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Comparative Effectiveness Research: The Impact of Biologic Agents in Ethnic Minorities With Metastatic Colorectal Cancer
Accepted Manuscript Comparative Effectiveness Research: the Impact of Biologic Agents In Ethnic Minorities with Metastatic Colorectal Cancer Sanjay Goel, Abdissa Negassa, Ashish Khot, Dharmendra Goyal, Shuang Guo, Amara Nandikolla, Kamila Bakirhan, Rahul Polineni, Umang Shah, Imran Chaudhary, Mohammad H. Ghalib, Lakshmi Rajdev, Andreas Kaubisch, Jennifer Chuy, Santiago Aparo PII:
S1533-0028(16)30187-6
DOI:
10.1016/j.clcc.2017.03.004
Reference:
CLCC 361
To appear in:
Clinical Colorectal Cancer
Received Date: 19 September 2016 Revised Date:
18 January 2017
Accepted Date: 1 March 2017
Please cite this article as: Goel S, Negassa A, Khot A, Goyal D, Guo S, Nandikolla A, Bakirhan K, Polineni R, Shah U, Chaudhary I, Ghalib MH, Rajdev L, Kaubisch A, Chuy J, Aparo S, Comparative Effectiveness Research: the Impact of Biologic Agents In Ethnic Minorities with Metastatic Colorectal Cancer, Clinical Colorectal Cancer (2017), doi: 10.1016/j.clcc.2017.03.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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COMPARATIVE EFFECTIVENESS RESEARCH: THE IMPACT OF BIOLOGIC AGENTS IN ETHNIC MINORITIES WITH METASTATIC COLORECTAL CANCER
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Sanjay Goel1,4,5,*,$, Abdissa Negassa3,5,*, Ashish Khot1,4, Dharmendra Goyal1,4, Shuang Guo2,4 , Amara Nandikolla1,4, Kamila Bakirhan1,4, Rahul Polineni1,4, Umang Shah1,5, Imran Chaudhary1,4, Mohammad H. Ghalib1,4, Lakshmi Rajdev1,4,5, Andreas Kaubisch1,4,5, Jennifer Chuy1,4,5, and Santiago Aparo1,4,5 Department of Medical Oncology1, Department of Medicine2, Department of Epidemiology and Population Health3, Montefiore Medical Center4, Bronx NY, Albert Einstein College of Medicine5, Bronx NY
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Corresponding Author $ Sanjay Goel, MD, MS Professor of Clinical Medicine Albert Einstein College of Medicine Department of Medical Oncology Montefiore Medical Center 1695 Eastchester Road Bronx NY 10461 Phone: 718-405-8404 Fax: 718-405-8433 E mail: [email protected]
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Equal contribution*
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Key words: comparative effectiveness research; colorectal cancer; biologic therapy; chemotherapy; bevacizumab; cetuximab; panitumumab; Hispanic; Black; Caucasian; race; clinical outcome
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Running Title: Biologics and race in colorectal cancer SG was funded by a grant "Advanced Clinical Research Award" in colorectal cancer by the Conquer Cancer Foundation of the American Society of Clinical Oncology
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Conflicts of Interest: None for all authors
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ABSTRACT
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Background: Biologic agents have improved the outcome of patients with metastatic colorectal cancer. Clinical trials have included a predominately White population (85%). Hispanics and Blacks were underrepresented; and the real world benefit among them remains unknown. Comparative Effectiveness Research is a tool allowing for this exploration. Patients and Methods: Demographic and clinical characteristics of patients treated for mCRC in years 2000-2011 were extracted from medical records of Montefiore Medical Center. A semi parametric accelerated failure time (AFT) model was used to assess survival differences between patients treated with chemotherapy alone (CT) vs. chemotherapy plus biologics (CBT). Results: Among 290 patients; 45.9%Black, 26.2%-Hispanic, and 27.9%-White; 53.8% received-biologics. The median overall survival (OS) was 15.2 months in the CT group and 25.6 months in CBT group (p=0.004). On univariate analysis; lower number of metastatic sites, CEA <41 ng/mL, and more lines of chemotherapy, were associated with an improved OS. In a propensity score based analysis of the entire cohort, CBT offered a survival benefit compared to CT (increased median survival 1.44 fold, 95%Cl 1.11-1.86, p=0.038). Subgroup analysis suggested a survival benefit for Whites (2.01, 95%CI 1.26-3.23, p=0.031), but not for Hispanics (1.42, 95%CI 0.91-2.20, p=0.370) or Blacks (1.12, 95%CI 0.76-1.66, p=0.596). Conclusions: In this cohort, CBT was associated with longer survival, the effect mainly driven by Whites, while Blacks did not appear to benefit. Clearly, these data are provocative, warranting further confirmation. Effort to increase ethnic minority patients’ enrolment in clinical trials is required to prospectively define the benefit from novel therapies.
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INTRODUCTION AND BACKGROUND
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Colorectal cancer (CRC) was diagnosed in 134,490, and resulted in the death of 49,190 Americans in 2016; the second leading cause of cancer related deaths1. According to Surveillance, Epidemiology, and End Results (SEER) data, Non-Hispanic (NH) Blacks have a higher incidence (39.5 vs. 29.1 per 100.000) and worse outcomes (9.1 vs. 13.7% 5-year overall survival [OS]) than NH-Whites, respectively2. In spite of recent major gains in therapeutic options, metastatic (m)CRC remains an incurable illness, with a dismal 5 year OS rate of 12.5%3. The median OS of patients with mCRC treated with cytotoxic drugs (CT), 5-fluorouracil (5-FU), oxaliplatin and irinotecan is around 20 months4. The addition of biologic agents, namely, bevacizumab [humanized monoclonal antibody to vascular endothelial growth factor (VEGF)]; and cetuximab (chimeric) and panitumumab (fully human) [monoclonal antibodies to epidermal growth factor receptor (EGFR)], leads to a median OS of around 30 months with response rates of 50-60%5-13. The greatest benefit of anti-EGFR therapy is observed exclusively in patients with RAS WT status14. Importantly, their use in patients with a KRAS mutation may be detrimental, with inferior outcomes12,15. The incorporation of molecular markers allows for better selection and personalization of therapy5.
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Ironically, despite the lack of strong evidence of survival benefit supporting the addition of bevacizumab to FOLFOX in the first line setting, this combination continues to be the commonest one, preferred by 64% of US oncologists7. It is possible, that bevacizumab is not as effective when used with regimens using oxaliplatin (e.g., FOLFOX) as compared to irinotecan (e.g., IFL), and may be diluted by the superiority of infusional 5-FU regimens8,10,16. Most importantly, a recent analysis of SEER-Medicare data has reported that the addition of bevacizumab only results in a modest improvement in OS, lower than that reported in clinical trials17; and only in combination with irinotecan and not so with oxaliplatin. This exemplifies the phenomenon that results of clinical trials are not always reproducible in the “real-world” setting.
