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Effectiveness of Adjuvant Fluorouracil in Clinical Practice: A Population-Based Cohort Study of Elderly Patients With Stage III Colon Cancer

From the Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA; and Department of Medicine, Sections of General Internal Medicine and Hematology-Oncology, Pritzker School of Medicine, and the Cancer Research Center, University of Chicago, Chicago, IL.

Address reprint requests to Elizabeth B. Lamont, MD, MS, University of Chicago Medical Center, 5841 S Maryland Ave, MC 2007, Chicago, IL 60637; email: elamont{at}medicine.bsd.uchicago.edu

	ABSTRACT

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PURPOSE: Although randomized controlled trials (RCTs) consistently show that adjuvant fluorouracil (5-FU) extends the survival of patients with stage III colon cancer, it is not yet known whether this benefit exists in populations underrepresented on clinical trials, particularly the elderly with medical comorbidity treated in the community. In this study, we ask the following: (1) What is the hazard of death associated with adjuvant 5-FU in the general population of elderly stage III colon cancer patients? (2) Does the hazard vary with patient age?

PATIENTS AND METHODS: In this prospective, nonrandomized, population-based cohort study of 3,357 elderly Medicare beneficiaries who had undergone resection of stage III colon cancer according to the Surveillance, Epidemiology, and End-Results registries, we use propensity score matching to compare the all-cause mortality of patients who received 5-FU to matched untreated patients.

RESULTS: 5-FU reduces the hazard of death by 27% (hazard ratio, 0.73; 95% confidence interval [CI], 0.65 to 0.82) across the 6 years of our data in a Cox model. At 5 years, 52.7% (95% CI, 49.6% to 55.6%) of the treated and 40.7% (95% CI, 38.1% to 43.4%) of the matched untreated are still alive. We find that these effects do not diminish with advancing patient age.

CONCLUSION: The survival benefit of adjuvant 5-FU that has been demonstrated in participants of RCTs is also evident in a population sample of elderly Medicare beneficiaries with stage III colon cancer treated in the community. Furthermore, this survival benefit does not appear to diminish with patient age. These findings support the continued use of adjuvant 5-FU in the general population of elderly patients with stage III colon cancer and suggest that oncologists in the community are practicing at a high level of effectiveness.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION REFERENCES

 
RANDOMIZED CONTROLLED trials (RCTs) conducted over the past decade show that treatment with fluorouracil (5-FU) after curative-intent resection of stage III colon cancer prolongs patient survival.^1-7 Sargent et al⁸ recently pooled survival data from several RCTs and quantified the hazard reduction at 24% (hazard ratio [HR], 0.76; 95% confidence interval [CI], 0.68 to 0.85). That group also noted that there was no evidence of decreased 5-FU efficacy among the older patients studied. However, their study sample did not reflect the age distribution of stage III colon cancer patients in the United States, as only 15% were over age 70, whereas the median age for colon cancer diagnosis in the United States is 73.⁹ So although 5-FU appears quite efficacious among patients treated on clinical trials, two important questions remain regarding the clinical care of the average stage III colon cancer patient: (1) Is adjuvant 5-FU as effective in clinical practice among the elderly as it is in clinical trials? (2) Does the effectiveness of adjuvant 5-FU diminish with advancing patient age?

The general concern about the external validity of chemotherapy RCT findings results not only from the well-known tendency of trial participants to differ from the population from which they are drawn (eg, to have different decision-making styles, socioeconomic status, treatment expectations)^10-13 but also from the underrepresentation of elderly patients and those with medical comorbidity and/or poor performance status.^14,15 In such situations where RCTs are not possible (eg, ethical or logistical reasons), well-designed observational trials (ie, cohort studies, case-control studies) may function as effective surrogates for RCTs. Some authors, including Horwitz and Feinstein,^16-20 have argued convincingly that when scientific principles inherent in RCTs (eg, strict eligibility criteria) are incorporated into the design of observational studies, the bias related to the nonrandom allocation of treatment is lessened, improving their scientific validity and therefore their ability to approximate results of RCTs.

