eTable 1. Protocol of the Target Trial to Study Adjuvant Fluorouracil-Based Chemotherapy in Stage II Colorectal Cancer and Protocol of the Existing QUASAR Trial (2007)
eTable 2. Protocol of the Target Trial to Study the Addition of Erlotinib to a Regimen of Gemcitabine in Locally Advanced or Metastatic Pancreatic Cancer and Protocol of the Existing Trial (Moore et al. 2007)
eTable 3. Characteristics of Eligible Individuals With Stage II Colorectal Cancer Who Were Included in the Emulation of the Fluorouracil Target Trial at Baseline and the End of the Grace Period (3 Months Post-Baseline), SEER-Medicare 2008-2013
eTable 4. Comparison of Individuals in the Existing QUASAR Trial (2007) in the Emulation of the Fluorouracil Target Trial Using SEER-Medicare 2008-2013
eTable 5. Characteristics of Eligible Individuals With Locally Advanced or Metastatic Pancreatic Cancer Who Were Included in the Emulation of the Erlotinib Target Trial at Baseline and the End of the Grace Period (12 Weeks Post-Baseline), SEER-Medicare 2007-2013
eTable 6. Comparison of Individuals in the Existing Trial (Moore et al. 2007) and in the Emulation of the Erlotinib Target Trial Using SEER-Medicare 2007-2013
eFigure 1. Flowchart of Eligibility for a Target Trial of Adjuvant Fluorouracil-Based Chemotherapy in Individuals With Stage II Colorectal Cancer, SEER-Medicare 2008-2013
eFigure 2. Flowchart of Eligibility for a Target Trial of Addition of Erlotinib to Gemcitabine in Individuals With Locally Advanced or Metastatic Pancreatic Cancer, SEER-Medicare 2007-2013
eFigure 3. Illustration of the Cloning and Censoring Process for the Fluorouracil Target Trial Emulation
eAppendix 1. Codes Used to Identify Variables Used in the Analyses
eAppendix 2. Details of Statistical Analysis
eAppendix 3. Models Used in the Emulation of the Fluorouracil Target Trial
eAppendix 4. Models Used in the Emulation of the Erlotinib Target Trial
eAppendix 5. Sensitivity Analyses for the Fluorouracil Target Trial Emulation
eAppendix 6. Sensitivity Analyses for the Erlotinib Target Trial Emulation
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Petito LC, García-Albéniz X, Logan RW, et al. Estimates of Overall Survival in Patients With Cancer Receiving Different Treatment Regimens: Emulating Hypothetical Target Trials in the Surveillance, Epidemiology, and End Results (SEER)–Medicare Linked Database. JAMA Netw Open. 2020;3(3):e200452. doi:10.1001/jamanetworkopen.2020.0452
Does the Surveillance, Epidemiology, and End Results–Medicare linked database contain sufficient information to estimate the effectiveness of adding a drug to an existing treatment regimen for elderly individuals with cancer who are systematically underrepresented in randomized trials?
After explicitly emulating target trials, findings from this comparative effectiveness study using Surveillance, Epidemiology, and End Results–Medicare data were not meaningfully different from those in elderly subgroup analyses reported from randomized trials. Naive observational estimates, however, were not compatible with those from previous trials.
Analyses using Surveillance, Epidemiology, and End Results–Medicare data may be informative for some research questions examining the comparative effectiveness of oncological therapies for elderly individuals.
The Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database may provide insights into the comparative effectiveness of oncological treatments for elderly individuals who are underrepresented in clinical trials.
To evaluate the suitability of SEER-Medicare data for assessing the effectiveness of adding a drug to an existing treatment regimen on the overall survival of elderly patients with cancer.