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Furthermore, most of the clinical trials that have demonstrated this benefit have been performed in the White population; with an under-representation of patients from ethnic minorities4,9,11,18 .The real world benefit is little known with the suggestion that it is underwhelming compared to clinical trial data17. With this background, and in keeping with the fact that we cater to a largely underserved ethnic minority population, we initiated a Comparative Effectiveness Research (CER) study to characterize the real world benefit of the addition of biologics to traditional cytotoxic chemotherapy. PATEINTS AND METHODS Clinical Data
The initial cohort was obtained from the Montefiore Medical Center (MMC) cancer registry database. It included patients diagnosed with CRC between the years 2000-2011 that received medical care at MMC. The database included demographic and survival information such as: date of birth/diagnosis/last contact (date of death or
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last follow-up), ethnicity (categorized as Hispanic, NH-White, and NH-Black), and tumor stage at diagnosis. Tumors were staged using the American Joint Committee on Cancer criteria. Ethnicity data was based on patients’ self-reports. Additional clinical characteristics were extracted through retrospective chart review using MMC’s clinical information system (Centricity Enterprise, version 6.6.3). These included: gender; Eastern Cooperative Oncology Group performance status (ECOG PS), body mass index, socioeconomic status, health insurance, colon vs. rectal primary, degree of differentiation, date of diagnosis of metastatic disease, number and sites of metastases, treatment received [including CT or biologics plus CT (CBT)], and important comorbidities such as hypertension and diabetes, that could influence overall survival. We also performed an additional secondary analysis considering only the patients who had received bevacizumab. All patient information was de-identified and stored as a Microsoft 2010 Excel file that was encrypted and password-protected. The study was approved by the MMC institutional review board. A change in the cytotoxic chemotherapy or the biologic agent was considered as moving from one line to the next. Progression free survival was the time from start of a therapy to the time of documented progression or last dose. Overall survival was from start of therapy to date of death. Statistical Analysis
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Demographic and baseline characteristics were compared between the 3 different racial groups using analysis of variance for continuous variables and a chisquared test or Fisher’s exact test for categorical variables. OS was defined as time from diagnosis till death. For patients with unknown vital status, the survival time was censored at the date of last contact. The Kaplan-Meier method was used to summarize survival experience, and log-rank test was used to compare survival experiences between treatment groups.
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For the analysis based on modeling, we proceeded by employing multiple imputation in order to properly handle missing data in covariates of interest. Additionally, since this is essentially an observational study with the goal of assessing the effect of treatment, i.e., biologics (or bevacizumab; secondary analysis), we strived to account for potential confounding and selection bias by employing a propensity score analysis. The models employed for imputation, propensity score and endpoint of interest are described below. Imputation model:
Multiple imputation techniques following the algorithm of full conditional specification19 were used to incorporate the extra variability induced from the imputation. Essentially, rather than imputing one single value for participants with missing covariates, we generated 50. For each realization, the corresponding set of complete data was analyzed in a standard manner and the results were pooled using a set of rules proposed by Rubin 20. The imputation method regressed each covariate with missing value on a priori specified set of covariates, as well as outcome of interest, and then random draws were taken from the posterior predictive conditional distribution. An
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assumption in multiple imputations is that of missing at random (MAR)20. Essentially, MAR means the probability of "missingness" may depend on the data that was observed but not on data values that are missing. In our database, MAR is a reasonable assumption.
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Propensity score model
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The propensity score was obtained from a logistic regression model as the conditional probability of being on treatment, biologics, given a set of pre-specified covariates. The following covariates were included in the model on the basis of a priori specification of potential confounding and association with treatment assignment status: age, gender, race/ethnicity, CEA at diagnosis, number of metastatic sites, degree of differentiation, colon vs. rectum, and lines of chemotherapy. Estimated propensity scores were created for each of the 50 imputations. Stabilized inverse probability of treatment (biologics or bevacizumab; secondary analysis) weights (SW) was created to be subsequently utilized in the weighted regression models. Inverse probability weighting can be used to estimate the biologics effect by appropriately adjusting for confounding and selection bias. We also checked the stabilized weights and none were extreme, i.e., all fell within (0.1, 10). Endpoint model
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We used a weighted semi-parametric accelerated failure time (AFT) model21 with SWs to estimate the effect of biologics (or bevacizumab; secondary analysis) on survival time using robust standard errors. The robust standard errors were derived via bootstrap re-sampling; using 500 bootstrap samples. A priori specified interaction terms were assessed followed by subgroup analysis. Results are presented as point estimate of effect of biologics (or bevacizumab), i.e., multiplicative factor on survival time, and associated 95% confidence intervals. In addition to nominal computed p-value, we also computed multiple comparison adjusted p-value, for primary analysis only, by employing the method of Benjamini and Hochberg22; primary results are based on adjusted pvalue.
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All analyses were carried out using STATA 11.2 software (STATACorp LP) or R version 3.1.2 (http://www.r-project.org). RESULTS
Patient characteristics
We initially identified over 2000 patients from the MMC cancer registry with diagnoses of CRC from 2001-2011. We narrowed our analysis to those 290 patients who had metastatic disease, and were treated within our medical center. The median age at diagnosis was 60 years, 43.8% were males, and 51.4% had received 2 or more lines of therapy (Table 1). Overall, NH-Blacks represented 45.9%, NH-Whites 27.9%
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and Hispanic 26.2%. Overall, 53.8% of the patients had received biologics, and 48.6% had received bevacizumab. Clinical Outcome
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Patients receiving CBT showed an overall improvement in the primary endpoint of OS. The OS ranged from 0.27 to 122.47 months. The median OS was 15.2 months in the CT group, versus 25.6 months in CBT group; p=0.0041 by log-rank test; (Figure1A). In a similar unadjusted analysis, the clinical factors, including number of metastatic sites (1 vs. >2; p=0.02); degree of differentiation (well/moderate vs. poor; p=0.008); CEA <41 (< vs. > median; p=0.01); and receiving >2 lines of chemotherapy (vs. 1); p=0.01) were associated with better survival (Table 2).
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Subsequently, we performed multiple imputations to properly handle missing data. Five covariates had missing values with varying level of "missingness": number of metastatic sites (0.7%), marital status (3.1%), degree of differentiation (16.9%), diabetes and hypertension (43.4%) each. This was followed with analysis based on propensity score as described in the methods section (Table 3). As shown in Figure 2, there is sufficient common support in order to get unbiased assessment of the effect of biologics.
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In this model, the added benefit of biologics was sustained, with a survival benefit [1.44, 95% confidence interval (Cl) 1.11-1.86, p=0.0389; adjusted for multiple comparison, Table 3a]. The result implies that biologic therapy is extending overall survival or decelerating time-to-death by 1.44 as compared to chemotherapy alone. Further exploratory analysis revealed a suggestive pattern for interaction between biologics and race (race categorized as NH-White vs. Hispanic/Black) (p=0.072). However, this pattern was driven by the clear disparity in benefits from biologics between NH-Whites and Blacks (p=0.072; based on a test for interaction and adjustment for multiple comparison). Of note, about 17% of the data on differentiation and 42% on hypertension and diabetes were missing and analysis was based on multiple imputation as described in the methods section. Therefore, this suggestive result indicates the need for further follow-up based on large database with low percentage of missing data in order to verify this pattern more definitively. Based on this suggestive result, we next performed additional analysis within each racial group. Subgroup analysis based on ethnicity showed survival benefit for NHWhites (15.1 vs. 28.6 months, p=0.0014; Figure 1B) but not for Hispanics (14.6 vs. 22.7 months, p=0.0959; Figure1C), or NH-Blacks (18.7 vs. 24.3 months, p=0.5516; Figure1D); by log-rank test. Further analysis based on propensity score, also confirmed this pattern; while the estimated biologics effect was greater than one, i.e., implying survival benefit associated with biologics, it only reached statistical significance among NHWhites (2.01, 95% CI 1.26–3.23, p=0.0309; Table 3a). While there is a beneficial trend from biologics among Hispanics, it did not reach statistical significance (1.42, 95% CI 0.91 - 2.20, p=0.370).
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We next restricted our analysis to those patients who received bevacizumab only. This would constitute a secondary analysis and as such correction for multiplicity was not carried out. In this analysis too, the added benefit of bevacizumab was sustained, with a survival benefit [1.48, 95% confidence interval (Cl) 1.14-1.93, p= 0.003; Table 3b]. Further exploratory analysis revealed a suggestive pattern for interaction between bevacizumab and race (race categorized as NH-White vs. Hispanic/Black) (p=0.092). However, this pattern was driven by the clear disparity in benefits from biologics between NH-Whites and Blacks (p=0.078). We next performed additional analysis within each racial group. Subgroup analysis based on ethnicity showed survival benefit for NH-Whites (1.85, p=0.015) but not for Hispanics (1.41, p=0.104); or NH-Blacks (1.25, p=0.294); by log-rank test.