In the current project, we apply rigorous analytic techniques to an observational data source, the Surveillance, Epidemiology, and End-Results (SEER)-Medicare data, to ask two critical questions relating to the clinical care of elderly patients with colon cancer: (1) What is the hazard of death associated with adjuvant 5-FU in the general population of elderly stage III colon cancer patients? (2) Does that hazard vary with patient age? In this project, we parallel an intention-to-treat analysis of an RCT: we compare stage III patients who received any dose of 5-FU after resection to observationally similar patients who did not, and examine differences in all-cause mortality.

	PATIENTS AND METHODS

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Data and Cohort Construction
We used data from the National Cancer Institute’s SEER-Medicare program to study the all-cause mortality of elderly Medicare beneficiaries with histories of stage III colon cancer. The SEER-Medicare database is a National Cancer Institute–sponsored individual-level linkage of the clinical data collected by the SEER registries with health services billing claims collected by Medicare for administrative purposes. These data are widely used by researchers studying outcomes, clinical epidemiology, and health services factors among elderly cancer patients.²¹

The SEER program collects information regarding the diagnosis and treatment of patients with cancer from 11 geographically diverse tumor registries in order to monitor trends in incidence and survival. It is estimated that approximately 14% of the American population with cancer⁹ is represented in these data and prior research has shown that, in the aggregate, patients in these registries are demographically representative of the general population.²² The SEER program collects detailed information about initial diagnosis and treatment, including date of diagnosis, site, histology, and stage of tumor at diagnosis, in addition to demographic information. Mortality data are provided through linkage to death certificates. The SEER program conducts annual audits of their data to ensure data quality and completeness, holding the standard of ascertainment at 98%.²³

Medicare is a federally sponsored health insurance program administered by the Centers for Medicare and Medicaid Services whose beneficiaries include more than 96% of all United States citizens aged 65 and older.²⁴ The Centers for Medicare and Medicaid Services maintains billing records of outpatient, inpatient, and other claims for all beneficiaries not enrolled in risk contract health maintenance organizations. Inpatient data are from the Medicare Provider Analysis and Review (MedPAR) file. Outpatient data are from the Outpatient Standard Analytic File (SAF) and the National Claims History file.

We empaneled an incidence cohort of all patients in the SEER file diagnosed with pathologically confirmed stage III adenocarcinoma of the colon; diagnosed at or after age 67; during the period January 1, 1993, to December 30, 1996; entitled to Medicare parts A and B during the observation period so that evaluation of both outpatient care (part B) and inpatient care (part A) was possible; and not enrolled in a health maintenance organization (for whom individual claims may not be submitted to Medicare because of capitated payment) during the observation period. Patients were excluded if they had Medicare International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for colon cancer that preceded the SEER date of colon cancer diagnosis by more than 2 months; if they had not undergone cancer surgery; if their colon cancer was diagnosed at autopsy; or if they died within 90 days of diagnosis. Surviving patients were fixed right censored at December 31, 1998. The analytic sample consisted of 3,357 patients.

Variables
Information regarding demographic factors of age, race, sex, marital status, socioeconomic status (eg, census tract median income), and market structural factors (ie, urban v rural residence, and cancer registry) was obtained directly from the data set. We also created variables to indicate 5-FU exposure and to adjust for prior medical comorbidity. 5-FU use was ascertained by reviewing each patient’s National Claims History, Outpatient SAF, and MedPAR file corresponding to the observation period for the presence or absence of codes consistent with administration of 5-FU. The details of the method and the resultant construct validity of the measure have been published previously.²⁵ Briefly, the procedure codes are examined for the presence of Healthcare Financing Administration Common Procedure Code (HCPC) J9190, indicating intravenously administered 5-FU. Patients with at least one HCPC J9190 during the observation period were considered to have been treated with 5-FU. Patients with IDC-9-CM codes V58.1, V662, V672, and 9925, indicating chemotherapy administration, in any diagnostic fields in the MedPAR files were also considered to have been treated with 5-FU, as prior investigations have shown that, for patients with histories of colon cancer, these codes invariably represent 5-FU administration (Warren, unpublished data).

