Metadata supporting data files of the related article: Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics
Creators: name: Blair, Cindy K.
name: Wiggins, Charles L.
name: Nibbe, Andrea M.
name: Storlie, Curt B.
name: Prossnitz, Eric R.
name: Royce, Melanie
name: Lomo, Lesley C.
name: Hill, Deirdre A.
Publisher: name: figshare
Year: 2019
Subjects: subject: Cancer
subject: Medicine
subject: 111202 Cancer Diagnosis
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: Biomarkers

description: In this large population-based cohort study, the authors evaluated obesity as a contributor to breast cancer survival according to tumor molecular subtypes, using data from the Surveillance Epidemiology and End Results (SEER) program.<br> <b>Data access: </b>Data file<b> sas7bdat.combined3aa</b> (containing aggregated patient data) supporting all the figures, tables and supplementary files in the published article, is not publicly available to protect patient privacy, but will be made available on request from the corresponding author Dr. Deirdre A. Hill, email address: dahill@salud.unm.edu, upon institutional review board approval. Release of vital status data is governed by the New Mexico state law, and therefore these data are not currently available.<br> <b>Patient consent: </b> A Health Insurance Portability and Accountability Act (HIPAA) waiver of consent was obtained for previously collected data. All study procedures were approved by the University of New Mexico Health Sciences Center institutional review board.<b><br> <br> </b> <b>Study aims and methodology: </b> The aim of the study was to determine whether obese women were more likely to be diagnosed with poor prognosis tumor characteristics, and quantify the contribution of obesity to survival. Hazard ratios (HR), and 95% confidence intervals (CI) were calculated via Cox multivariate models. Analyses were conducted using SAS (version 9.4; Cary, N.C.) and R (v.3.4.3, Vienna, Austria). Final multiple imputation estimates were produced using SAS Proc MIanalyze (20 imputations). Kaplan-Meier plots were produced in R software. A two-sided test of statistical significance was defined as p&lt;0.05. The effect of obesity on survival was evaluated among 859 incident breast cancers (subcohort; 15% random sample; median survival 7.8 years) and 697 deaths from breast cancer (cases; 100% sample). The authors investigated the role of obesity and associated mechanisms on breast cancer-specific death. Firstly, they examined whether higher BMI was associated with more aggressive tumor characteristics. The authors next investigated the relationship between higher BMI and breast cancer survival, with consideration of the possibility that tumor characteristics were on the causal pathway, often termed "mediators". The BMI-mortality relationship was evaluated according to breast cancer subtypes, as results from previous studies have suggested that the effect of BMI may be stronger in luminal disease. Study participants were Hispanic Hispanic white and non-Hispanic white women diagnosed with invasive breast cancer between 1997 and 2009 in six New Mexico counties (representing 50% of the New Mexico population). Breast cancer cases were identified through the New Mexico Tumor Registry, a founding member of the Surveillance, Epidemiology, and End Results (SEER) program. For more details on the multiple imputation methods and statistical analyses, see the related published article.<br><b>Dataset description: </b>Dataset<b> </b><b>sas7bdat.combined3aa </b>is in a SAS (v 9.4) file format and supports figure 2, tables 1, 2 and 3 and supplementary table 1 in the published article. The data file consists of raw patient data that are coded in categories to protect patient confidentiality. The file contains: a) characteristics of women diagnosed with incident invasive breast cancer in six New Mexico countries, b) data on overweight and obese patients in relation to tumor characteristics, Charlson comorbidity index and treatment, c) body mass in relation to breast cancer-specific mortality by tumor subtype, d) breast cancer-specific survival by BMI categories for Luminal A-like and Luminal B-like tumor subtypes.<br><b> </b><b>Software needed to access the dataset: sas7bdat.combined3aa</b> is a SAS analysis file and it requires the SAS software to be accessed. <b> </b>