DISCUSSION
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Obvious reasons for these differences, though just suggestive, were sought. Lower use of biological therapy was considered; however, 53.8% of all patients, 51.9% of NH-Whites, 50.4% of NH-Blacks, and 61.8% of Hispanics had received biologic agents (p=0.26). As presence of a KRAS mutation precludes the use of cetuximab and panitumumab11,12,14,15, we looked at their prevalence, and noted no difference. KRAS results were available for 33% of the patients (incorporated into routine clinical care in 2008), and 48.6% had a mutation, Whites-43%, Blacks-50%, and Hispanics-63% (p=0.369). We next considered uncontrolled hypertension and diabetic nephropathy/proteinuria as contraindications to bevacizumab, and saw no differences among the races (p=0.343 for diabetes, and 0.315 for hypertension). Moreover, these prognostic factors failed to show an association with neither survival (see table 2) nor treatment assignment (p=0.55 for diabetes, and 0.64 for hypertension) and as a consequence not included in the propensity score model.
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Biologic agents (the monoclonal antibodies, bevacizumab, cetuximab, and panitumumab) have significantly improved the outcome for patients with mCRC, as suggested by clinical trial data. A vexing limitation of current data is that clinical trial enrolment is biased, highly selective, dominated by White patients, and do not truly represent the real world scenario18. In a recent SEER-Medicare analysis, the additional benefit of bevacizumab was only observed with irinotecan, but not oxaliplatin, and was less impressive than reported in trials17. In multiple prospective trials and retrospective reports, the beneficial effects are less impressive than initially perceived, improving OS by only 11-20% 10,17,23. In a similar vein, the beneficial effects of the biologic agents in the ethnic minority community remain unknown. To address this perplexing issue, we embarked on a CER study to gain insight, particularly since our cancer center caters to a predominantly (>70%) minority population. CER can be utilized to study the added value of alternative modalities on clinical outcomes and has the potential to be practice changing, or hypothesis generating, for prospective validation. We report for the first time that Black patients appear not to benefit from the monoclonal antibodies, and the clinical outcome appears comparable to those patients who receive chemotherapy only.
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The data was thoroughly and carefully analyzed with proper handling of missing data and unadjusted analysis. This was followed by a propensity score analyses for exploration of the "a priori" specified interaction between biologics and race, adjusting for potential confounding and selection bias. As expected, a few common themes were apparent repeatedly; low number of metastatic sites, well/moderate degree of differentiation, low CEA (2 lines of chemotherapy, and exposure to biologic agents were associated with better survival; based on univariate analysis. In the subsequent model, accounting for potential confounding and selection bias, therapy with a biologic agent stood as a strong prognostic marker. Even more interesting is that this was only apparent in the White population, but not in other races, and there was a suggestive pattern of interaction observed. Given the data and its potential limitations, the subgroup analysis is suggestive of interaction, and clearly, this impression needs to be confirmed in a larger database with low percentage of missing data on important prognostic factors.
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There is limited literature on outcome differences based on race/ethnicity; however, it appears that most of them are due to disparities in access to care. For example, even after curative surgery, the mortality of Blacks is higher than for Whites, with a hazard ratio (HR) of 1.2524. The issue of race as a predictive biomarker has not been considered to such an extent until now. However, there are some ongoing efforts in this direction. Data from a large randomized phase III trial (E3200) found that there was an inferior OS of African American vs. Caucasian patients with mCRC when bevacizumab was added to standard FOLFOX chemotherapy, 10.2 vs. 11.8 months, respectively, p=0.003. Blacks did not benefit from bevacizumab, while the Whites did 25. We believe that such analysis of ethnic and racial differences should get due consideration and form the basis of future endeavors. In keeping with this earlier report, we also carried out a secondary analysis of our data with a specific focus on bevacizumab only, and found very similar results.
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We considered potential reasons for our results. A clear cause for this difference could be a lower use of biological therapy among the minorities; however, we employed propensity score analysis in order to address selection bias. Furthermore, as presence of a KRAS mutation precludes the use of cetuximab and panitumumab, its prevalence was considered, and again, no differences were observed (p=0.369). Assessing comorbidities, especially, uncontrolled hypertension and diabetes, we found no differences among the races. In addition, these co-morbidities were neither associated with OS, nor treatment received, in our database. This report will be incomplete without discussion of its limitations. We chose to combine all biologic drugs that are currently available, rather than focus on a single one. However, we believe this to be justified, since each agent has an incremental clinical benefit, with additional cost and, presumably, toxicities. In an effort to address this, we have considered bevacizumab as a secondary analysis, and observe a very similar pattern. Second, we have not captured toxicities of these agents. However, as a corollary, that would only have clinical significance, provided we have a biological
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plausibility that the biologics actually result in a higher mortality in an additional subgroup within a particular race, such that the beneficial effect was nullified within that race. There is currently no evidence to support such a notion, and can be safely ruled out. Third, we had low number of Hispanic patients, and even though we see a trend favoring the use of biologic therapy, this did not reach statistical significance. However, with a larger patient database, we may see a benefit in this sub-population, since the effect of biologics in our current analysis is 1.42, implying a potential 42% improvement in median OS.
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We did not consider the role of BRAF gene (V600E) mutations in our patients. While it appears to carry a worse prognosis in metastatic cancer, there is no data to support its role as a predictive biomarker. Further, in the curative setting, as in an update from MOSAIC, it failed to demonstrate prognostic significance26. BRAF mutation prevalence could influence OS only under the highly unlikely situation that it is higher in minorities, selectively in the group treated with biologic agents. Considering all of the above including its low prevalence of 5-9%13; it is highly improbable that incorporation of BRAF mutation status will affect the outcome of our study.
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So how does one utilize these data? This report generates some interesting and perplexing ethical questions for future directions. To validate and confirm our findings, the prospective approach would be to randomize the minority patients to CT or CBT arms, an ethically non-viable option. Our future approach is to validate these findings retrospectively by gathering data from hospitals that serve a predominantly minority population, and to access the SEER Medicare data. There is a strong potential that race may indeed be validated as novel biomarker of benefit for the biologic drugs in mCRC. Overall, this study forms an ideal example of a CER based approach to medical therapeutics in the management of patients with mCRC. It is observational studies such as these that can ignite endeavors that could lead to practice altering results.
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Legends to Figures and Tables
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Legends to Figures Figure 1: Survival curves for patients treated with chemotherapy only versus those treated with chemotherapy plus biologics. A: all patients; B: NH-Whites; C: Hispanics, and D: NH-Blacks (p-values are from corresponding log-rank test). Figure 2: Assessment of common support using distribution of propensity score by treatment received. Legends to Tables
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Table 1: Patient characteristics of the 290 patients who were included in this analysis
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Table 2: Univariate analysis of clinical parameters that are considered important in determining outcome of patients with mCRC. Variable of interest: Chemotherapy + biologics vs. Chemotherapy alone. Table 3a: Propensity Score based Analysis, Variable of interest: Chemotherapy + biologics vs. Chemotherapy alone P values given are unadjusted (adjusted for multiple testing using the Benjamini and Hochberg approach).