To minimize confounding by unmeasured health, we used health-related data from a number of sources and controlled for health in a maximally flexible way to estimate baseline medical comorbidity for each patient. The Charlson comorbidity score is a convenient method of operationalizing co-occurring medical illness in cancer patients and is often used for risk adjustment. The score, ranging from 0 to 29, consists of a weighted sum of 17 major illnesses (eg, myocardial infarction, cerebrovascular accident, diabetes, liver disease, dementia, renal disease). All claims from the prior 2 years of inpatient and outpatient use were reviewed, and separate scores for each data source were developed and parameterized as a family of five indicator variables.^26-29 Furthermore, the histologic data pertaining to tumor grade, pathologic data pertaining to number of lymph nodes containing tumor, and clinical data regarding treatment with postoperative radiotherapy (all of which are important prognostic factors) from the SEER registries were incorporated into all models.

Propensity Score Matching
In order to minimize the bias related to the nonrandom allocation of treatment, we developed a matching scheme that included variables that have been shown previously to be associated with chemotherapy use.^25,30-42 We included variables within the following domains: oncologic (tumor grade, number of lymph nodes containing tumor, need for postoperative radiotherapy), medical comorbidity (Charlson scores), demographics (age, race, sex, marital status, socioeconomic status), market structural factors (urban v rural, with separate indicator variables for each registry), and temporal factors (year of diagnosis). These variables were included in a logistic regression model that determined the probability of adjuvant 5-FU use; this probability is referred to as the propensity score.^43-47 By matching treated and untreated patients on their propensity score, we then reduce selection bias. The approach has been used successfully in the medical context in the past^48-53 and recent work has shown that propensity scores can be used to observationally replicate RCT results, even in contexts where conventional regression performs poorly.^54,55

To implement the propensity score matching, we first used logistic regression to model likelihood of receipt of 5-FU during the observation period, using the previously mentioned covariates as predictors. The resultant model had a c-statistic of greater than 0.83, indicating good discrimination between those who did and did not receive 5-FU. We then used the model to predict each patient’s probability of receiving 5-FU. The resultant probability is the propensity score. On the basis of their propensity score, patients who received 5-FU were then matched, with replacement, to patients who did not receive 5-FU. Individuals were matched within tight bounds on their propensity scores; that is, their predicted probability of receiving adjuvant 5-FU could vary by no more than 0.005 (0.5%) on a scale of 0 to 1.

Statistical Analyses
We evaluated the association of claims for 5-FU on the cohort’s survival in two ways. First, we examined unadjusted Kaplan-Meier curves to evaluate for crude differences between treated cases and matched untreated patients. Second, we used Cox regression to estimate an average difference in the hazard of death across a 6-year follow-up period. We stratified these results on matched pairs in order to appropriately adjust standard errors for the matching, and controlled for the replication of controls as clusters in the manner of Lin and Wei.⁵⁶ All regression results control for propensity score using categorical variables for each decile of score among treated patients.^43,54 The Kaplan-Meier curves without matching show an implausible substantial and instantaneous benefit of 5-FU. Conventional Cox modeling with all 67 variables included in the propensity score generation regression are, in this case, able to reproduce the 5-FU benefit demonstrated by propensity score matching, although without having performed the matching, we would have no assurance that it was able to perform so well.

All analyses were performed in STATA 7.0 (Stata Corp, College Station, TX). The research qualified for institutional review board exemption.

Sensitivity Analyses
Using the propensity score technique, we adjusted adequately for all factors included in the propensity regression. This approach relies on the assumption of selection on observables; that is, we assume that the decision to administer chemotherapy is made on the basis of only those variables that we can observe through our data: tumor grade, number of lymph nodes containing tumor, postoperative radiation therapy, patient age, comorbidity, race, sex, marital status, socioeconomic status, location of residence, and year of diagnosis. Our approach meets the criteria that Heckman et al⁵⁷ articulated for the effective use of a propensity score: the data for cases and controls are drawn from the same source, outcome data are drawn from the same sources, a rich set of covariates is available to model selection, and cases and controls come from the same local areas so that any likely unobserved factors shaping behavior are also matched.^57,58 The process of matching serves to ensure that the treated and untreated groups are equivalent in any known confounders that influence both 5-FU use and outcome; each patient has a matched untreated control that was equally likely (but for random factors) to have been treated.

