer. Patients with biopsy proven adenocarcinoma of the prostate with locally advanced T2B-T4 tumors were eligible for this study. The FACT-General (FACT-G) is a reliable and valid instrument to measure QOL in cancer patients (8). FACT-G consists of five dimensions. Emotional well-being (EWB) contains five items (range, 0 –20) that pertain to psychologic distress or symptoms, such as depression or anxiety. Relationship with physician contains two items (range, 0 – 8) that relate to the doctor’s interpersonal skills and competence. The other three dimensions each contain seven items (range, 0 –28). Physical well-being (PWB) describes the presence of physical symptoms (such as fatigue, pain or nausea), functional well-being (FWB) measures one’s mobility and ability to perform daily activities and social well-being (SWB) refers to participation in family relationships, sexuality, and social interactions. Each of these variables, as well as the overall QOL score (an aggregate score consisting of 28 items, range, 0 –112), was calculated according to the scoring instructions for FACT (9). Briefly, Likert’s method for summated rating scores was used to measure responses (10). After reversing the scoring of negatively worded items (so that a higher score always indicated a favorable response), item responses were summed. The average value of the items for a subscale was computed for missing values, as long as ⬎50% of the questions in the subscale were answered. Patients were analyzed by the following pretreatment factors: age, KPS, gender, race, primary disease site, marital status, education level, income level, and employment status. This information was derived from the RTOG pretreatment and demographic forms. Age and KPS were analyzed as continuous variables. The other covariates were divided up as shown in Table 1.
Statistical analysis Means and standard deviations were calculated. Tests between two groups were performed using a t test. Tests between multiple groups were performed using analysis of variance. Multivariate analysis was performed using a stepwise linear regression model. As a “significant” p value (p ⬍ 0.05) is not necessarily clinically meaningful, the standard error of measurement (SEM) was used as an indicator of clinically meaningful differences. SEM is calcu-
● B. MOVSAS et al.
831
lated as the baseline standard deviation of all scores multiplied by the square root of 1 minus the reliability (1). The reliability was obtained from Cella et al. (8).
RESULTS Table 1 shows the breakdown of the patient population by the pretreatment characteristics. Overall, 51% of patients were ⱕ60 years of age. The majority of the patients had head-andneck cancer (80%), were male (79%), white (75%), and had a KPS of 80 –100 (90%). Thirty-five percent were full-time employees and 36% were retired. Sixty-eight percent of patients had an income level ⬍$25,000. Patients who were married or had a live-in relationship comprised 56% of the population. Thirty-five percent were college graduates. Table 2 lists the mean, range, standard deviation, and SEM for the overall FACT-G scores and its five components. Overall, the mean FACT-G score for the 1428 patients was 86.0 with a SEM of 5.3. Statistically significant differences in the overall FACT-G scores were observed based on age, race, KPS, marital status, education level, income level, and employment status, but not by gender or primary disease site (Table 1). For each pretreatment factor showing a statistically significant difference, an arrow has been placed to indicate clinically meaningful break points. For example, based on the SEM of 5.3, a clinically meaningful difference was found between patients ⱕ50 years (mean FACT-G ⱕ83.3) and patients ⱖ65 years (mean FACT-G ⱖ89). Similarly, an arrow delineates a clinically meaningful difference in QOL between whites (FACT-G 87.5) and Hispanics (FACT-G 78.3). The difference between whites and blacks is borderline. There is a relative linear increase in FACT-G with increasing KPS (p ⬍ 0.0001), with a clinically meaningful cutoff at KPS 80. Patients who were married/live-in relationship had a better QOL than single/widowed/divorced patients. College graduates had the best overall QOL (p ⬍ 0.0001), with a clinically meaningful difference between college graduates and those with some high school education. Both employment status and income level showed clinically meaningful different cutoffs in overall QOL. Overall, there was no difference in FACT-G based on gender. However, in lung cancer, a clinically meaningful difference in pretreatment QOL was observed between males (FACT 87.3) and females (FACT 78.6). Table 3 summarizes which pretreatment factors were associated with clinically meaningful differences (based on the SEM) for the overall FACT-G scores, as well as its five dimensions. Because of space limitations, the data for the separate dimensions could not be shown. Overall, the findings for PWB and FWB mostly paralleled those for the overall FACT-G score. However, the only pretreatment factor positively influencing SWB was increased income and the only factor positively influencing EWB was increased age. Table 4 shows the results of the multivariate analysis. KPS and age were analyzed as continuous variables with parameter estimates of 0.395 and 0.197, respectively. Thus, as KPS increased by 10 points, FACT-G increased by 3.95
832
I. J. Radiation Oncology
● Biology ● Physics
Volume 65, Number 3, 2006
Table 1. Pretreatment FACT scores (n ⫽ 1,428)
Abbreviations: FACT ⫽ Functional Assessment of Cancer Therapy; NS ⫽ not significant; SD ⫽ standard deviation. Arrows indicate clinically meaningful changes based on a SEM of 5.3.
