This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Elkin, E. B. Articles by Panageas, K. S. Search for Related Content PubMed PubMed Citation Articles by Elkin, E. B. Articles by Panageas, K. S.

Adjuvant Chemotherapy and Survival in Older Women With Hormone Receptor–Negative Breast Cancer: Assessing Outcome in a Population-Based, Observational Cohort

Elena B. Elkin, Arti Hurria, Nandita Mitra, Deborah Schrag, Katherine S. Panageas

From the Health Outcomes Research Group and Biostatistics Service, Department of Epidemiology and Biostatistics; and the Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY

Address reprint requests to Elena B. Elkin, PhD, Health Outcomes Research Group, Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 44, New York, NY 10021; e-mail: elkine{at}mskcc.org

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
PURPOSE: For older breast cancer patients, there is limited evidence of the efficacy of adjuvant chemotherapy from randomized clinical trials. Our goal wastoassess the relationship between adjuvant chemotherapy use and survival in a large, population-based cohort of older women with hormone receptor (HR) –negative breast cancer.

METHODS: We identified women age 66 and older diagnosed with HR-negative, nonmetastatic breast cancer from 1992 to 1999 in the Surveillance, Epidemiology and End Results (SEER) cancer registries. Chemotherapy use was identified in Medicare claims linked to SEER records. Clinical and sociodemographic predictors of chemotherapy use were identified using logistic regression. The effect of chemotherapy on survival was evaluated using propensity score methods and multivariable proportional hazards regression.

RESULTS: A total of 1,711 (34%) of 5,081 women with HR-negative breast cancer received chemotherapy within 6 months of cancer diagnosis. Chemotherapy use decreased with increasing age and comorbidity, and increased with year of diagnosis, tumor size, number of positive lymph nodes, and higher tumor grade. Adjuvant chemotherapy was associated with a mortality reduction of approximately 15% whether analyzed using propensity scores or standard multivariable methods. The greatest overall survival benefit was observed in patients with node-positive disease and in the node-negative patients most likely to receive chemotherapy.

CONCLUSION: This analysis suggests a survival benefit from adjuvant chemotherapy in older women with HR-negative breast cancer. The benefit of chemotherapy is most pronounced in the patients most likely to be selected for treatment, including those with involved lymph nodes or other high-risk disease characteristics.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
The efficacy of adjuvant chemotherapy has been established by meta-analysis of randomized trials of traditional regimens¹ and individual randomized trials of newer drug combinations and treatment schedules.^2,3 However, women age 70 and older have been under represented in most breast cancer treatment trials.⁴ Consequently, adjuvant chemotherapy guidelines for older breast cancer patients are less definitive than guidelines for their younger counterparts.^5-7 This is reflected in lower rates of adjuvant chemotherapy use among older women.^8-16

Even when data from multiple trials are combined, statistical power remains insufficient to detect significant associations between treatment and survival in older women.¹ A recent meta-analysis of four randomized trials in women with node-positive disease found that older age was not associated with poorer disease-free survival in comparisons of more adjuvant chemotherapy (eg, higher dose, greater dose-intensity, more toxic regimens) versus less chemotherapy,¹⁷ but this study did not address the benefit of any chemotherapy versus no chemotherapy in older women.

Randomized clinical trials are the gold standard for evidence of treatment efficacy. However, the paucity of such data for adjuvant chemotherapy in older breast cancer patients suggests that we examine other possible data sources. Although analysis of observational cohorts can never provide the definitive evidence obtained from a randomized trial, statistical methods have been developed to reduce the bias associated with the lack of treatment randomization in such cohorts.