Because there may be important but unmeasured covariates that were not represented in our data set and not identified in prior epidemiologic research, we sought to evaluate the sensitivity of our analysis to possible missing covariates in the following manner. After identifying variables that were highly associated with the receipt of chemotherapy (as quantified by the partial R² in the propensity score regression) and removing them from the propensity regression, we then re-estimated the model to determine the stability of our estimates of 5-FU’s effectiveness in the setting of an intentionally inadequate model. We quantified the degree to which results varied between the intentionally misspecified model and the original model. Results that are relatively insensitive to the exclusion of factors known to be of major importance suggest that the degree of bias introduced by some other unidentified factor would be small.

In a second sensitivity test, we replicated all of our analyses examining the impact of age on the effectiveness of 5-FU. We found that our conclusions did not vary with different parameterizations of age, including quadratic, cubic, or multiple indicator variables approaches. We therefore presented the results with a linear age effect for simplicity.

	RESULTS

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Description of Cohort and Matching
Table 1 lists differences between 1,834 patients treated with 5-FU and those not treated with 5-FU using first unmatched analyses and then matched analyses. Of note, 32 treated individuals (1.7%) could not be matched to an untreated patient within our tight propensity score bounds and are excluded from the analyses. In the matched analyses, the treatment groups are comparable, with the only statistically significant difference being that patients treated with 5-FU are slightly more likely to live in urban regions than those untreated. The groups are also balanced across registries (data not shown).

View larger version (26K): [in this window] [in a new window]   Fig 1. Age distribution of study cohort. Comparison information for SEER distribution is all surgically resected, pathologically confirmed, stage III colon cancer patients who survived at least 90 days from diagnosis present in SEER (same restrictions as required for study population).

Effectiveness of 5-FU in the Elderly
As shown in Fig 2, the Kaplan-Meier curves for the effects of 5-FU after matching on the propensity score show a clear survival benefit associated with 5-FU. Fully 89.6% (95% CI, 88.1% to 90.1%) of patients receiving 5-FU are alive 1 year after diagnosis, in contrast to 85.6% (95% CI, 83.9% to 87.2%) of matched patients who did not receive 5-FU. At 5 years, 52.7% (95% CI, 49.6% to 55.6%) of the treated and 40.7% (95% CI, 38.1% to 43.4%) of the matched untreated are still alive. 5-FU reduces the hazard of death by 27% (HR, 0.73; 95% CI, 0.65 to 0.82) across the 6 years of our data in a Cox model.

View larger version (13K): [in this window] [in a new window]   Fig 2. 5-FU in the elderly, matched sample results. Kaplan-Meier estimates for survival among patients who received 5-FU and propensity score–matched untreated patients. One- and 5-year cut points are shown for the reader’s convenience.

Sensitivity Analyses
To evaluate the sensitivity of our analyses to possible excluded variables, we intentionally misspecified the model. The three most important predictors of 5-FU use were comorbidity, age, and cancer registry. We repeated our analyses, sequentially excluding each of these predictors from the propensity score generation algorithm (logistic regression). The effect sizes are listed in Table 2; in no case did intentional omission of a known important confounder lead to a 20% change in the estimated benefit of 5-FU treatment. These results suggest the magnitude of the effect that an important unmeasured covariate (ie, highly correlated both with receipt of 5-FU and with mortality) would need to have to introduce substantial bias into our estimation.

	DISCUSSION

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There are limitations to the work. Our sensitivity analyses suggest that our results would not be immune to bias if there exists an unmeasured covariate that both (1) has as powerful an effect as comorbidity on both the decision to treat patients and mortality, and (2) was poorly correlated with our existing controls. Furthermore, our work has the well-described limitations of administrative claims data.^59-65 Among these may be incomplete ascertainment of 5-FU treatment; such omissions would likely bias our results towards the null (ie, it would lead us to underestimate the effectiveness of 5-FU). Finally, although we have controlled extensively for comorbidity, the effectiveness of 5-FU might vary depending on what other diseases a patient has; future work should establish whether there are particular categories of comorbidity for which 5-FU may be more or less effective.

In summary, these results suggest that adjuvant 5-FU is similarly effective in unselected elderly patients with resected stage III colon cancer as it is efficacious in younger patients enrolled on clinical trials. Moreover, these results suggest that practicing oncologists treating patients in the community with adjuvant 5-FU provide the same survival advantage as those clinical researchers treating patients on experimental protocols, providing a reassuring counterexample to studies suggesting that community physicians may be suboptimal in their practice.⁶⁶

	ACKNOWLEDGMENTS

 
Supported by National Institutes of Health grant nos. K12 AG-0048-09 (to E.B.L.), K07 CA93892-01 (to E.B.L.), and T32 GM07281 (to T.J.I.) and John Hartford Foundation grant no. 990590 (to E.B.L.).