Design, Setting, and Participants
This comparative effectiveness study analyzed SEER-Medicare data from 9549 individuals who received a new diagnosis of stage II colorectal cancer (2008-2012) and 940 patients who received a new diagnosis of advanced pancreatic adenocarcinoma (2007-2012), with follow-up to December 31, 2013 (SEER-Medicare data released in 2015). Two (hypothetical) target trials were designed and emulated based on 2 existing randomized clinical trials: (1) adjuvant fluorouracil after curative surgery for individuals with stage II colorectal cancer and (2) erlotinib added to gemcitabine for individuals with advanced pancreatic adenocarcinoma. Data were analyzed January 2018 to March 2019.
The following treatment strategies were compared: (1) fluorouracil initiation vs no initiation within 3 months of tumor resection and (2) erlotinib initiation vs no initiation within 12 weeks of gemcitabine initiation.
Main Outcomes and Measures
All-cause mortality within 60 months of baseline for the fluorouracil trial and within 72 weeks for the erlotinib trial.
Compared with 3293 individuals in the existing fluorouracil trial, 9549 eligible individuals included in the present analyses were more likely to have colon cancer (8565 [90%] vs 2291 [71%]) and were older (median [interquartile range], 79 [73-84] vs 63 [56-68] years). The 5-year risk difference for initiation vs noninitiation of fluorouracil after surgery was −3.8% (95% CI, −14.8% to 12.6%), and the mortality hazard ratio (HR) was 0.95 (95% CI, 0.85-1.04). Compared with 569 individuals in the existing erlotinib trial, 940 eligible patients included in the present analysis were older (median [range], 74 [66-93] vs 64 [36-92] years) and more likely to be male (547 [58%] vs 298 [52%]). The 1-year risk difference for initiation vs noninitiation of erlotinib was 4.7% (95% CI, −9.4% to 18.0%), and the corresponding mortality HR was 1.04 (95% CI, 0.86-1.42). In naive analyses, the mortality HR estimate was 1.14 (95% CI, 0.95-1.36) for the fluorouracil emulation and 0.68 (95% CI, 0.54-0.87) for the erlotinib emulation.
Conclusions and Relevance
The present estimates were similar to those from randomized clinical trials that studied adding the same cancer drugs to existing regimens. The published HR was 1.02 (95% CI, 0.70-1.48) in the fluorouracil trial for individuals aged 70 or older and 0.96 (95% CI, 0.74-1.24) in the erlotinib trial for individuals aged 65 years or older. The SEER-Medicare database may be adequate for studying the real-world effectiveness of adding a drug to treatment regimens used for elderly individuals with cancer.
When randomized clinical trials are not available at the time a clinical decision needs to be made, clinicians often must choose between making decisions based on either observational data or no human data at all. A cautious use of observational data has been recently endorsed by the American Society of Clinical Oncology, which states that observational studies can “inform questions that either have not been or cannot be answered by randomized trials”1(p1845) and “complement the evidence collected in randomized trials.”1(p1845) Observational studies may be particularly important for patients who are underrepresented in trials, such as elderly patients.2,3
Although health care databases make it possible to study cancer treatments as they are used in clinical practice, observational analyses are subject to several sources of bias.4 Some of these biases, however, can be eliminated by using observational data to emulate a (hypothetical) pragmatic target trial.5,6 Explicitly emulating a target trial is important because some well-known failures of observational research7-9 were the result of deviating from basic principles of study design rather than with the shortcomings inherent to observational data.
Here, we demonstrate how to use the Surveillance, Epidemiology, and End Results (SEER)–Medicare database10—a linkage between the SEER cancer registries and Medicare—to emulate target trials that compare cancer treatments for elderly individuals. Our goal is to describe procedures to increase the validity of comparative effectiveness analyses using the SEER-Medicare database.11-13 To demonstrate these procedures, we emulated 2 target trials for estimating the effect of the addition of a cancer drug to an existing treatment regimen on overall survival: (1) adjuvant fluorouracil after curative surgery in individuals with stage II colorectal cancer and (2) the addition of erlotinib to a regimen of gemcitabine for individuals with advanced pancreatic adenocarcinoma. As a benchmark, we used the age-specific estimates from 2 published randomized clinical trials,14,15 both of which estimated a near-null and imprecise effect for elderly individuals: hazard ratios (HRs) of 1.02 (95% CI, 0.70-1.48) for fluorouracil and 0.96 (95% CI, 0.74-1.24) for erlotinib. We also compared the emulated randomized clinical trial estimates with those from naive observational analyses that did not attempt to emulate a target trial.