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Table 3b: Propensity Score based Secondary Analysis, Variable of interest: Chemotherapy + bevacizumab vs. Chemotherapy alone
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Table 4: Propensity Score Model (for biologics) Propensity score model result pooled over 50 imputations. The predictive capacity of the propensity models ranged between 0.72 – 0.74 with a median of 0.73
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References:
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1. Siegel RL, Miller KD, Jemal A: Cancer statistics, 2016. CA Cancer J Clin 66:7-30, 2016 2. Surveillance, Epidemiology, and End Results; Incidence and Outcomes in combined Colon and Rectal Cancer; Years 2008-2012, 3. Cancer Prevention and Early Detection Facts and Figures. , 2015 4. Goldberg RM, Sargent DJ, Morton RF, et al: A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. J Clin Oncol 22:23-30, 2004 5. Aparo S, Goel S: Evolvement of the treatment paradigm for metastatic colon cancer. From chemotherapy to targeted therapy. Crit Rev Oncol Hematol 83:4758, 2012 6. Tournigand C, Andre T, Achille E, et al: FOLFIRI followed by FOLFOX6 or the reverse sequence in advanced colorectal cancer: a randomized GERCOR study. J Clin Oncol 22:229-37, 2004 7. Zafar SY, Marcello JE, Wheeler JL, et al: Longitudinal patterns of chemotherapy use in metastatic colorectal cancer. J Oncol Pract 5:228-33, 2009 8. Cassidy J, Clarke S, Diaz-Rubio E, et al: Randomized phase III study of capecitabine plus oxaliplatin compared with fluorouracil/folinic acid plus oxaliplatin as first-line therapy for metastatic colorectal cancer. J Clin Oncol 26:2006-12, 2008 9. Hurwitz H, Fehrenbacher L, Novotny W, et al: Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 350:2335-42, 2004 10. Saltz LB, Clarke S, Diaz-Rubio E, et al: Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J Clin Oncol 26:2013-9, 2008 11. Schwartzberg LS, Rivera F, Karthaus M, et al: PEAK: a randomized, multicenter phase II study of panitumumab plus modified fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) or bevacizumab plus mFOLFOX6 in patients with previously untreated, unresectable, wild-type KRAS exon 2 metastatic colorectal cancer. J Clin Oncol 32:2240-7, 2014 12. Douillard JY, Siena S, Cassidy J, et al: Final results from PRIME: randomized phase III study of panitumumab with FOLFOX4 for first-line treatment of metastatic colorectal cancer. Ann Oncol 25:1346-55, 2014 13. National Comprehensive Cancer Network Guidelines. Colon Cancer. , 2015 14. Allegra CJ, Jessup JM, Somerfield MR, et al: American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. J Clin Oncol 27:2091-6, 2009 15. Dahabreh IJ, Terasawa T, Castaldi PJ, et al: Systematic review: Antiepidermal growth factor receptor treatment effect modification by KRAS mutations in advanced colorectal cancer. Ann Intern Med 154:37-49, 2011
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62 32-89
60 27-84
Gender - # (%) Male Female
45 (55.6) 36 (45.4)
56 (42) 77 (58)
# metastatic lesions # (%) One 49 (60.5) Two or more 32 (39.5)
46 (68.7) 21 (31.3)
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Differentiation - # (%) Well-Moderate Poor
46 (35) 87 (65)
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32 (39.5) 49 (60.5)
# chemotherapy lines # (%) administered One 37 (45.7) Two or more 44 (54.3)
68 (51) 65 (49)
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Metastatic sites - # (%) Liver only Others
0.64
60 28-87
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Age – years Median Range
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Table 1: Patient demographics (n=290) _________________________________________________________________________________ Characteristics NH White NH Black Hispanic p-value N=81 n=133 n=76 27.9% 45.9% 26.2% _________________________________________________________________________________
80 (69.6) 35 (30.4)
0.03
26 (34) 50 (66) 0.68
30 (39.4) 46 (60.6) 0.16
48 (63.2) 28 (36.8) 0.75 46 (74.2) 16 (25.8) 0.85
67 (50) 66 (50)
37 (48.7) 39 (51.3)
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0.59 44 (54.3) 37 (45.7)
65 (49) 68 (51)
36 (47.4) 40 (53.6)
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CEA (dichotomized at median) – ng/mL < 41 > 41
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Received biologics 42 (51.9) 67 (50.4) 47(61.8) 0.26 Bevacizumab 41 (50.6) 58 (43.6) 42 (55.3 Cetuximab 14 (17.3) 30 (22.6) 18 (23.7) Panitumumab 8 (9.9) 8 (6) 1 (1.3) ___________________________________________________________________________________
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0.41 0.99 0.40 0.09 0.80 0.02 0.008* 0.01 0.01 0.53 0.47* 0.42*
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Chemotherapy + biologics vs. Chemotherapy alone Gender Age at diagnosis (dichotomized at median) Race Colon vs. Rectum Liver only vs. other # metastatic sites (1 vs > 2) Differentiation (well/mod vs poorly) CEA at diagnosis (dichotomized at median) Lines of chemotherapy (1 vs > 2) Marital status Hypertension Diabetes
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Table 2: Univariate analysis ____________________________________________________________ Variables Log-rank p-value ____________________________________________________________
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*Multiple imputation employed ______________________________________________________________
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Overall
290
1.44
1.11 -1.86
0.005
Whites
81
2.01
1.26-3.23
0.004
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Table 3a: Propensity score based analysis overall and across the races ____________________________________________________________________ Group # patients Effect of 95% CI p-value Biologics† (adjusted*) ____________________________________________________________________
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(0.0389)
(0.0309) 76
1.42
0.91-2.20
Blacks
133
1.12
0.76 -1.66
0.123
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Hispanics
(0.370)
0.596
(0.587)
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*P-value adjusted for multiple testing using the Benjamini and Hochberg approach † A multiplicative factor by which biologics extends overall survival. Variable of interest: Chemotherapy + biologics (CBT) vs. Chemotherapy alone (CT)
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1.48
1.14 -1.93
0.003
Whites
80
1.85
1.13-3.03
0.015
Hispanics
70
1.41
0.93-2.15
0.104
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Table 3b: Propensity score based secondary analysis: overall and across the races (biologics restricted to bevacizumab) ____________________________________________________________________ Group # patients Effect of 95% CI p-value Biologics† ____________________________________________________________________
Blacks 123 1.25 0.82 -1.92 0.294 ____________________________________________________________________ †
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A multiplicative factor by which biologics extends overall survival. Variable of interest: Chemotherapy + biologics (Avastin) vs. Chemotherapy alone (CT)
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Table 4. Propensity score model Odds ratio
95% CI
p-value
Age at diagnosis (dichotomized at median)
0.53
0.30 – 0.93
0.03
Gender
1.52
0.85 – 2.72
0.16
Hispanic
2.08
0.93 – 4.57
Black
0.85
# metastatic sites (1 vs > 2)
0.92
Differentiation (well/mod vs poorly)
0.53
Colon vs. Rectum
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– 1.65
0.63 0.79 0.05
1.57
0.84 - 2.94
0.15
0.76
0.44 – 1.35
0.36
3.71
2.10 – 6.49
0.00**
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Lines of chemotherapy (1 vs > 2)
0.43 – 1.67
0.07
0.29 – 0.99
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CEA at diagnosis (dichotomized at median)
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Race
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Covariate
**: p=0.0000098
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Propensity score model result pooled over 50 imputations. The predictive capacity of the propensity score models ranged between 0.72 – 0.74 with a median of 0.73
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Figure 1a
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60 Time in Months
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Figure 2
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Biologics
S1533-0028(16)30187-6
DOI:
10.1016/j.clcc.2017.03.004
Reference:
CLCC 361
To appear in:
Clinical Colorectal Cancer
Received Date: 19 September 2016 Revised Date:
18 January 2017
Accepted Date: 1 March 2017
Please cite this article as: Goel S, Negassa A, Khot A, Goyal D, Guo S, Nandikolla A, Bakirhan K, Polineni R, Shah U, Chaudhary I, Ghalib MH, Rajdev L, Kaubisch A, Chuy J, Aparo S, Comparative Effectiveness Research: the Impact of Biologic Agents In Ethnic Minorities with Metastatic Colorectal Cancer, Clinical Colorectal Cancer (2017), doi: 10.1016/j.clcc.2017.03.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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COMPARATIVE EFFECTIVENESS RESEARCH: THE IMPACT OF BIOLOGIC AGENTS IN ETHNIC MINORITIES WITH METASTATIC COLORECTAL CANCER
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Sanjay Goel1,4,5,*,$, Abdissa Negassa3,5,*, Ashish Khot1,4, Dharmendra Goyal1,4, Shuang Guo2,4 , Amara Nandikolla1,4, Kamila Bakirhan1,4, Rahul Polineni1,4, Umang Shah1,5, Imran Chaudhary1,4, Mohammad H. Ghalib1,4, Lakshmi Rajdev1,4,5, Andreas Kaubisch1,4,5, Jennifer Chuy1,4,5, and Santiago Aparo1,4,5 Department of Medical Oncology1, Department of Medicine2, Department of Epidemiology and Population Health3, Montefiore Medical Center4, Bronx NY, Albert Einstein College of Medicine5, Bronx NY