We thank the Applied Research Program, National Cancer Institute; the Office of Information Services, and the Office of Strategic Planning, Centers for Medicare and Medicaid Services;Information Management Services, Inc; and the SEER program tumor registries in the creation of the SEER-Medicare database. We also thank Joan Warren, PhD,at the National Cancer Institute for assistance with our analyses.

	NOTES

 
This study used the linked Surveillance, Epidemiology, and End-Results–Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors.

	REFERENCES

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1. Moertel CG, Fleming TR, Macdonald JS, et al: Levamisole and fluorouracil for adjuvant treatment of resected colon carcinoma. N Engl J Med 322: 353-358, 1990

2. NIH Consensus Conference: Adjuvant therapy for patients with colon and rectal cancer. JAMA 264: 1444-1450, 1990[Abstract/Free Full Text]

3. Moertel CG, Fleming TR, Macdonald JS, et al: Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: A final report. Ann Intern Med 122: 321-326, 1995[Abstract/Free Full Text]

4. Midgley RSJ, Kerr DJ: Colorectal cancer. Lancet 353: 391-399, 1999[CrossRef][Medline]

5. Laurie JA, Moertel CG, Fleming TR, et al: Surgical adjuvant therapy for large-bowel carcinoma: An evaluation of levamisole and the combination of fluorouracil. J Clin Oncol 7: 1447-1456, 1989[Abstract]

6. O’Connell MJ, Mailliard JA, Kahn MJ, et al: Controlled trial of fluorouracil and low-dose leucovorin given for 6 months as postoperative adjuvant therapy for colon cancer. J Clin Oncol 15: 246-250, 1997[Abstract/Free Full Text]

7. Wolmark N, Rockette H, Fisher B, et al: The benefit of leucovorin-modulated fluorouracil as postoperative adjuvant therapy for primary colon cancer: Results from National Surgical Adjuvant Breast and Bowel Project protocol C-03. J Clin Oncol 11: 1879-1887, 1993[Abstract/Free Full Text]

8. Sargent DJ, Goldberg RM, Jacobson SD, et al: A pooled analysis of adjuvant chemotherapy for resected colon cancer in elderly patients. N Engl J Med 345: 1091-1097, 2001[Abstract/Free Full Text]

9. Ries LAG, Kosary CL, Hankey BF, et al: SEER Cancer Statistics Review, 1973-1994. Bethesda MD, National Cancer Institute, NIH publication 97-2789, 1997

10. Llewellyn-Thomas HA, McGreal MJ, Thiel EC, et al: Patients’ willingness to enter clinical trials: Measuring the association with perceived benefit and preference for decision-participation. Soc Sci Med 32: 35-42, 1991[CrossRef][Medline]

11. Verheggen FW, Nieman F, Jonkers R: Determinants of patient participation in clinical studies requiring informed consent: Why patients enter a clinical trial. Patient Educ Couns 35: 111-125, 1998[CrossRef][Medline]

12. Penman DT, Holland JC, Bahna GF, et al: Informed consent for investigational chemotherapy: Patients’ and physicians’ perceptions. J Clin Oncol 2: 849-855, 1984[Abstract]

13. McCusker J, Wax A, Bennett JM: Cancer patient accessions into clinical trials. Am J Clin Oncol 5: 227-236, 1982[Medline]

14. Goodwin JS, Hunt WC, Humble CG, et al: Cancer treatment protocols: Who gets chosen? Arch Intern Med 148: 2258-2260, 1988[Abstract/Free Full Text]

15. 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]

16. Benson K, Hartz AJ: A comparison of observational studies and randomized, controlled trials. N Engl J Med 342: 1878-1886, 2000[Abstract/Free Full Text]

17. Concato J, Shah N, Horwitz RI: Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342: 1887-1892, 2000[Abstract/Free Full Text]

18. Feinstein AR: An additional basic science for clinical medicine: II. The limitations of randomized trials. Ann Intern Med 99: 544-550, 1983[Abstract/Free Full Text]