Our goal was to assess the relationship between adjuvant chemotherapy use and survival in a large population-based cohort of older women with hormone receptor (HR) –negative breast cancer. We chose this subgroup for two reasons. First, these patients have few or no adjuvant systemic treatment options other than chemotherapy because hormonal therapies such as tamoxifen and aromatase inhibitors are ineffective for HR-negative breast cancer.^18-20 Consequently, understanding the potential benefits of adjuvant chemotherapy in these women is essential for the appropriate and effective clinical management of their disease. Second, because these patients do not benefit from hormonal therapy, use of such therapy is unlikely to confound the relationship between chemotherapy and survival.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Data
Our population-based sample was obtained from Surveillance, Epidemiology, and End Results (SEER) cancer registry data linked with Medicare claims. SEER, sponsored by the National Cancer Institute, is a consortium of population-based cancer registries in 11 regions of the United States, covering approximately 14% of the US population. For all incident cancers in their coverage areas, the SEER registries collect data regarding site and extent of disease, surgery and radiation therapy planned or administered within 4 months of diagnosis, and sociodemographic characteristics, with active follow-up for date and cause of death.²¹ For cancer patients age 65 and older residing in SEER areas, Medicare claims have been linked to SEER files. Medicare is the primary health insurer for 97% of Americans 65 years and older, covering inpatient hospital care (Part A), and outpatient care and physician services (Part B). Compared with the US elderly population, the SEER-Medicare population has similar age and sex distributions, but has a smaller proportion of nonwhites, and individuals in SEER-Medicare are more likely to live in urban areas and affluent areas.²¹

Study Population
In the linked SEER-Medicare database, we identified all women age 66 and older diagnosed with a first primary invasive breast cancer between 1992 and 1999. Patients whose diagnoses were made only at the time of death, those who were in a managed care plan during the course of treatment, and those who lacked Part B Medicare coverage were excluded. The sample was limited to women age 66 and older so that we would have a full year of Medicare claims before breast cancer diagnosis for assessing comorbidity. Our analysis included all breast cancers designated as stage I, II, or III by the SEER modification of the American Joint Committee on Cancer’s staging system (third revision).²²

Since 1990, SEER has recorded the estrogen receptor status and progesterone receptor status of incident breast cancers, with values for each of these characteristics classified as positive, negative, borderline, test not done, test ordered but results missing, or unknown. We identified HR-negative cancers as those that were both estrogen receptor–negative and progesterone receptor–negative, and all analyses were restricted to this patient subgroup.

Chemotherapy
Use of chemotherapy was identified on the basis of International Classification of Disease (9th revision, clinical modification) diagnosis and procedure codes and Healthcare Common Procedure Coding System codes indicating chemotherapy in inpatient, outpatient, or physician claims. Specific codes for chemotherapy are listed in Appendix (Table A1). Patients were classified as having any claim versus no claim for chemotherapy in the first 6 months after breast cancer diagnosis. Prior studies suggest that Medicare claims generally have high sensitivity and specificity for chemotherapy use overall and for the identification of individual agents.^23,24

Survival
Survival was measured as the time from breast cancer diagnosis until death as a result of any cause or until date of last follow-up. Date of death was identified from Medicare enrollment records, with follow-up for vital status through December 31, 2002. Patients alive at last follow-up were censored at that time. Although SEER contains information on cause of death abstracted from state death certificates, we did not examine cancer-specific survival as an end point. Cause-of-death attributions in death certificates are prone to bias and error,^25-28 and statistical power would be reduced substantially in an analysis of cancer-specific mortality, compared with all-cause mortality, due to the smaller number of events.

