Estimates of Overall Survival in Patients With Cancer Receiving Different Treatment Regimens

Abstract

IMPORTANCEThe 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. OBJECTIVE 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. EXPOSURES 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. RESULTS 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.

Evaluation of Survival Outcomes of Endovascular Versus Open Aortic Repair for Abdominal Aortic Aneurysms with a Big Data Approach

“…For treating rAAA, there is a lack of real clinical trials. As such, similar to in some literature [ 19 ], we need to design a hypothetical target trial. The following design has been motivated by relevant observational studies [ 6 , 10 , 11 , 12 , 13 ] and is clinically well grounded.…”

“…Here we note that, with extensive examinations and comparisons, no approach has been observed to dominate others—it is expected that such an approach may not exist, and different approaches have different pros and cons. In this article, we adopt the emulation approach, which is relatively new but has already been examined in many publications [ 17 , 18 , 19 ]. With this approach, a clinical trial is explicitly designed and assembled using observational data, and statistical analysis approaches designed for clinical trials can be then adopted, bearing the potential of drawing causal conclusions.…”

“…Compared to for example hospital- and community-based data, Medicare data is advantageous with its unbiased sample selection and relatively uniform and detailed data collection. It has served as the basis of a large number of clinical and public health studies, including those that adopt causal inference analysis techniques [ 19 , 20 ]. More details on the analyzed Medicare data are provided below in Section 2.1 .…”

“…The second big data perspective is that in the analysis of the emulated trial, deep learning techniques are adopted. In “standard” emulation analysis (as well as most if not all analysis of real clinical trials) [ 18 , 19 ], regression (e.g., logistic and Cox) techniques have been adopted. For diverse fields including engineering, business, social science, and others [ 21 , 22 ], the superiority of deep learning techniques in prediction has been well established through a myriad of published studies.…”

“…Specifically, this is the first application of deep learning to the emulation analysis and study of rAAA. Building on the existing deep learning components, we assemble an analysis pipeline that mimics the “propensity score + inverse probability treatment (IPT) weighting Cox regression” approach [ 18 , 19 ].…”