Quality of life in cancer patients
● B. MOVSAS et al.
Table 2. Results: pretreatment FACT scores (n ⫽ 1,428)
Overall FACT-G score Physical well-being Social-family well-being Relationship with doctor Emotional well-being Functional well-being
833
Table 4. Multivariate analysis
Mean
Standard deviation
Range
SEM
86.0 22.5 22.8 7.4 15.2 18.1
15.9 5.2 25.1 1.2 3.8 6.8
20.7–112 0–28 4.7–28 0–8 0–20 0–28
5.3 2.2 2.8 0.7 1.9 3.0
KPS Age Marital status Race (Whites vs. Hispanics) Education (⬍HS vs. College) Income (⬍$8,000 vs. ⬎$50,000)
DISCUSSION A challenge with all statistical analyses is that a “significant” p value does not necessarily correlate with a clinically meaningful change (11). This is particularly an issue in QOL research, in which many data points are generated. To overcome this problem, two basic strategies for QOL analyses have been proposed: anchor-based and distributionbased (1). Of the distribution methods, using the SEM as an indicator of clinically meaningful differences has the advantage of being sample independent. Indeed, the SEM is one distribution-based method that is emerging as an instrument that could add information on the clinical importance of QOL changes. For example, in several recent studies ranging from patients with heart disease, chronic obstructive pulmonary disease, and asthma to lung cancer, the SEM has consistently corresponded to anchor-based measures of minimal clini-
Table 3. Clinically meaningful differences (based on SEM)
Overall PWB FWB SWB EWB
* * *
Race
KPS
* * *
* * *
0.0001 0.0001 0.0001 0.0006 0.004 0.006
cally important differences in QOL endpoints (12–15). It remains unclear, however, whether the minimal clinically important differences standard can be reliably extrapolated to other diseases (14, 15). In a lung cancer study, Cella et al. reported clinically meaningful changes using both clinical anchors and distribution-based criteria (including the SEM) to establish thresholds for the FACT lung instrument (12). Interestingly, using the Global Rating of Change scale as an anchor, Cella et al. recently proposed that a clinically meaningful change corresponds to a “total FACT-G raw score change in the range of 5–7 points” (16). This value corresponds well to the SEM (for FACT-G) in this study of 5.3. Nevertheless, in the words of Wyrwich et al., “there is no universally accepted approach for determining the clinical significance of QOL data” (11). Several prior studies analyzed the impact of pre-treatment factors on QOL with mixed findings. Jordhoy et al. studied 396 patients with advanced cancer utilizing the European Organization for Research and Treatment of Cancer’s Quality of Life Questionnaire-C30 instrument (17). Similar to the results of this study, they reported significantly improved emotional functioning with increasing age. KPS was significantly associated with most functioning scores and only minor differences in QOL were based on gender. However, Jordhoy et al. (17) found no clear impact of living with a partner or of a higher education. This discrepancy may be due to their smaller sample size or that their study population comprised severely ill and incurable patients with an overall median survival of only 13 weeks. Wan et al. studied the influence of clinical and patient factors, including personal expectations, on QOL (via FACT-G) in 466 cancer patients (18). On multivariate analysis, they found that five variables were associated with sta-
points (p ⬍ 0.0001). As age increased per year, FACT-G increased by 0.2 (p ⬍ 0.0001). For marital status, the parameter estimate was ⫺2.04 (p ⬍ 0.0001). Single people had a reduction of 2.04 points in overall QOL. The difference in pretreatment QOL between Hispanics and whites was significant in the model, but only borderline for blacks vs. whites. Hispanics had a 6.93-point drop in FACT-G compared with whites (p ⫽ 0.0006). Lower income (⬍$8,000) was associated with a 2.83-point drop in FACT-G compared with higher income (⬎$50,000), p ⫽ 0.006. Lower education (⬍ high school) was associated with a 3.2 drop in global QOL compared with college graduates (p ⫽ 0.004).