Covariates
We examined a number of variables hypothesized to predict receipt of adjuvant chemotherapy and potentially confound the relationship between chemotherapy use and survival. Tumor size, recorded in SEER as the largest diameter of the primary tumor, was categorized as less than 1.0, 1.0 to 2.0, 2.1 to 5.0, or greater than 5.0 cm. Regional lymph node involvement was classified by the number of positive nodes (zero, one to three, four to nine, 10 or more, positive nodes but number unspecified, or nodal status unknown). Tumors with regional extension to skin, pectoral fascia or muscle, chest wall, ribs, or intercostal or serratus anterior muscles were distinguished from tumors entirely confined to the breast. Tumor grade was categorized as well or moderately differentiated versus poorly or undifferentiated. Tumor histology was classified as ductal, lobular, or other. Two measures were used to define comorbidity in the year before diagnosis: the Romano modification of the Charlson comorbidity index,^29,30 based solely on inpatient claims, and Klabunde’s modification of the Charlson comorbidity index, based on both inpatient and outpatient claims.³¹

Demographic characteristics, from SEER and Medicare records, included age at diagnosis, race (white, black, other), marital status (married v unmarried), and urban-rural location (metropolitan v nonmetropolitan county). Several measures of socioeconomic status (SES) were evaluated. At the individual level, we identified patients who had state buy-in (SBI) to Medicaid for supplemental health insurance coverage at any time during the year of breast cancer diagnosis. At the census tract level, we first evaluated quartiles of median household income; in a separate analysis we replaced this variable with the percent of all residents in the census tract living at or below the federal poverty level, categorized in tertiles. Of these two area-level measures, the stronger predictor was retained in the final model.

Unadjusted associations between adjuvant chemotherapy use and each covariate were examined using ² statistics. We used multivariable logistic regression to estimate the adjusted effects of each variable on the likelihood of receiving adjuvant chemotherapy. Odds ratios, 95% CIs, and two-sided P values were calculated for each predictor.

Propensity Score Analysis
We used propensity score methods to minimize bias related to the nonrandom assignment of treatment.^32,33 In this framework, the relationship between treatment and survival is adjusted for each patient’s likelihood of receiving treatment conditional on observed sociodemographic and clinical covariates. A propensity score reflects an individual patient’s predicted probability of receiving treatment; analysis of the relationship between treatment and outcome is then adjusted for the propensity score. We estimated propensity scores using a logistic regression model that included the covariates described. Cox proportional hazards regression was used to estimate the effect of adjuvant chemotherapy on survival, adjusting for propensity score in two ways. In one model, the propensity score was included as a continuous covariate in a regression that included the entire cohort. In a second approach, Cox models were estimated within strata defined by propensity score quintiles, and a stratified hazard ratio was estimated for the whole cohort. These two approaches are well-accepted methods of applying propensity scores.³⁴ We performed a sensitivity analysis to evaluate the potential impact of an unmeasured binary confounder on the hazard ratio associated with chemotherapy. Propensity score analyses were also compared with a standard multivariable Cox model in which chemotherapy use and all covariates were entered as independent variables.

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Of 45,701 women diagnosed with a first primary stage I to III breast cancer from 1992 to 1999 in the SEER-Medicare data set, 5,081 had HR-negative disease. Overall, 1,711 (34%) patients in the HR-negative cohort had a claim for chemotherapy within 6 months of diagnosis. Chemotherapy use declined dramatically with age (Table 1), from more than 52% of women age 66 to 69 to approximately 5% of women age 85 and older. The proportion of women who received adjuvant chemotherapy increased over time, from 25% of those diagnosed in 1992 to 45% of those diagnosed in 1999. In bivariate analysis, chemotherapy use was associated with larger tumors, greater regional lymph node involvement, disease extension beyond the breast, poorly differentiated tumors, and fewer comorbid conditions. Chemotherapy use was also more likely among women who were married and those who lived in areas with less poverty or higher median household income, and less likely among those who had SBI supplemental insurance coverage. Race, urban versus rural location, and tumor histology were not significantly associated with chemotherapy use. Differences in adjuvant chemotherapy use across SEER registries were significant, varying from 24% in San Francisco-Oakland (n = 413) to almost 42% in Hawaii (n = 180).