The SEER-Medicare database is a linkage of patient demographic and cancer-related variables collected by 17 SEER cancer registries across 12 states with Medicare claim files from the Centers for Medicare & Medicaid Services. The SEER data are summarized in the Patient Entitlement and Diagnosis Summary File, which is linked to Medicare claims. More information about the SEER-Medicare linked database can be found elsewhere.16 This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline and the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) reporting guideline for good practices for comparative effectiveness research.17,18 The institutional review board at the Harvard T.H. Chan School of Public Health deemed this study exempt from review because the research involves the study of publicly available deidentified data.
To identify eligible individuals, construct a combined comorbidity score19 and a performance status proxy score,20,21 and also identify all other variables in this study, we used Medicare claims from the Inpatient, Outpatient, Home Health Agency, Durable Medical Equipment, Medpar, National Claims History, and Patient Entitlement and Diagnosis Summary Files. To identify treatment, we used the Healthcare Common Procedure Coding System and Common Procedural Terminology codes. Because erlotinib is an oral medication, we determined its use by records of filled prescriptions in Medicare Part D, which started in 2007. See eAppendix 1 in the Supplement for details on data use.
Our approach had 2 steps. The first step was to fully articulate the clinical question by specifying the protocol of the (hypothetical) target trial.5 The second step was to emulate the target trial using the observational data. Table 1 and Table 2 outline the specifications of 2 target trials: (1) the addition of fluorouracil in stage II colorectal cancer and (2) the use of erlotinib in advanced pancreatic adenocarcinoma, based on existing randomized trials of fluorouracil14 and erlotinib15 (eTable 1 and eTable 2 in the Supplement). Table 1 and Table 2 also summarize the emulation procedure for both target trials using SEER-Medicare data. Key steps of the procedure are described herein.
The patient eligibility process is given in eFigure 1 and eFigure 2 in the Supplement. In addition to the eligibility criteria of the existing trials, our target trials also required having 1 year of continuous enrollment in Medicare Parts A and B, without being enrolled in a health maintenance organization, and having less than 3 months between diagnosis and initiation of first-line treatment.
For the fluorouracil trial, there were no eligibility restrictions related to the use of neoadjuvant chemotherapy or radiation. In addition, because some colorectal cancers are diagnosed during surgery, we allowed surgery to occur up to 4 weeks before the recorded date of cancer diagnosis to account for time spent in pathologic confirmation of the diagnosis.
For the erlotinib target trial, 3 months of enrollment in Medicare Part D was also required. In addition, individuals with an early cancer stage (I, II, or III) who had undergone surgery to remove the tumor began a second eligibility period if they initiated gemcitabine at least 12 weeks after surgery (otherwise gemcitabine was considered adjuvant care rather than treatment for progression) and before initiating other adjuvant chemotherapy or radiation; this modification emulated the randomized clinical trial conducted by Moore et al15 (eTable 2 in the Supplement).
The treatment strategies compared in our hypothetical target trials mimicked those in 2 trials: (1) initiation of fluorouracil vs no initiation of fluorouracil and (2) initiation of erlotinib vs no initiation of erlotinib in individuals receiving gemcitabine. All components of the treatment strategy needed to be in place within a prespecified period (the grace period) after baseline. Because the existing trials did not explicitly specify the length of the grace period, we chose a 3-month period based on our understanding of the clinical management of stage II colorectal cancer and of advanced pancreatic adenocarcinoma.