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Corresponding Author $ Sanjay Goel, MD, MS Professor of Clinical Medicine Albert Einstein College of Medicine Department of Medical Oncology Montefiore Medical Center 1695 Eastchester Road Bronx NY 10461 Phone: 718-405-8404 Fax: 718-405-8433 E mail: [email protected]
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Equal contribution*
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Key words: comparative effectiveness research; colorectal cancer; biologic therapy; chemotherapy; bevacizumab; cetuximab; panitumumab; Hispanic; Black; Caucasian; race; clinical outcome
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Running Title: Biologics and race in colorectal cancer SG was funded by a grant "Advanced Clinical Research Award" in colorectal cancer by the Conquer Cancer Foundation of the American Society of Clinical Oncology
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Conflicts of Interest: None for all authors
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ABSTRACT
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Background: Biologic agents have improved the outcome of patients with metastatic colorectal cancer. Clinical trials have included a predominately White population (85%). Hispanics and Blacks were underrepresented; and the real world benefit among them remains unknown. Comparative Effectiveness Research is a tool allowing for this exploration. Patients and Methods: Demographic and clinical characteristics of patients treated for mCRC in years 2000-2011 were extracted from medical records of Montefiore Medical Center. A semi parametric accelerated failure time (AFT) model was used to assess survival differences between patients treated with chemotherapy alone (CT) vs. chemotherapy plus biologics (CBT). Results: Among 290 patients; 45.9%Black, 26.2%-Hispanic, and 27.9%-White; 53.8% received-biologics. The median overall survival (OS) was 15.2 months in the CT group and 25.6 months in CBT group (p=0.004). On univariate analysis; lower number of metastatic sites, CEA <41 ng/mL, and more lines of chemotherapy, were associated with an improved OS. In a propensity score based analysis of the entire cohort, CBT offered a survival benefit compared to CT (increased median survival 1.44 fold, 95%Cl 1.11-1.86, p=0.038). Subgroup analysis suggested a survival benefit for Whites (2.01, 95%CI 1.26-3.23, p=0.031), but not for Hispanics (1.42, 95%CI 0.91-2.20, p=0.370) or Blacks (1.12, 95%CI 0.76-1.66, p=0.596). Conclusions: In this cohort, CBT was associated with longer survival, the effect mainly driven by Whites, while Blacks did not appear to benefit. Clearly, these data are provocative, warranting further confirmation. Effort to increase ethnic minority patients’ enrolment in clinical trials is required to prospectively define the benefit from novel therapies.
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INTRODUCTION AND BACKGROUND
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Colorectal cancer (CRC) was diagnosed in 134,490, and resulted in the death of 49,190 Americans in 2016; the second leading cause of cancer related deaths1. According to Surveillance, Epidemiology, and End Results (SEER) data, Non-Hispanic (NH) Blacks have a higher incidence (39.5 vs. 29.1 per 100.000) and worse outcomes (9.1 vs. 13.7% 5-year overall survival [OS]) than NH-Whites, respectively2. In spite of recent major gains in therapeutic options, metastatic (m)CRC remains an incurable illness, with a dismal 5 year OS rate of 12.5%3. The median OS of patients with mCRC treated with cytotoxic drugs (CT), 5-fluorouracil (5-FU), oxaliplatin and irinotecan is around 20 months4. The addition of biologic agents, namely, bevacizumab [humanized monoclonal antibody to vascular endothelial growth factor (VEGF)]; and cetuximab (chimeric) and panitumumab (fully human) [monoclonal antibodies to epidermal growth factor receptor (EGFR)], leads to a median OS of around 30 months with response rates of 50-60%5-13. The greatest benefit of anti-EGFR therapy is observed exclusively in patients with RAS WT status14. Importantly, their use in patients with a KRAS mutation may be detrimental, with inferior outcomes12,15. The incorporation of molecular markers allows for better selection and personalization of therapy5.
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Ironically, despite the lack of strong evidence of survival benefit supporting the addition of bevacizumab to FOLFOX in the first line setting, this combination continues to be the commonest one, preferred by 64% of US oncologists7. It is possible, that bevacizumab is not as effective when used with regimens using oxaliplatin (e.g., FOLFOX) as compared to irinotecan (e.g., IFL), and may be diluted by the superiority of infusional 5-FU regimens8,10,16. Most importantly, a recent analysis of SEER-Medicare data has reported that the addition of bevacizumab only results in a modest improvement in OS, lower than that reported in clinical trials17; and only in combination with irinotecan and not so with oxaliplatin. This exemplifies the phenomenon that results of clinical trials are not always reproducible in the “real-world” setting.
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Furthermore, most of the clinical trials that have demonstrated this benefit have been performed in the White population; with an under-representation of patients from ethnic minorities4,9,11,18 .The real world benefit is little known with the suggestion that it is underwhelming compared to clinical trial data17. With this background, and in keeping with the fact that we cater to a largely underserved ethnic minority population, we initiated a Comparative Effectiveness Research (CER) study to characterize the real world benefit of the addition of biologics to traditional cytotoxic chemotherapy. PATEINTS AND METHODS Clinical Data
The initial cohort was obtained from the Montefiore Medical Center (MMC) cancer registry database. It included patients diagnosed with CRC between the years 2000-2011 that received medical care at MMC. The database included demographic and survival information such as: date of birth/diagnosis/last contact (date of death or
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last follow-up), ethnicity (categorized as Hispanic, NH-White, and NH-Black), and tumor stage at diagnosis. Tumors were staged using the American Joint Committee on Cancer criteria. Ethnicity data was based on patients’ self-reports. Additional clinical characteristics were extracted through retrospective chart review using MMC’s clinical information system (Centricity Enterprise, version 6.6.3). These included: gender; Eastern Cooperative Oncology Group performance status (ECOG PS), body mass index, socioeconomic status, health insurance, colon vs. rectal primary, degree of differentiation, date of diagnosis of metastatic disease, number and sites of metastases, treatment received [including CT or biologics plus CT (CBT)], and important comorbidities such as hypertension and diabetes, that could influence overall survival. We also performed an additional secondary analysis considering only the patients who had received bevacizumab. All patient information was de-identified and stored as a Microsoft 2010 Excel file that was encrypted and password-protected. The study was approved by the MMC institutional review board. A change in the cytotoxic chemotherapy or the biologic agent was considered as moving from one line to the next. Progression free survival was the time from start of a therapy to the time of documented progression or last dose. Overall survival was from start of therapy to date of death. Statistical Analysis
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Demographic and baseline characteristics were compared between the 3 different racial groups using analysis of variance for continuous variables and a chisquared test or Fisher’s exact test for categorical variables. OS was defined as time from diagnosis till death. For patients with unknown vital status, the survival time was censored at the date of last contact. The Kaplan-Meier method was used to summarize survival experience, and log-rank test was used to compare survival experiences between treatment groups.
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For the analysis based on modeling, we proceeded by employing multiple imputation in order to properly handle missing data in covariates of interest. Additionally, since this is essentially an observational study with the goal of assessing the effect of treatment, i.e., biologics (or bevacizumab; secondary analysis), we strived to account for potential confounding and selection bias by employing a propensity score analysis. The models employed for imputation, propensity score and endpoint of interest are described below. Imputation model:
Multiple imputation techniques following the algorithm of full conditional specification19 were used to incorporate the extra variability induced from the imputation. Essentially, rather than imputing one single value for participants with missing covariates, we generated 50. For each realization, the corresponding set of complete data was analyzed in a standard manner and the results were pooled using a set of rules proposed by Rubin 20. The imputation method regressed each covariate with missing value on a priori specified set of covariates, as well as outcome of interest, and then random draws were taken from the posterior predictive conditional distribution. An
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assumption in multiple imputations is that of missing at random (MAR)20. Essentially, MAR means the probability of "missingness" may depend on the data that was observed but not on data values that are missing. In our database, MAR is a reasonable assumption.