Sensitivity analysis suggested that an unmeasured binary confounder could alter the hazard ratio for chemotherapy, depending on its prevalence in the treated and untreated groups and its association with overall survival. For example, if an unmeasured confounder was present in 10% of treated patients and in 40% of untreated patients, and it was associated with a 50% increase in mortality, then the hazard ratio for chemotherapy would be 0.97. As the imbalance between groups and the detrimental effect of the unmeasured confounder on mortality were increased, the more likely it was that such a confounder explained the observed association between chemotherapy and survival.

The nonconstant hazard ratios across propensity score quintiles prompted further evaluation of the patients in each stratum. Although within each quintile characteristics were well balanced between those who received chemotherapy and those who did not–an important aim of propensity score methodology–the distribution of characteristics varied markedly across quintiles. Of note, women in quintile 5 were rather different from the rest of the cohort with respect to age, tumor size, regional lymph node involvement, and tumor grade (Fig 1). Analysis of subgroups by nodal status (Table 4) suggests that the survival benefit seen in the entire cohort is largely driven by the benefit realized among women with node-positive disease; the adjusted hazard ratio for chemotherapy in this subgroup varied from 0.68 to 0.73, depending on the method of adjustment. In a separate subgroup analysis by age at diagnosis, the overall survival benefit of chemotherapy in women younger than age 70 was nearly identical to the benefit observed in the entire cohort. In women age 70 and older, trends were similar to those observed in the entire cohort, but the hazard ratio for chemotherapy was nonsignificant. We also repeated the multivariable regression of women with node-positive disease, including an interaction term for the relationship between receipt of chemotherapy and age older than 70 years; the coefficient on this term was close to zero (0.019) and not statistically significant, suggesting that in women with node-positive disease, the benefit of adjuvant chemotherapy does not vary with age.

View larger version (10K): [in this window] [in a new window]   Fig 1. Selected characteristics of entire cohort compared with propensity score quintile 5. Quintile 5 represents patients with the highest predicted probability of receiving chemotherapy. Comorbidity was measured by the Romano-Charlson index.

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

Although propensity score methods have become increasingly common in analyses of large population-based cohorts, and specifically in analyses of SEER-Medicare cohorts,^35-40 conclusions about treatment effectiveness drawn from observational data are inherently limited. Propensity scores may reduce selection bias, but they can only control for measured characteristics, and therefore cannot completely replicate the random treatment assignment of a clinical trial. Because these analyses are based on administrative data, they omit important characteristics such as performance status, cognitive function, social support, and more detailed comorbidity information, as well as tumor characteristics such as lymphovascular invasion and HER2/neu expression, all of which may be associated with the use of adjuvant chemotherapy, survival, or both. Propensity scores based on administrative rather than clinical data may leave unmeasured clinical confounders unbalanced and may result in biased estimates of treatment effectiveness.⁴¹ In addition, statistical significance does not necessarily suggest unbiased estimates if selection bias is strong and not adequately controlled for, whether by traditional multivariable methods or with propensity scores.

The results of our survival analysis are consistent with those reported in this issue by Giordano et al.⁴² Results are also similar to the recently published meta-analysis of randomized trials, which found a 13% reduction in all-cause mortality among women age 70 and older who received adjuvant chemotherapy.¹ The meta-analysis finding did not achieve statistical significance, and small sample size precluded subgroup analysis by HR status.

Other than age, few sociodemographic characteristics were significantly associated with adjuvant chemotherapy use. Notably, similar to several prior studies of chemotherapy use in breast cancer patients,^13,43 we found no evidence of racial disparity in receipt of treatment. This result differs from that of Giordano et al,⁴² who found that black women were 26% less likely to receive chemotherapy than white women. Two factors may explain this difference in results. First, Giordano et al⁴² included women with both HR-negative and HR-positive disease. Race could be associated with receipt of adjuvant chemotherapy among women with HR-positive disease (who represent a larger proportion of all patients and who generally have a better prognosis) but not among those with HR-negative disease.