Observational analyses attempt to emulate randomized assignment via adjustment for confounders (ie, potential prognostic factors that are imbalanced between treatment groups at baseline). For example, performance status was expected to be a confounder for the effect of erlotinib, whereas perceived risk of relapse (summarized here by size of the tumor, grade of differentiation, number of resected nodes, and T category) was expected to be a confounder for that of fluorouracil. We adjusted for those factors and demographic characteristics, geographic characteristics, and comorbidities (as identified by previously validated procedure and diagnosis codes20-26) listed in Table 1 and Table 2. Target trials must be pragmatic trials in which treatment assignment is not blinded because observational data typically cannot be used to emulate double-blind trials.5
The start of follow-up (baseline or time zero) for each individual was the time of first eligibility: the date of the first gemcitabine claim while otherwise eligible in the emulation of the erlotinib trial and the date of hospital discharge after surgery while otherwise eligible in the emulation of the fluorouracil trial. The end of follow-up was death, loss of insurance eligibility follow-up (loss of enrollment in Parts A, B, or D; enrollment in a health maintenance organization), or administrative end of follow-up (December 31, 2013; 60 months for the fluorouracil trial and 72 weeks for the erlotinib trial), whichever was earliest.
The primary outcome of interest in both target trials was all-cause mortality. The SEER-Medicare database contains information about the date of death in both the SEER file and the Medicare claims. We used the Medicare date of death, which is more up to date and is validated by the National Death Index.27 The SEER-Medicare data do not include enough information to reliably study progression-free survival.28
Our causal contrast of interest was the per-protocol effect, that is, the effect that would have been observed if all trial participants had adhered to the treatment strategies specified in the protocol.29 Because our treatment strategies simply required the initiation of either fluorouracil or erlotinib within the grace period (regardless of future continuation), our causal contrast was similar to the intention-to-treat effect in a trial in which all individuals initiated their assigned treatment during the grace period, even if some of them later discontinued treatment.
We refer to analyses whose goal was to estimate the per-protocol effect as “per-protocol analyses”; these analyses should not to be confused with naive per-protocol analyses conducted in some trials.5 There are 3 steps of the per-protocol analysis that we implemented in our emulation of the target trials, which could also be implemented in target trials if they were actually conducted. (Technical details are provided in eAppendix 2 in the Supplement.)
For the first step, we censored individuals if or when they deviated from their assigned protocol: individuals who (1) initiated fluorouracil in the observation strategy or (2) initiated erlotinib in the gemcitabine alone strategy. In addition, because eligible individuals could not be uniquely assigned to a strategy at baseline when emulating target trials with a grace period, we duplicated (“cloned”) eligible individuals and assigned 1 clone to each treatment strategy (eFigure 3 in the Supplement).30 We then censored clones if they had no data compatible with their assigned treatment strategy by the end of the grace period.6,30 Details on all possible censoring schemes are given in eAppendix 2 in the Supplement.
Second, we used inverse probability weighting to adjust for the potential selection bias introduced by censoring.31 To do so, we estimated stabilized inverse probability weights as a function of the variables listed in Table 1 and Table 2.32,33 Third, we fit an inverse probability weighted discrete-time hazard model34,35 by pooled logistic regression, with death as the response and the following regressors: the indicator for the assigned treatment strategy, a function of time of follow-up (restricted cubic spline with knots at 3, 16, 30, 44, and 57 months for the fluorouracil emulation; quadratic for the erlotinib emulation), product terms for treatment strategy and time, and baseline covariates. To obtain standardized, treatment-specific risks for time points between baseline and the end of follow-up, we standardized the model-derived estimated values to the joint distribution of the baseline covariates. To estimate the mean HR commonly reported in randomized clinical trials, we fit a Cox proportional hazards model with treatment strategy as the only covariate, as described elsewhere.36 The final models are described in eAppendix 3 and eAppendix 4 in the Supplement.