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Propensity score model
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The propensity score was obtained from a logistic regression model as the conditional probability of being on treatment, biologics, given a set of pre-specified covariates. The following covariates were included in the model on the basis of a priori specification of potential confounding and association with treatment assignment status: age, gender, race/ethnicity, CEA at diagnosis, number of metastatic sites, degree of differentiation, colon vs. rectum, and lines of chemotherapy. Estimated propensity scores were created for each of the 50 imputations. Stabilized inverse probability of treatment (biologics or bevacizumab; secondary analysis) weights (SW) was created to be subsequently utilized in the weighted regression models. Inverse probability weighting can be used to estimate the biologics effect by appropriately adjusting for confounding and selection bias. We also checked the stabilized weights and none were extreme, i.e., all fell within (0.1, 10). Endpoint model
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We used a weighted semi-parametric accelerated failure time (AFT) model21 with SWs to estimate the effect of biologics (or bevacizumab; secondary analysis) on survival time using robust standard errors. The robust standard errors were derived via bootstrap re-sampling; using 500 bootstrap samples. A priori specified interaction terms were assessed followed by subgroup analysis. Results are presented as point estimate of effect of biologics (or bevacizumab), i.e., multiplicative factor on survival time, and associated 95% confidence intervals. In addition to nominal computed p-value, we also computed multiple comparison adjusted p-value, for primary analysis only, by employing the method of Benjamini and Hochberg22; primary results are based on adjusted pvalue.
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All analyses were carried out using STATA 11.2 software (STATACorp LP) or R version 3.1.2 (http://www.r-project.org). RESULTS
Patient characteristics
We initially identified over 2000 patients from the MMC cancer registry with diagnoses of CRC from 2001-2011. We narrowed our analysis to those 290 patients who had metastatic disease, and were treated within our medical center. The median age at diagnosis was 60 years, 43.8% were males, and 51.4% had received 2 or more lines of therapy (Table 1). Overall, NH-Blacks represented 45.9%, NH-Whites 27.9%
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and Hispanic 26.2%. Overall, 53.8% of the patients had received biologics, and 48.6% had received bevacizumab. Clinical Outcome
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Patients receiving CBT showed an overall improvement in the primary endpoint of OS. The OS ranged from 0.27 to 122.47 months. The median OS was 15.2 months in the CT group, versus 25.6 months in CBT group; p=0.0041 by log-rank test; (Figure1A). In a similar unadjusted analysis, the clinical factors, including number of metastatic sites (1 vs. >2; p=0.02); degree of differentiation (well/moderate vs. poor; p=0.008); CEA <41 (< vs. > median; p=0.01); and receiving >2 lines of chemotherapy (vs. 1); p=0.01) were associated with better survival (Table 2).
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Subsequently, we performed multiple imputations to properly handle missing data. Five covariates had missing values with varying level of "missingness": number of metastatic sites (0.7%), marital status (3.1%), degree of differentiation (16.9%), diabetes and hypertension (43.4%) each. This was followed with analysis based on propensity score as described in the methods section (Table 3). As shown in Figure 2, there is sufficient common support in order to get unbiased assessment of the effect of biologics.
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In this model, the added benefit of biologics was sustained, with a survival benefit [1.44, 95% confidence interval (Cl) 1.11-1.86, p=0.0389; adjusted for multiple comparison, Table 3a]. The result implies that biologic therapy is extending overall survival or decelerating time-to-death by 1.44 as compared to chemotherapy alone. Further exploratory analysis revealed a suggestive pattern for interaction between biologics and race (race categorized as NH-White vs. Hispanic/Black) (p=0.072). However, this pattern was driven by the clear disparity in benefits from biologics between NH-Whites and Blacks (p=0.072; based on a test for interaction and adjustment for multiple comparison). Of note, about 17% of the data on differentiation and 42% on hypertension and diabetes were missing and analysis was based on multiple imputation as described in the methods section. Therefore, this suggestive result indicates the need for further follow-up based on large database with low percentage of missing data in order to verify this pattern more definitively. Based on this suggestive result, we next performed additional analysis within each racial group. Subgroup analysis based on ethnicity showed survival benefit for NHWhites (15.1 vs. 28.6 months, p=0.0014; Figure 1B) but not for Hispanics (14.6 vs. 22.7 months, p=0.0959; Figure1C), or NH-Blacks (18.7 vs. 24.3 months, p=0.5516; Figure1D); by log-rank test. Further analysis based on propensity score, also confirmed this pattern; while the estimated biologics effect was greater than one, i.e., implying survival benefit associated with biologics, it only reached statistical significance among NHWhites (2.01, 95% CI 1.26–3.23, p=0.0309; Table 3a). While there is a beneficial trend from biologics among Hispanics, it did not reach statistical significance (1.42, 95% CI 0.91 - 2.20, p=0.370).
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We next restricted our analysis to those patients who received bevacizumab only. This would constitute a secondary analysis and as such correction for multiplicity was not carried out. In this analysis too, the added benefit of bevacizumab was sustained, with a survival benefit [1.48, 95% confidence interval (Cl) 1.14-1.93, p= 0.003; Table 3b]. Further exploratory analysis revealed a suggestive pattern for interaction between bevacizumab and race (race categorized as NH-White vs. Hispanic/Black) (p=0.092). However, this pattern was driven by the clear disparity in benefits from biologics between NH-Whites and Blacks (p=0.078). We next performed additional analysis within each racial group. Subgroup analysis based on ethnicity showed survival benefit for NH-Whites (1.85, p=0.015) but not for Hispanics (1.41, p=0.104); or NH-Blacks (1.25, p=0.294); by log-rank test.
DISCUSSION
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Obvious reasons for these differences, though just suggestive, were sought. Lower use of biological therapy was considered; however, 53.8% of all patients, 51.9% of NH-Whites, 50.4% of NH-Blacks, and 61.8% of Hispanics had received biologic agents (p=0.26). As presence of a KRAS mutation precludes the use of cetuximab and panitumumab11,12,14,15, we looked at their prevalence, and noted no difference. KRAS results were available for 33% of the patients (incorporated into routine clinical care in 2008), and 48.6% had a mutation, Whites-43%, Blacks-50%, and Hispanics-63% (p=0.369). We next considered uncontrolled hypertension and diabetic nephropathy/proteinuria as contraindications to bevacizumab, and saw no differences among the races (p=0.343 for diabetes, and 0.315 for hypertension). Moreover, these prognostic factors failed to show an association with neither survival (see table 2) nor treatment assignment (p=0.55 for diabetes, and 0.64 for hypertension) and as a consequence not included in the propensity score model.
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Biologic agents (the monoclonal antibodies, bevacizumab, cetuximab, and panitumumab) have significantly improved the outcome for patients with mCRC, as suggested by clinical trial data. A vexing limitation of current data is that clinical trial enrolment is biased, highly selective, dominated by White patients, and do not truly represent the real world scenario18. In a recent SEER-Medicare analysis, the additional benefit of bevacizumab was only observed with irinotecan, but not oxaliplatin, and was less impressive than reported in trials17. In multiple prospective trials and retrospective reports, the beneficial effects are less impressive than initially perceived, improving OS by only 11-20% 10,17,23. In a similar vein, the beneficial effects of the biologic agents in the ethnic minority community remain unknown. To address this perplexing issue, we embarked on a CER study to gain insight, particularly since our cancer center caters to a predominantly (>70%) minority population. CER can be utilized to study the added value of alternative modalities on clinical outcomes and has the potential to be practice changing, or hypothesis generating, for prospective validation. We report for the first time that Black patients appear not to benefit from the monoclonal antibodies, and the clinical outcome appears comparable to those patients who receive chemotherapy only.