A second possible explanation is an association between race and SES measured at the individual level. Giordano et al⁴² included only census tract-level measures of SES, whereas we also evaluated the effect of supplemental public health insurance coverage, a marker of low income. We found that women with SBI for supplemental insurance were more than 40% less likely to receive adjuvant chemotherapy, controlling for other factors. Compared with white women, black women were almost four times more likely to have SBI (9% v 35%; P < .001). Although low income is a requirement for SBI eligibility, states vary with respect to SBI rules and administrative burden, and there is heterogeneity in the degree of coverage among beneficiaries identified as having SBI coverage (eg, some have only coverage of the Medicare Part B premium, whereas others also have coverage of Part B coinsurance payments). Therefore, as an individual-level indicator of low income or poverty, this measure is considered specific but not sensitive.⁴⁴ Nonetheless, our finding that SBI coverage was negatively associated with chemotherapy use, independent of prognostic characteristics, age, and comorbidity, suggests that additional exploration of the relationship between SES and adjuvant treatment is warranted. In addition, our finding that black women were equally likely to receive chemotherapy does not necessarily suggest that they received equally complete treatment.⁴⁵

In contrast to prior studies of breast cancer chemotherapy based on the SEER-Medicare data set,^12,13 our analysis focuses specifically on older women with HR-negative nonmetastatic disease, a group whose patterns of chemotherapy use are particularly important because they have no other adjuvant systemic treatment options.

Average remaining life expectancy is almost 16 years for a 70-year-old woman and almost 7 years for an 85-year-old woman.⁴⁶ Our findings suggest a significant survival benefit among HR-negative older women with node-positive disease and those with node-negative disease who are most likely to receive treatment.

The benefit of adjuvant chemotherapy in older women who are least likely to receive treatment (for example, women age 75 or older, patients with favorable prognostic characteristics, and those with substantial comorbidity) remains uncertain. In the absence of information regarding physician recommendations and individual patient preferences, we could not determine why these women did not receive treatment. Physicians may be reluctant to recommend chemotherapy for their older patients due to uncertainty regarding benefit and concerns about toxicity. Although a randomized clinical trial is the only way to definitively assess treatment efficacy, results of our study, the study by Giordano et al⁴² and the recent meta-analysis are suggestive of a survival benefit in older patients. Given the challenges of initiating clinical trials, especially in the elderly, observational studies of adjuvant therapy can inform clinical decision making for older women with breast cancer.

	Appendix

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 

	Authors’ Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

	Author Contributions

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 

Conception and design: Elena B. Elkin, Arti Hurria, Katherine S. Panageas Data analysis and interpretation: Elena B. Elkin, Arti Hurria, Nandita Mitra, Deborah Schrag, Katherine S. Panageas Manuscript writing: Elena B. Elkin, Arti Hurria, Nandita Mitra, Deborah Schrag, Katherine S. Panageas Final approval of manuscript: Elena B. Elkin, Arti Hurria, Nandita Mitra, Deborah Schrag, Katherine S. Panageas

 

	ACKNOWLEDGMENTS

 
We thank the Applied Research Program, National Cancer Institute; the Centers for Medicare & Medicaid Services; Information Management Services Inc; and the SEER Program tumor registries for their efforts in the creation of the SEER-Medicare database. We also thank Joan Warren, PhD, of Applied Research Program, National Cancer Institute, for guidance in the use of these data.

	NOTES

 
Supported by an Aging and Cancer Planning Grant from the National Cancer Institute (Grant No. P20-CA-103694).