We conducted several sensitivity analyses to test the robustness of our estimates to different choices of functional forms and grace periods (results were similar; see eAppendix 5 and eAppendix 6 in the Supplement). We computed 95% CIs via a nonparametric bootstrap based on 500 resamplings. We used SAS, version 9.4 (SAS Institute Inc), for data processing, and R, version 3.4.4 (The R Foundation), for data analyses. The SEER-Medicare data were from those released in 2015 (containing content through December 31, 2013), and our analyses were conducted from January 2018 to March 2019.
We identified 9549 eligible patients for the fluorouracil trial and 940 for the erlotinib trial, as described in eFigure 1 and eFigure 2 in the Supplement, respectively.
The baseline characteristics of the eligible individuals are given eTable 3 in the Supplement. Compared with 3293 individuals in the existing randomized clinical trial,14 9549 eligible individuals included in the present analyses were more likely to have colon cancer (8565 [90%] vs 2291 [71%]) and were older (median [interquartile range] age, 79 [73-84] years vs 63 [56-68] years) (eTable 4 in the Supplement).
Of the 9549 eligible individuals included in the present analysis (23 447 person-years of follow-up), 204 initiated fluorouracil-based chemotherapy within 3 months of their hospital discharge. Fluorouracil initiation was more likely for people who were younger, married, and had a T4 tumor category at diagnosis and rectum involvement (eAppendix 3 in the Supplement). Individuals were less likely to initiate fluorouracil-based chemotherapy if they had a prolonged hospitalization after surgery, preoperative radiotherapy, and, in the year before diagnosis, anemia and asthenia. By the end of the grace period, 185 individuals remained in the fluorouracil group and 6150 in the observation group (eTable 3 in the Supplement).
There were 2148 deaths during follow-up. The adjusted estimated 5-year survival was 66.6% for fluorouracil and 62.7% for no fluorouracil; the 5-year risk difference was −3.8% (95% CI, −14.8% to 12.6%) (Figure 1). The mortality HR for fluorouracil vs no fluorouracil was 1.00 (95% CI, 0.89-1.12) without any adjustment, 1.01 (95% CI, 0.97-1.05) after adjustment for baseline covariates, and 0.95 (95% CI, 0.85-1.04) after adjustment for baseline and postbaseline covariates.
The baseline characteristics of the eligible individuals are given in eTable 5 in the Supplement. Compared with 569 individuals in the existing randomized clinical trial,15 940 individuals included in the present analysis were older (median [range] age, 74 [66-93] years vs 64 [36-92] years), more likely to be male (547 [58%] vs 298 [52%]), and less likely to have undergone prior chemotherapy (53 [6%] vs 45 [8%]). In addition, all participants in the existing trial had a good performance status at enrollment, whereas 82 of the eligible individuals in our study (9%) had a poor performance status20,21 (eTable 6 in the Supplement).
Of the 940 eligible individuals (412 person-years of follow-up), 62 initiated erlotinib within 12 weeks of their initial gemcitabine dose. Erlotinib initiation was more likely for individuals with stage IV cancer and for those with comorbidities, and less likely for those with a poor performance status and older age (eAppendix 4 in the Supplement). By the end of the grace period, 44 individuals remained with the gemcitabine plus erlotinib strategy and 480 remained with the gemcitabine alone strategy (eTable 5 in the Supplement).
There were 659 deaths during follow-up. The adjusted estimated 1-year survival was 15.6% for gemcitabine plus erlotinib and 20.4% in gemcitabine alone; the risk difference was 4.7% (95% CI, −9.4% to 18.0%) (Figure 2). The all-cause mortality HR for gemcitabine plus erlotinib vs gemcitabine alone was 1.08 (95% CI, 0.93-1.25) without any adjustment, 1.03 (95% CI, 0.96-1.09) after adjustment for baseline covariates, and 1.04 (95% CI, 0.86-1.42) after adjustment for baseline and postbaseline covariates.