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The data was thoroughly and carefully analyzed with proper handling of missing data and unadjusted analysis. This was followed by a propensity score analyses for exploration of the "a priori" specified interaction between biologics and race, adjusting for potential confounding and selection bias. As expected, a few common themes were apparent repeatedly; low number of metastatic sites, well/moderate degree of differentiation, low CEA (
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There is limited literature on outcome differences based on race/ethnicity; however, it appears that most of them are due to disparities in access to care. For example, even after curative surgery, the mortality of Blacks is higher than for Whites, with a hazard ratio (HR) of 1.2524. The issue of race as a predictive biomarker has not been considered to such an extent until now. However, there are some ongoing efforts in this direction. Data from a large randomized phase III trial (E3200) found that there was an inferior OS of African American vs. Caucasian patients with mCRC when bevacizumab was added to standard FOLFOX chemotherapy, 10.2 vs. 11.8 months, respectively, p=0.003. Blacks did not benefit from bevacizumab, while the Whites did 25. We believe that such analysis of ethnic and racial differences should get due consideration and form the basis of future endeavors. In keeping with this earlier report, we also carried out a secondary analysis of our data with a specific focus on bevacizumab only, and found very similar results.
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We considered potential reasons for our results. A clear cause for this difference could be a lower use of biological therapy among the minorities; however, we employed propensity score analysis in order to address selection bias. Furthermore, as presence of a KRAS mutation precludes the use of cetuximab and panitumumab, its prevalence was considered, and again, no differences were observed (p=0.369). Assessing comorbidities, especially, uncontrolled hypertension and diabetes, we found no differences among the races. In addition, these co-morbidities were neither associated with OS, nor treatment received, in our database. This report will be incomplete without discussion of its limitations. We chose to combine all biologic drugs that are currently available, rather than focus on a single one. However, we believe this to be justified, since each agent has an incremental clinical benefit, with additional cost and, presumably, toxicities. In an effort to address this, we have considered bevacizumab as a secondary analysis, and observe a very similar pattern. Second, we have not captured toxicities of these agents. However, as a corollary, that would only have clinical significance, provided we have a biological
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plausibility that the biologics actually result in a higher mortality in an additional subgroup within a particular race, such that the beneficial effect was nullified within that race. There is currently no evidence to support such a notion, and can be safely ruled out. Third, we had low number of Hispanic patients, and even though we see a trend favoring the use of biologic therapy, this did not reach statistical significance. However, with a larger patient database, we may see a benefit in this sub-population, since the effect of biologics in our current analysis is 1.42, implying a potential 42% improvement in median OS.
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We did not consider the role of BRAF gene (V600E) mutations in our patients. While it appears to carry a worse prognosis in metastatic cancer, there is no data to support its role as a predictive biomarker. Further, in the curative setting, as in an update from MOSAIC, it failed to demonstrate prognostic significance26. BRAF mutation prevalence could influence OS only under the highly unlikely situation that it is higher in minorities, selectively in the group treated with biologic agents. Considering all of the above including its low prevalence of 5-9%13; it is highly improbable that incorporation of BRAF mutation status will affect the outcome of our study.
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So how does one utilize these data? This report generates some interesting and perplexing ethical questions for future directions. To validate and confirm our findings, the prospective approach would be to randomize the minority patients to CT or CBT arms, an ethically non-viable option. Our future approach is to validate these findings retrospectively by gathering data from hospitals that serve a predominantly minority population, and to access the SEER Medicare data. There is a strong potential that race may indeed be validated as novel biomarker of benefit for the biologic drugs in mCRC. Overall, this study forms an ideal example of a CER based approach to medical therapeutics in the management of patients with mCRC. It is observational studies such as these that can ignite endeavors that could lead to practice altering results.
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Legends to Figures and Tables
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Legends to Figures Figure 1: Survival curves for patients treated with chemotherapy only versus those treated with chemotherapy plus biologics. A: all patients; B: NH-Whites; C: Hispanics, and D: NH-Blacks (p-values are from corresponding log-rank test). Figure 2: Assessment of common support using distribution of propensity score by treatment received. Legends to Tables
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Table 1: Patient characteristics of the 290 patients who were included in this analysis
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Table 2: Univariate analysis of clinical parameters that are considered important in determining outcome of patients with mCRC. Variable of interest: Chemotherapy + biologics vs. Chemotherapy alone. Table 3a: Propensity Score based Analysis, Variable of interest: Chemotherapy + biologics vs. Chemotherapy alone P values given are unadjusted (adjusted for multiple testing using the Benjamini and Hochberg approach).
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Table 3b: Propensity Score based Secondary Analysis, Variable of interest: Chemotherapy + bevacizumab vs. Chemotherapy alone
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Table 4: Propensity Score Model (for biologics) Propensity score model result pooled over 50 imputations. The predictive capacity of the propensity models ranged between 0.72 – 0.74 with a median of 0.73
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References:
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1. Siegel RL, Miller KD, Jemal A: Cancer statistics, 2016. CA Cancer J Clin 66:7-30, 2016 2. Surveillance, Epidemiology, and End Results; Incidence and Outcomes in combined Colon and Rectal Cancer; Years 2008-2012, 3. Cancer Prevention and Early Detection Facts and Figures. , 2015 4. Goldberg RM, Sargent DJ, Morton RF, et al: A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. J Clin Oncol 22:23-30, 2004 5. Aparo S, Goel S: Evolvement of the treatment paradigm for metastatic colon cancer. From chemotherapy to targeted therapy. Crit Rev Oncol Hematol 83:4758, 2012 6. Tournigand C, Andre T, Achille E, et al: FOLFIRI followed by FOLFOX6 or the reverse sequence in advanced colorectal cancer: a randomized GERCOR study. J Clin Oncol 22:229-37, 2004 7. Zafar SY, Marcello JE, Wheeler JL, et al: Longitudinal patterns of chemotherapy use in metastatic colorectal cancer. J Oncol Pract 5:228-33, 2009 8. Cassidy J, Clarke S, Diaz-Rubio E, et al: Randomized phase III study of capecitabine plus oxaliplatin compared with fluorouracil/folinic acid plus oxaliplatin as first-line therapy for metastatic colorectal cancer. J Clin Oncol 26:2006-12, 2008 9. Hurwitz H, Fehrenbacher L, Novotny W, et al: Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 350:2335-42, 2004 10. Saltz LB, Clarke S, Diaz-Rubio E, et al: Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J Clin Oncol 26:2013-9, 2008 11. Schwartzberg LS, Rivera F, Karthaus M, et al: PEAK: a randomized, multicenter phase II study of panitumumab plus modified fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) or bevacizumab plus mFOLFOX6 in patients with previously untreated, unresectable, wild-type KRAS exon 2 metastatic colorectal cancer. J Clin Oncol 32:2240-7, 2014 12. Douillard JY, Siena S, Cassidy J, et al: Final results from PRIME: randomized phase III study of panitumumab with FOLFOX4 for first-line treatment of metastatic colorectal cancer. Ann Oncol 25:1346-55, 2014 13. National Comprehensive Cancer Network Guidelines. Colon Cancer. , 2015 14. Allegra CJ, Jessup JM, Somerfield MR, et al: American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. J Clin Oncol 27:2091-6, 2009 15. Dahabreh IJ, Terasawa T, Castaldi PJ, et al: Systematic review: Antiepidermal growth factor receptor treatment effect modification by KRAS mutations in advanced colorectal cancer. Ann Intern Med 154:37-49, 2011