Gender
⬍ ⬍ ⬍ ⫽ ⫽ ⫽
Abbreviations: HS ⫽ high school; KPS ⫽ Karnofsky performance status.
Abbreviations: FACT ⫽ Functional Assessment of Cancer Therapy; SEM ⫽ standard error of measurement.
Age
p p p p p p
Type of primary *
Marital status
Educ. level
Income level
Employment status
*
*
* * * *
* * *
*
Abbreviations: PWB ⫽ physical well-being; FWB ⫽ functional well-being; SWB ⫽ social well-being; EWB ⫽ emotional well-being; KPS ⫽ Karnofsky performance status. * Clinically meaningful differences (based on SEM).
834
I. J. Radiation Oncology
● Biology ● Physics
tistically improved QOL: (1) older age, (2) increased performance status, (3) living with others, (4) managed care enrollment, and (5) positive expectations rating. The first three factors are consistent with this study; the latter two were not part of this analysis. Why do studies consistently demonstrate that older cancer patients have improved QOL compared with younger patients? Based on normative data, older people report decreased functioning and increased symptoms (19). This finding is likely related to increased comorbidities in the elderly (20). As in this analysis, however, population-based normative studies have also reported that older age correlates with improved emotional functioning (20, 21). In light of this, it is not too surprising that the devastating impact of a cancer diagnosis at a young age is so emotionally overbearing that younger patients with cancer have decreased QOL compared with older cancer patients. Indeed, QOL has been defined as the gap between an individual’s expectations and actual experience (22). Thus a young person who is “not supposed to get cancer” would likely report a lower QOL compared with an elderly patient. In this study, the only factor positively influencing EWB was increased age. Similarly, in Jordhoy et al.’s analysis, they found that older cancer patients had significantly improved emotional functioning (17). Mor et al. also reported that older patients with cancer have less psychosocial problems compared with younger cancer patients (23). In a separate analysis, Wan et al. studied the impact of sociocultural and clinical factors on QOL (via FACT) among 761 Hispanic and African-American patients (24). They found that performance status and spiritual beliefs were consistent predictors of overall QOL. However, among these patients, there was no clear effect of socioeconomic status (SES), gender, age, living arrangement, or insurance status. In light of the findings in the present analysis, showing differences in QOL based on race, SES, and living arrangement, it is important that further studies be done to better understand these phenomena. Prior studies have shown conflicting results regarding the impact of race and SES on outcome. For example, Axtell et al. reported decreased survival in blacks compared with whites (25). Based on Surveillance Epidemiology and End Results (SEER) data, McWhorter et al. found that race differences were largely due to the underlying SES (26). More recently, Cella et al. reported that low annual income (⬍$5,000) and low education level (grade school only), but not race, predicted for poor survival
Volume 65, Number 3, 2006