For comparison purposes, we conducted an analysis without cloning, censoring by deviation from protocol, or inverse probability weighting. Eligible individuals for the fluorouracil comparison were assigned to the fluorouracil group if they started fluorouracil therapy at any time during the follow-up and to the no fluorouracil group otherwise. We then fit a Cox proportional hazards regression model with treatment group and the baseline covariates. The mortality HR estimate was 1.14 (95% CI, 0.95-1.36). In a similar analysis for the erlotinib comparison, the mortality HR estimate was 0.68 (95% CI, 0.54-0.87).
We described 2 target trials for the addition of a cancer drug to an existing treatment regimen. These target trials were based on 2 existing randomized clinical trials: 1 pragmatic trial (fluorouracil) and 1 placebo-controlled, double-blind trial (erlotinib).14,15 We emulated these target trials using observational data from individuals 65 years of age or older whose data were in the SEER-Medicare database. Our observational estimates were consistent with the null estimates reported by the existing trials when we explicitly emulated the target trials but not when we conducted naive analyses that did not attempt to emulate a target trial.
The first emulation yielded a mortality HR of 0.95 (95% CI, 0.85-1.04) for fluorouracil vs no fluorouracil initiation, which is compatible with that reported in the actual fluorouracil trial14 for individuals 70 years of age or older (HR, 1.02; 95% CI, 0.70-1.48). Five-year survival could not be directly compared because the actual trial did not report these numbers for the elderly individuals. The second emulation yielded a mortality HR of 1.04 (95% CI, 0.86-1.42) for erlotinib vs no erlotinib initiation after gemcitabine, which is compatible with that reported in the existing erlotinib trial15 for individuals 65 years of age or older (HR, 0.96; 95% CI, 0.74-1.24). The estimated 1-year survival in the erlotinib strategy was approximately 16% in the emulated trial and 23% in the existing trial; the estimated 1-year survival in the gemcitabine alone strategy was approximately 20% in the emulated trial and 17% in the existing trial.
The target trial framework requires data sources with adequate longitudinal information on treatments, outcomes, and confounders. We were able to obtain that information from the linkage of detailed cancer information from the SEER registries with administrative data from Medicare claims. Detailed timing information for comorbidity onset and treatment administration exists solely in Medicare; thus, using SEER only would preclude us from emulating target trials. The use of SEER registry data alone may have contributed to erroneous conclusions in recent reports (later retracted)37,38 that compared cancer treatments in the elderly population. The use of Medicare data enabled us to reduce some errors present in SEER alone (eg, in the radiation variable) but does not guarantee perfect coding reliability of all variables (eg, rare histologic results).39
Similar to all observational analyses, ours assume that the lack of randomized treatment assignment can be approximately replaced by adjustment for the measured confounders. In our analyses, after the target trial was specified and the emulation procedures followed, we found no indication of strong confounding by measured variables. In fact, adjustment for multiple prognostic factors only modestly changed the estimates in both emulations. Given that we adjusted for some of the most important indications for the studied treatments, it is likely that confounding was not the most important validity threat in our examples.
Leaving aside a lack of randomization, a major threat to the validity of observational comparative effectiveness analyses is a failure to emulate components of a target trial other than randomization, which may introduce biases such as selection bias and immortal time bias. Explicit emulation of a target trial helps eliminate these problems.6,23
Similar to our naive analyses, previous uses of SEER-Medicare data to estimate treatment effects that did not explicitly attempt to emulate a target trial may have introduced immortal time bias.40 For the same reasons, prior comparisons of randomized clinical trials and observational studies based on SEER-Medicare41 or other databases42 are difficult to interpret because, in the absence of an explicit emulation of a target trial, any discrepancies may be due to incorrect analysis rather than to inherent limitations of the observational data.