Presented in part at the 41st Annual Meeting of the American Society of Clinical Oncology, Orlando, FL, May 13-17, 2005.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
1. Early Breast Cancer Trialists’ Collaborative Group: Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 365:1687-1717, 2005[CrossRef][Medline]

2. Henderson IC, Berry DA, Demetri GD, et al: Improved outcomes from adding sequential paclitaxel but not from escalating doxorubicin dose in an adjuvant chemotherapy regimen for patients with node-positive primary breast cancer. J Clin Oncol 21:976-983, 2003[Abstract/Free Full Text]

3. Citron ML, Berry DA, Cirrincione C, et al: Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: First report of Intergroup Trial C9741/Cancer and Leukemia Group B trial 9741. J Clin Oncol 21:1431-1439, 2003[Abstract/Free Full Text]

4. Hutchins LF, Unger JM, Crowley JJ, et al: Underrepresentation of patients 65 years of age or older in cancer-treatment trials. N Engl J Med 341:2061-2067, 1999[Abstract/Free Full Text]

5. NIH consensus conference: Treatment of early-stage breast cancer. JAMA 265:391-395, 1991[CrossRef][Medline]

6. The National Institutes of Health Consensus Development Conference: Adjuvant Therapy for Breast Cancer. November 1-3, 2000. Bethesda, MD, J Natl Cancer Inst Monogr, 2001, pp 5-15

7. Carlson RW, Goldstein LJ, Gradishar WJ, et al: NCCN Breast Cancer Practice Guidelines: The National Comprehensive Cancer Network. Oncology (Williston Park) 10:47-75, 1996[Medline]

8. Newcomb PA, Carbone PP: Cancer treatment and age: Patient perspectives. J Natl Cancer Inst 85:1580-1584, 1993[Abstract/Free Full Text]

9. Merchant TE, McCormick B, Yahalom J, et al: The influence of older age on breast cancer treatment decisions and outcome. Int J Radiat Oncol Biol Phys 34:565-570, 1996[CrossRef][Medline]

10. Diab SG, Elledge RM, Clark GM: Tumor characteristics and clinical outcome of elderly women with breast cancer. J Natl Cancer Inst 92:550-556, 2000[Abstract/Free Full Text]

11. Mandelblatt JS, Hadley J, Kerner JF, et al: Patterns of breast carcinoma treatment in older women: Patient preference and clinical and physician influences. Cancer 89:561-573, 2000[CrossRef][Medline]

12. Du X, Goodwin JS: Patterns of use of chemotherapy for breast cancer in older women: Findings from Medicare claims data. J Clin Oncol 19:1455-1461, 2001[Abstract/Free Full Text]

13. Du X, Goodwin JS: Increase of chemotherapy use in older women with breast carcinoma from 1991 to 1996. Cancer 92:730-737, 2001[CrossRef][Medline]

14. Mariotto A, Feuer EJ, Harlan LC, et al: Trends in the use of adjuvant multi-agent chemotherapy and tamoxifen for breast cancer in the United States: 1975-1999. J Natl Cancer Inst 94:1626-1634, 2002[Abstract/Free Full Text]

15. Du XL, Key CR, Osborne C, et al: Discrepancy between consensus recommendations and actual community use of adjuvant chemotherapy in women with breast cancer. Ann Intern Med 138:90-97, 2003[Abstract/Free Full Text]

16. Giordano SH, Hortobagyi GN, Kau SW, et al: Breast cancer treatment guidelines in older women. J Clin Oncol 23:783-791, 2005[Abstract/Free Full Text]

17. Muss HB, Woolf S, Berry D, et al: Adjuvant chemotherapy in older and younger women with lymph node-positive breast cancer. JAMA 293:1073-1081, 2005[Abstract/Free Full Text]

18. Early Breast Cancer Trialists’ Collaborative Group: Tamoxifen for early breast cancer: An overview of the randomized trials. Lancet 351:1451-1467, 1998[CrossRef][Medline]

19. Howell A, Cuzick J, Baum M, et al: Results of the ATAC (Arimidex, Tamoxifen, Alone or in Combination) trial after completion of 5 years’ adjuvant treatment for breast cancer. Lancet 365:60-62, 2005[CrossRef][Medline]

20. Winer EP, Hudis C, Burstein HJ, et al: American Society of Clinical Oncology technology assessment on the use of aromatase inhibitors as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer: Status report 2004. J Clin Oncol 23:619-629, 2005[Abstract/Free Full Text]