among cancer patients (27). However, few studies have directly studied the issue of QOL as it relates to race. Several caveats must be kept in mind when interpreting this study. This study assessed only the baseline (pretreatment) QOL. Because QOL likely changes over time, a longitudinal study is necessary to analyze such temporal changes in QOL. Indeed, “response shifts” may occur when there are changes over time in patient’s internal standards regarding QOL (28). Patients may “get used to” certain toxicities related to treatment over time and view these as having less impact on their QOL. Recently, methods to assess the “response shift” phenomenon are emerging (29). Moreover, this study mostly included head-and-neck cancer patients, such that potential differences between disease sites may not have been discernible. As well, relatively few females (20%) and minority patients (e.g., only 4% Hispanics) were part of this analysis. Thus it should not be assumed that these findings can be generalized to other cancer patient populations. Finally, there is ongoing controversy related to the interpretation of a distribution-based statistical approach (e.g., based on SEM). This methodology has been used to analyze QOL changes in the longitudinal evaluation of patients (12–15), yet its role in assessing interindividual QOL changes remains to be determined. In conclusion, most pretreatment factors meaningfully influenced the baseline QOL in cancer patients. The fact that QOL outcomes may be confounded by pretreatment variables should be considered in designing QOL studies and statistical analyses. The improved QOL in married patients, college graduates, and those with higher income may reflect better support mechanisms. Other studies in the general population previously demonstrated that having a higher education or being married (rather than living alone) had a positive impact on QOL (20, 21). Accrual of minority patients, especially Hispanics, to clinical trials must be increased. Of particular concern is that the Hispanics that did enroll to this trial were found to have particularly low pretreatment QOL. An Intercultural Cancer Council has been set up by the National Cancer Institute to review challenges specific to minorities, the medically underserved and cancer patients (30). Similarly, RTOG has established a minority recruitment task force to enhance enrollment of minority patients. The potentially devastating impact of a cancer diagnosis, particularly in young and minority patients, must be addressed.
REFERENCES 1. Movsas B. Quality of life in oncology trials: A clinical guide. Semin Radiat Oncol 2003;13:235–247. 2. Coates A, Porzsolt F, Osoba D. Quality of life in oncology practice: Prognostic value of EORTC QLQ-C30 scores in patients with advanced malignancy. Eur J Cancer 1997;33: 1025–1030. 3. Fu KK, Pajak TF, Trotti A, et al. A Radiation Therapy Oncology Group (RTOG) Phase III Randomized study to
compare hyperfractionation and two variants of accelerated fractionation to standard fractionation radiotherapy for head and neck squamous cell carcinomas: First report of RTOG 9003. Int J Radiat Oncol Biol Phys 2000;48:7–16. 4. Forastiere AA, Goepfert H, Maor M, et al. Concurrent chemotherapy and radiotherapy for organ preservation in advanced laryngeal cancer. N Engl J Med 2003;349:2091–2098. 5. Minsky BD, Pajak TF, Ginsberg RJ, et al. INT 0123 (Radia-
Quality of life in cancer patients
6.
7.
8. 9. 10. 11. 12.
13.
14.
15.
16.