19. Horwitz RI: The experimental paradigm and observational studies of cause-effect relationships in clinical medicine. J Chronic Dis 40: 91-99, 1987[CrossRef][Medline]

20. Horwitz RI, Feinstein AR: The application of therapeutic-trial principles to improve the design of epidemiologic research: A case-control study suggesting that anticoagulants reduce mortality in patients with myocardial infarction. J Chronic Dis 34: 575-583, 1981[CrossRef][Medline]

21. Potosky AL, Riley GF, Lubitz JD, et al: Potential for cancer related health services research using a linked Medicare-tumor registry database. Med Care 31: 732-748, 1993[Medline]

22. Nattinger AB, McAuliffe TL, Schapira MM: Generalizability of the Surveillance, Epidemiology, and End Results registry population: Factors relevant to epidemiologic and health care research. J Clin Epidemiol 50: 939-945, 1997[CrossRef][Medline]

23. Zippin C, Lum D, Hankey BF: Completeness of hospital cancer case reporting from the SEER program of the National Cancer Institute. Cancer 76: 2343-2350, 1995[CrossRef][Medline]

24. Hatten J: Medicare’s common denominator: The covered population. Health Care Financ Rev 2: 53-64, 1980[Medline]

25. Lamont EB, Lauderdale DS, Schilsky RL, et al: Construct validity of Medicare chemotherapy claim: The case of 5FU. Med Care 40: 201-211, 2002[CrossRef][Medline]

26. 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]

27. Deyo RA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: A response. J Clin Epidemiol 46: 1081-1082, 1993[CrossRef]

28. Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 45: 613-619, 1992[CrossRef][Medline]

29. Zhang JX, Iwashyna TJ, Christakis NA: The impact of alternative lookback periods and sources of information on Charlson comorbidity adjustment in Medicare claims. Med Care 37: 1128-1139, 1999[CrossRef][Medline]

30. Roetzheim RG, Pal N, Gonzalez EC, et al: Effects of health insurance and race on colorectal cancer treatments and outcomes. Am J Public Health 90: 1746-1754, 2000[Abstract/Free Full Text]

31. Dominitz JA, Samsa GP, Landsman P, et al: Race, treatment, and survival among colorectal carcinoma patients in an equal-access medical system. Cancer 82: 2312-2320, 1988

32. Mor V, Guadagnoli E, Silliman RA, et al: Influence of old age, performance status, medical and psychological status on management of cancer patients, in Yancik R, Yates J (eds): Cancer in the Elderly ( ed 1 ). New York, Springer, 1989

33. Ball JK, Alixhauser A: Treatment differences between blacks and whites with colorectal cancer. Med Care 34: 970-984, 1996[CrossRef][Medline]

34. Tropman SE, Hatzell T, Paskett E, et al: Colon cancer treatment in rural North and South Carolina. Cancer Detect Prev 23: 428-434, 1999[CrossRef][Medline]

35. Goodwin JS, Hunt WC, Key CR, et al: The effect of marital status on stage, treatment, and survival of cancer patients. JAMA 258: 3125-3130, 1987[Abstract/Free Full Text]

36. Hodgson DC, Fuchs CS, Ayanian JZ: Impact of patient and provider characteristics on the treatment and outcomes of colorectal cancer. J Natl Cancer Inst 93: 501-515, 2001[Abstract/Free Full Text]

37. Hatzell TA, Ricketts TC, Tropman SE, et al: Rural physicians’ understanding of the state-of-the-art in breast, colon and rectum cancer treatment. Cancer Causes Control 10: 261-267, 1999[CrossRef][Medline]

38. Boyd C, Zhang-Salomons JY, Groome PA, et al: Associations between community income and cancer survival in Ontario, Canada, and the United States. J Clin Oncol 17: 2244-2255, 1999[Abstract/Free Full Text]

39. Monnet E, Boutron MC, Faivre J, et al: Influence of socioeconomic status on prognosis of colorectal cancer: A population-based study in Cote D’Or, France. Cancer 72: 1165-1170, 1993[CrossRef][Medline]

40. Mayberry RM, Coates RJ, Hill HA, et al: Determinants of black/white differences in colon cancer survival. J Natl Cancer Inst 87: 1686-1693, 1995[Abstract/Free Full Text]