21. Warren JL, Klabunde CN, Schrag D, et al: Overview of the SEER-Medicare data: Content, research applications, and generalizability to the United States elderly population. Med Care 40:IV3-IV18, 2002

22. SEER: SEER Program: Comparative Staging Guide for Cancer, Version 1.1. Bethesda, MD, National Institutes of Health, 1993

23. Lamont EB, Herndon JE II, Weeks JC, et al: Criterion validity of Medicare chemotherapy claims in Cancer and Leukemia Group B breast and lung cancer trial participants. J Natl Cancer Inst 97:1080-1083, 2005[Abstract/Free Full Text]

24. Warren JL, Harlan LC, Fahey A, et al: Utility of the SEER-Medicare data to identify chemotherapy use. Med Care 40:IV55-IV61, 2002

25. Ederer F, Geisser MS, Mongin SJ, et al: Colorectal cancer deaths as determined by expert committee and from the death certificate: A comparison—The Minnesota study. J Clin Epidemiol 52:447-452, 1999[CrossRef][Medline]

26. Hoel DG, Ron E, Carter R, et al: Influence of death certification errors on cancer mortality trends. J Natl Cancer Inst 85:1063-1068, 1993[Abstract/Free Full Text]

27. Lloyd-Jones DM, Martin DO, Larson MG, et al: Accuracy of death certificates for coding coronary heart disease as the cause of death. Ann Intern Med 129:1020-1026, 1998[Abstract/Free Full Text]

28. Lu TH, Shih TP, Lee MC, et al: Diversity in death certification: A case vignette approach. J Clin Epidemiol 54:1086-1093, 2001[CrossRef][Medline]

29. Charlson ME, Pompei P, Ales KL, et al: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 40:373-383, 1987[CrossRef][Medline]

30. Romano PS, Roos LL, Jollis JG: Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: Differing perspectives. J Clin Epidemiol 46:1075-1079, 1081-1090, 1993[CrossRef][Medline]

31. Klabunde CN, Potosky AL, Legler JM, et al: Development of a comorbidity index using physician claims data. J Clin Epidemiol 53:1258-1267, 2000[CrossRef][Medline]

32. Rosenbaum PR: Optimal matching for observational studies. J Am Stat Assoc 84:1024-1032, 1989[CrossRef]

33. Rubin DB: Estimating causal effects from large data sets using propensity scores. Ann Intern Med 127:757-763, 1997[Abstract/Free Full Text]

34. D’Agostino RB Jr: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17:2265-2281, 1998[CrossRef][Medline]

35. Earle CC, Tsai JS, Gelber RD, et al: Effectiveness of chemotherapy for advanced lung cancer in the elderly: Instrumental variable and propensity analysis. J Clin Oncol 19:1064-1070, 2001[Abstract/Free Full Text]

36. Iwashyna TJ, Lamont EB: Effectiveness of adjuvant fluorouracil in clinical practice: A population-based cohort study of elderly patients with stage III colon cancer. J Clin Oncol 20:3992-3998, 2002[Abstract/Free Full Text]

37. Sundararajan V, Mitra N, Jacobson JS, et al: Survival associated with 5-fluorouracil-based adjuvant chemotherapy among elderly patients with node-positive colon cancer. Ann Intern Med 136:349-357, 2002[Abstract/Free Full Text]

38. Krzyzanowska MK, Weeks JC, Earle CC: Treatment of locally advanced pancreatic cancer in the real world: Population-based practices and effectiveness. J Clin Oncol 21:3409-3414, 2003[Abstract/Free Full Text]

39. Ramsey SD, Howlader N, Etzioni RD, et al: Chemotherapy use, outcomes, and costs for older persons with advanced non-small-cell lung cancer: Evidence from Surveillance, Epidemiology and End Results-Medicare. J Clin Oncol 22:4971-4978, 2004[Abstract/Free Full Text]