tion Therapy Oncology Group 94-05) phase III trial of combined-modality therapy for esophageal cancer: High-dose versus standard-dose radiation therapy. J Clin Oncol 2002;20:1167–1174. Johnstone DW, Byhardt RW, Ettinger D, et al. Phase III study comparing chemotherapy and radiotherapy with preoperative chemotherapy and surgical resection in patients with nonsmall-cell lung cancer with spread to mediastinal lymph nodes (N2); final report of RTOG 89-01. Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys 2002;54:365–369. Lawton CA, Coleman CN, Buzydlowski JW, et al. Results of a phase II trial of external beam radiation with etanidazole (SR 2508) for the treatment of locally advanced prostate cancer (RTOG Protocol 90-20). Int J Radiat Oncol Biol Phys 1996;36: 673– 680. Cella DF, Tulsky DS, Gray G, et al. The functional assessment of cancer therapy scale: Development and validation of the general measure. J Clin Oncol 1993;11:570 –579. Cella DF. Manual for the functional assessment of cancer therapy scales, version I. Chicago: Rush-Presbyterian-St. Luke’s Medical Center, 1988. Likert RA. A technique for the measurement of attitudes. Arch Psychol 1932;140:5. Wyrwich K, Bullinger M, Aaronson N, et al. Estimating clinically significant differences in quality of life outcomes. Qual Life Res 2005;14:285–295. Cella DF, Eton D, Fairclough D, et al. What is Clinically meaningful change (CMC) on the Functional Assessment of Cancer Therapy–Lung (FACT-L) Questionaire? An analysis of date from ECOG 5592. J Clin Epidemiol 2002;55:286 –295. Wyrwich K, Nienaber N, Tierney W, et al. Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life. Med Care 1999;37:469 – 478. Wyrwich K, Tierney W, Wolinsky F. Further evidence supporting a SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. J Clin Epidemiol 1999;52:861– 873. Wyrwich K, Tierney W, Wollinsky F. Using the standard error of measurement to identify important intra-individual change on the Asthma Quality of Life Questionnaire. Qual Life Res 2002;11:1–7. Cella DF, Hahn EA, Dineen K. Meaningful change in cancerspecific quality of life scores: Differences between improvement and worsening. Qual Life Res 2002;11:207–221.
● B. MOVSAS et al.
835
17. Jordhoy MS, Fayers P, Loge JH, et al. Quality of life in advanced cancer patients: The impact of sociodemographic and medical characteristics. Br J Cancer 2001;85:1478 –1485. 18. Wan G, Counte M, Cella DF. The influence of personal expectations on cancer patients’ reports of health-related quality of life. Psycho-Oncology 1997;6:1–11. 19. Hjermstad M, Fayers P, Bjordal K, et al. Health-related quality of life in the general Norwegian population assessed by the EORTC core Quality of Life Questionnaire—the QLQ-C30 (⫹3). J Clin Oncol 1998;16:1188 –1196. 20. Michelson H, Bolund C, Nilsson B, et al. Health-related quality of life measured by the EORTC QLQ-C30 —reference values from a large sample of Swedish population. Acta Oncol 2000;39:477– 484. 21. Klee M, Groenvold M, Machin D. Quality of life of Danish women; population-based norms for the EORTC QLQ-C30. Qual Life Res 1997;6:27–34. 22. Calman KC. Quality of life in cancer patient—An hypothesis. J Med Ethics 1984;10:124 –127. 23. Mor V, Allen S, Malin M. The psychosocial impact of cancer on older versus younger patients and their families. Cancer 1994;74:2118 –2127. 24. Wan G, Counte M, Cella DF, et al. The impact of sociocultural and clinical factors on health-related quality of life reports among Hispanic and African-American cancer patients. J Outcome Measurements 1999;3:200 –215. 25. Axtell LM, Myers MH. Contrasts in survival of black and white cancer patients, 1960 –73. J Natl Cancer Inst 1978;60: 1209 –1215. 26. McWhorter WP, Schatzkin AG, et al. Contribution of socioeconomic status to black/white differences in cancer incidence. Cancer 1989;63:982–987. 27. Cella DF, Orav EJ, Kornblith AB, et al. Socioeconomic status and cancer survival. J Clin Oncol 1991;9:1500 –1509. 28. Sprangers M, Schwartz C. The challenge of response shift for quality-of-life-based clinical oncology research. Ann Oncol 1999;10:747–749. 29. O’Boyle C, McGee H, Browne J. Measuring response shift using the schedule for evaluation of individual quality of life. In: Schwartz C, Sprangers M, editors. Adaptation to changing health: Response shift in quality-of-life research. Washington, DC: American Psychological Association; 2000. pp. 123–136. 30. Leffall LS. 7th biennial symposium on minorities, the medically underserved and cancer. Foreword. Cancer 2001;91(Suppl. 1): 189 –192.