41. Lee AJ, Baker CS, Gehlbach S, et al: Do black elderly Medicare patients receive fewer services? An analysis of procedure use for selected patient conditions. Med Care Res Rev 55: 314-333, 1998[Abstract/Free Full Text]

42. 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]

43. Rosenbaum PR, Rubin DB: Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 79: 516-524, 1984[CrossRef]

44. Rosenbaum PR, Rubin DB: The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41-55, 1984[CrossRef]

45. Rosenbaum PR, Rubin DB: Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 39: 33-38, 1985[CrossRef]

46. Rubin DB, Thomas N: Matching using estimated propensity scores: Relating theory to practice. Biometrics 52: 249-264, 1996[CrossRef][Medline]

47. Smith HL: Matching with multiple controls to estimate treatment effects in observational studies, in Raferty AE (ed): Sociological Methodology 1997. Oxford, Basil Blackwell, 1997, pp 325-353

48. Christakis NA, Iwashyna TJ: The health impact on families of healthcare: A matched cohort study of hospice use by decedents and mortality outcomes in surviving, widowed spouses. Soc Sci Med (in press)

49. 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]

50. Connors AF Jr, Speroff T, Dawson NV, et al: The effectiveness of right heart catheterization in the initial care of critically ill patients: SUPPORT Investigators. JAMA 276: 889-897, 1996[Abstract/Free Full Text]

51. Polanczyk CA, Rohde LE, Goldman L, et al: Right heart catheterization and cardiac complications in patients undergoing noncardiac surgery: An observational study. JAMA 286: 309-314, 2001[Abstract/Free Full Text]

52. Gum PA, Thamilarasan M, Watanabe J, et al: Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: A propensity analysis. JAMA 286: 1187-1194, 2001[Abstract/Free Full Text]

53. Stenestrand U, Wallentin L: Early statin treatment following acute myocardial infarction and 1-year survival. JAMA 285: 430-436, 2001[Abstract/Free Full Text]

54. Dehejia RH, Wahba S: Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. J Am Stat Assoc 94: 1053-1062, 1999[CrossRef]

55. Smith JA, Todd PE: Reconciling conflicting evidence on the performance of propensity-score matching methods. Am Econ Rev 91: 112-118, 2001

56. Lin DY, Wei LJ: The robust inference for the Cox proportional hazards model. J Am Stat Assoc 84: 1074-1078, 1989[CrossRef]

57. Heckman JJ, Ichimura H, Todd PE: Matching as an econometric evaluation estimator. Rev Econ Stud 65: 261-294, 1998[CrossRef]

58. Ichimura H, Taber C: Propensity-score matching with instrumental variables. Am Econ Rev 91: 119-124, 2001

59. Fisher ES, Barton JA, Malenka DJ, et al: Overcoming potential pitfalls in the Medicare data for epidemiologic research. Am J Public Health 80: 533-546, 1990

60. Fisher ES, Whaley FS, Krushat M, et al: The accuracy of Medicare’s hospital claims data: Progress has been made, but problems remain. Am J Public Health 82: 243-248, 1992[Abstract/Free Full Text]

61. Iezzoni LI: Risk Adjustment for Measuring Health Outcomes ( ed 2 ). Chicago IL, Health Administration Press, 1997

62. Lauderdale DS, Furner SE, Miles TP, et al: Epidemiologic uses of Medicare data. Epidemiol Rev 15: 319-327, 1993[Free Full Text]

63. Romano PS, Mark DH: Bias in the coding of hospital discharge data and its implications for quality assessment. Med Care 32: 81-90, 1994[Medline]

64. Roos LL, Sharp SM, Cohen MM: Comparing clinical information with claims data: Some similarities and differences. J Clin Epidemiol 44: 881-888, 1991[CrossRef][Medline]

65. Roos LL, Stranc L, James RC, et al: Complications, comorbidities, and mortality: Improving classification and prediction. Health Serv Res 32: 229-238, 1997[Medline]

66. Kohn LT, Corrigan JM, Donaldson MS: To Err Is Human: Building a Safer Health System. Washington DC, National Academy Press, 2000

Submitted March 15, 2002; accepted June 12, 2002.

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