The use of large observational databases does not guarantee transportable or precise estimates. The requirements of target trial emulation (eg, requiring individuals to participate in Medicare Part D, which was necessary in the erlotinib analysis) may limit the generalizability of our results, although probably not as much as do the eligibility criteria of actual randomized clinical trials. Perhaps the most surprising aspect of our analyses was the small sample sizes despite the apparently large amount of data in the SEER-Medicare database. Because few eligible individuals received the adjuvant therapy of interest, there were fewer than 200 individuals in the fluorouracil strategy after 3 months of follow-up and fewer than 190 individuals in the erlotinib strategy after 12 weeks of follow-up. When, as in our analyses, few individuals follow the treatment strategies of interest, the researchers’ ability to adequately control for confounding and selection bias is limited because not many covariates can be added to the regression models.
We emulated 2 target trials for the addition of a cancer treatment by using the SEER-Medicare database. Our findings showed that, in some settings, observational estimates from this database are consistent with those from randomized clinical trials in elderly populations, but only when an appropriate causal inference approach is implemented.
As the US population ages, understanding how cancer treatments affect elderly individuals will have profound implications for clinical decision-making. Because elderly individuals tend to be underrepresented in randomized clinical trials,2,3 observational health care databases can be used to obtain more precise estimates in that segment of the population or, at the very least, to guide the design of future randomized clinical trials by helping to rule out clearly ineffective or harmful treatment strategies.
Accepted for Publication: January 8, 2020.
Published: March 5, 2020. doi:10.1001/jamanetworkopen.2020.0452
Correction: This article was corrected on April 6, 2020, to remove data from 1 sentence in the Results and to remove or replace data in the Supplement in violation of the SEER-Medicare data use agreement.
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Petito LC et al. JAMA Network Open.
Corresponding Author: Lucia C. Petito, PhD, Feinberg School of Medicine, Division of Biostatistics, Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Dr, Ste 1400, Chicago, IL 60611 (firstname.lastname@example.org).
Author Contributions: Dr Petito had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Petito, García-Albéniz, Howlader, Mariotto, Dahabreh, Hernán.
Acquisition, analysis, or interpretation of data: Petito, Logan, Hernán.
Drafting of the manuscript: Petito, Logan, Hernán.
Critical revision of the manuscript for important intellectual content: Petito, García-Albéniz, Howlader, Mariotto, Dahabreh, Hernán.
Statistical analysis: Petito, Logan, Dahabreh, Hernán.
Obtained funding: Howlader, Hernán.
Administrative, technical, or material support: Logan, Howlader, Mariotto, Hernán.
Supervision: García-Albéniz, Hernán.
Conflict of Interest Disclosures: Dr Petito reported that she is currently funded by the following National Institutes of Health grants as a coinvestigator: 5R01HL133860-04 (National Heart, Lung, and Blood Institute [NHLBI]), 5R01HL096875-08 (NHLBI), 4R33AG057383-03 (National Institute on Aging [NIA]), 5R01MD012236-02 (National Institute on Minority Health and Health Disparities), 1R01AG059291-01A1 (NIA), 1R01HL141881-01A1 (NHLBI), 2UL1TR001422-05A1 (National Center for Advancing Translational Sciences). Drs Logan and Hernán reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Dahabreh reported receiving personal fees from Sanofi Pasteur outside the submitted work.
Funding/Support: This work was supported by the US National Cancer Institute contract HHSN261201700047 and grant P01-CA134294 (to Drs Petito, García-Albéniz, Logan, and Hernán), and by Patient-Centered Outcomes Research Institute awards ME-1306-03758 and ME-1502-27794 (to Dr Dahabreh). The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; by the Surveillance, Epidemiology and End Results Program of the National Cancer Institute under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and by the National Program of Cancer Registries from the Centers for Disease Control and Prevention under agreement U58DP003862-01 awarded to the California Department of Public Health.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.
Disclaimer: This study used the linked Surveillance, Epidemiology, and End Results (SEER)–Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The ideas and opinions expressed herein are those of the authors and endorsement by the California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred. Statements in this article do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors, or the Methodology Committee.
Additional Contributions: We acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information Centers for Medicare and Medicaid Services; Information Management Services Inc; and the SEER Program tumor registries in the creation of the SEER-Medicare database.
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