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16. Meta-analysis Group In C, Piedbois P, Rougier P, et al: Efficacy of intravenous continuous infusion of fluorouracil compared with bolus administration in advanced colorectal cancer. J Clin Oncol 16:301-8, 1998 17. Meyerhardt JA, Li L, Sanoff HK, et al: Effectiveness of bevacizumab with first-line combination chemotherapy for Medicare patients with stage IV colorectal cancer. J Clin Oncol 30:608-15, 2012 18. Murthy VH, Krumholz HM, Gross CP: Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA 291:2720-6, 2004 19. van Buuren S: Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res 16:219-42, 2007 20. Rubin DB: Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, 1987, ISSN 0271-6232 Biometrical Journal, 1989, pp 131-132 21. Chiou SH, Kang S, Kim J, et al: Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations. Lifetime Data Anal 20:599-618, 2014 22. Benjamini Y, Hochberg, Y., : Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57:289-300, 1995 23. Welch S, Spithoff K, Rumble RB, et al: Bevacizumab combined with chemotherapy for patients with advanced colorectal cancer: a systematic review. Ann Oncol 21:1152-62, 2010 24. Breslin TM, Morris AM, Gu N, et al: Hospital factors and racial disparities in mortality after surgery for breast and colon cancer. J Clin Oncol 27:3945-50, 2009 25. Catalano P.J. MEP, Giantonio B. J., Meropol N. J., Benson, III, A.B.: Outcomes differences for African Americans and Caucasians treated with bevacizumab, FOLFOX4 or the combination in patients with metastatic colorectal cancer (MCRC): Results from the Eastern Cooperative Oncology Group Study E3200. Journal of Clinical Oncology, 2007 ASCO Annual Meeting Proceedings (Post-Meeting Edition). 25, 2007 26. Andre T, de Gramont A, Vernerey D, et al: Adjuvant Fluorouracil, Leucovorin, and Oxaliplatin in Stage II to III Colon Cancer: Updated 10-Year Survival and Outcomes According to BRAF Mutation and Mismatch Repair Status of the MOSAIC Study. J Clin Oncol 33:4176-87, 2015
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62 32-89
60 27-84
Gender - # (%) Male Female
45 (55.6) 36 (45.4)
56 (42) 77 (58)
# metastatic lesions # (%) One 49 (60.5) Two or more 32 (39.5)
46 (68.7) 21 (31.3)
AC C
Differentiation - # (%) Well-Moderate Poor
46 (35) 87 (65)
TE D
32 (39.5) 49 (60.5)
# chemotherapy lines # (%) administered One 37 (45.7) Two or more 44 (54.3)
68 (51) 65 (49)
EP
Metastatic sites - # (%) Liver only Others
0.64
60 28-87
M AN U
Age – years Median Range
SC
RI PT
Table 1: Patient demographics (n=290) _________________________________________________________________________________ Characteristics NH White NH Black Hispanic p-value N=81 n=133 n=76 27.9% 45.9% 26.2% _________________________________________________________________________________
80 (69.6) 35 (30.4)
0.03
26 (34) 50 (66) 0.68
30 (39.4) 46 (60.6) 0.16
48 (63.2) 28 (36.8) 0.75 46 (74.2) 16 (25.8) 0.85
67 (50) 66 (50)
37 (48.7) 39 (51.3)
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0.59 44 (54.3) 37 (45.7)
65 (49) 68 (51)
36 (47.4) 40 (53.6)
RI PT
CEA (dichotomized at median) – ng/mL < 41 > 41
AC C
EP
TE D
M AN U
SC
Received biologics 42 (51.9) 67 (50.4) 47(61.8) 0.26 Bevacizumab 41 (50.6) 58 (43.6) 42 (55.3 Cetuximab 14 (17.3) 30 (22.6) 18 (23.7) Panitumumab 8 (9.9) 8 (6) 1 (1.3) ___________________________________________________________________________________
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0.41 0.99 0.40 0.09 0.80 0.02 0.008* 0.01 0.01 0.53 0.47* 0.42*
SC
0.0041
TE D
Chemotherapy + biologics vs. Chemotherapy alone Gender Age at diagnosis (dichotomized at median) Race Colon vs. Rectum Liver only vs. other # metastatic sites (1 vs > 2) Differentiation (well/mod vs poorly) CEA at diagnosis (dichotomized at median) Lines of chemotherapy (1 vs > 2) Marital status Hypertension Diabetes
RI PT
Table 2: Univariate analysis ____________________________________________________________ Variables Log-rank p-value ____________________________________________________________
AC C
EP
*Multiple imputation employed ______________________________________________________________
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Overall
290
1.44
1.11 -1.86
0.005
Whites
81
2.01
1.26-3.23
0.004
RI PT
Table 3a: Propensity score based analysis overall and across the races ____________________________________________________________________ Group # patients Effect of 95% CI p-value Biologics† (adjusted*) ____________________________________________________________________
SC
(0.0389)
(0.0309) 76
1.42
0.91-2.20
Blacks
133
1.12
0.76 -1.66
0.123
M AN U
Hispanics
(0.370)
0.596
(0.587)
AC C
EP
TE D
*P-value adjusted for multiple testing using the Benjamini and Hochberg approach † A multiplicative factor by which biologics extends overall survival. Variable of interest: Chemotherapy + biologics (CBT) vs. Chemotherapy alone (CT)
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1.48
1.14 -1.93
0.003
Whites
80
1.85
1.13-3.03
0.015
Hispanics
70
1.41
0.93-2.15
0.104
SC
283
M AN U
Overall
RI PT
Table 3b: Propensity score based secondary analysis: overall and across the races (biologics restricted to bevacizumab) ____________________________________________________________________ Group # patients Effect of 95% CI p-value Biologics† ____________________________________________________________________
Blacks 123 1.25 0.82 -1.92 0.294 ____________________________________________________________________ †
AC C
EP
TE D
A multiplicative factor by which biologics extends overall survival. Variable of interest: Chemotherapy + biologics (Avastin) vs. Chemotherapy alone (CT)
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Table 4. Propensity score model Odds ratio
95% CI
p-value
Age at diagnosis (dichotomized at median)
0.53
0.30 – 0.93
0.03
Gender
1.52
0.85 – 2.72
0.16
Hispanic
2.08
0.93 – 4.57
Black
0.85
# metastatic sites (1 vs > 2)
0.92
Differentiation (well/mod vs poorly)
0.53
Colon vs. Rectum
M AN U 0.52
– 1.65
0.63 0.79 0.05
1.57
0.84 - 2.94
0.15
0.76
0.44 – 1.35
0.36
3.71
2.10 – 6.49
0.00**
EP
Lines of chemotherapy (1 vs > 2)
0.43 – 1.67
0.07
0.29 – 0.99
TE D
CEA at diagnosis (dichotomized at median)
SC
Race
RI PT
Covariate
**: p=0.0000098
AC C
Propensity score model result pooled over 50 imputations. The predictive capacity of the propensity score models ranged between 0.72 – 0.74 with a median of 0.73
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AC C
EP
0 .2
TE D
0 .4
0 .6
Biologics Chemotherapy p=0.004
0 .0
S u rv iv a l P ro b a b ility
0 .8
SC
1 .0
RI PT
Figure 1a
20
40
60 Time in Months
80
100
120
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AC C
EP
0 .2
TE D
0 .4
0 .6
Biologics Chemotherapy p=0.001
0 .0
S u rv iv a l P ro b a b ility
0 .8
SC
1 .0
RI PT
Figure 1b
20
40
60 Time in Months
80
100
120
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M AN U 0
AC C
EP
0 .2
0 .4
TE D
0 .6
Biologics Chemotherapy p=0.096
0 .0
S u rv iv a l P ro b a b ility
0 .8
SC
1 .0
RI PT
Figure 1c
10
20
30
40 Time in Months
50
60
70
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0
AC C
EP
0 .2
0 .4
TE D
0 .6
Biologics Chemotherapy p=0.552
0 .0
S u rv iv a l P ro b a b ility
0 .8
M AN U
SC
1 .0
RI PT
Figure 1d
20
40
60 Time in Months
80
100
120
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SC M AN U
0.6
EP
TE D
0.4 0.2
Propensity Score
0.8
RI PT
Figure 2
AC C
Chemotherapy
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