40. Hershman D, Jacobson JS, McBride R, et al: Effectiveness of platinum-based chemotherapy among elderly patients with advanced ovarian cancer. Gynecol Oncol 94:540-549, 2004[CrossRef][Medline]

41. Austin PC, Mamdani MM, Stukel TA, et al: The use of the propensity score for estimating treatment effects: Administrative versus clinical data. Stat Med 24:1563-1578, 2005[CrossRef][Medline]

42. Giordano SH, Duan Z, Kuo Y, et al: Use and outcomes of adjuvant chemotherapy in older women with breast cancer. J Clin Oncol 24:10.1200/JCO.2005.02.3028

43. Muss HB, Hunter CP, Wesley M, et al: Treatment plans for black and white women with stage II node-positive breast cancer: The National Cancer Institute Black/White Cancer Survival Study experience. Cancer 70:2460-2467, 1992[CrossRef][Medline]

44. Bach PB, Guadagnoli E, Schrag D, et al: Patient demographic and socioeconomic characteristics in the SEER-Medicare database applications and limitations. Med Care 40:IV19-IV25, 2002

45. Hershman D, McBride R, Jacobson JS, et al: Racial disparities in treatment and survival among women with early-stage breast cancer. J Clin Oncol 23:6639-6646, 2005[Abstract/Free Full Text]

46. Arias E: United States Life Tables, 2002. Natl Vital Stat Rep 53:1-39, 2004[Medline]

Submitted July 27, 2005; accepted January 4, 2006.

This article has been cited by other articles:

				J. L. Warren, A. B. Mariotto, A. Meekins, M. Topor, and M. L. Brown Current and Future Utilization of Services From Medical Oncologists J. Clin. Oncol., July 1, 2008; 26(19): 3242 - 3247. [Abstract] [Full Text] [PDF]

				J. L. Warren, K. R. Yabroff, A. Meekins, M. Topor, E. B. Lamont, and M. L. Brown Evaluation of Trends in the Cost of Initial Cancer Treatment J Natl Cancer Inst, June 18, 2008; 100(12): 888 - 897. [Abstract] [Full Text] [PDF]

				S. Dellapasqua, M. Colleoni, M. Castiglione, and A. Goldhirsch New Criteria for Selecting Elderly Patients for Breast Cancer Adjuvant Treatment Studies Oncologist, August 1, 2007; 12(8): 952 - 959. [Abstract] [Full Text] [PDF]

				C. Bouchardy, E. Rapiti, S. Blagojevic, A.-T. Vlastos, and G. Vlastos Older Female Cancer Patients: Importance, Causes, and Consequences of Undertreatment J. Clin. Oncol., May 10, 2007; 25(14): 1858 - 1869. [Abstract] [Full Text] [PDF]

				D. Crivellari, M. Aapro, R. Leonard, G. von Minckwitz, E. Brain, A. Goldhirsch, A. Veronesi, and H. Muss Breast Cancer in the Elderly J. Clin. Oncol., May 10, 2007; 25(14): 1882 - 1890. [Abstract] [Full Text] [PDF]

				K. D. Crew, A. I. Neugut, X. Wang, J. S. Jacobson, V. R. Grann, G. Raptis, and D. L. Hershman Racial Disparities in Treatment and Survival of Male Breast Cancer J. Clin. Oncol., March 20, 2007; 25(9): 1089 - 1098. [Abstract] [Full Text] [PDF]

				R. A. Silliman and P. A. Ganz Adjuvant Chemotherapy Use and Outcomes in Older Women With Breast Cancer: What Have We Learned? J. Clin. Oncol., June 20, 2006; 24(18): 2697 - 2699. [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Elkin, E. B. Articles by Panageas, K. S. Search for Related Content PubMed PubMed Citation Articles by Elkin, E. B. Articles by Panageas, K. S.