bc_data: Data for examples

In wheelerb/iCARE: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE)

Description

Example data for computeAbsoluteRisk, computeAbsoluteRiskSplitInterval, ModelValidation, and plotModelValidation.

Details

• bc_model_cov_info: a main list containing information on family history and parity; information on each risk factor is given as a list

• bc_15_snps: contains published SNP information from reference (2)

• bc_model_log_or: vector of log-odds ratios of family history and parity

• bc_model_log_or_post_50: vector of log-odds ratios of family history and parity for women 50 years or older

• ref_cov_dat: contains a subsample of data imputed using reference (4) and (5)

• ref_cov_dat_post_50: contains a subsample of the data on family history and parity, to be used as reference dataset for women 50 years or older

• bc_inc: contains age-specific incidence rates of breast cancer from reference (3)

• mort_inc: contains age-specific incidence rates of all-cause mortality from reference (1) below

• new_cov_prof: Information on family history and parity for three women (given for illustration of absolute risk prediction)

• new_snp_prof: Information on 15 breast cancer associated SNPs for three women (given for illustration of absolute risk prediction)

• validation.cohort.data: Simulated full cohort dataset of 50,000 women for illustration of model validation. The variables are:

  • id: Subject id

  • famhist: Family history; binary indicator of presence/absence of first degree relatives

  • parity: Number of child births

  • study.entry.age: age of study entry

  • study.exit.age: age of study exit

  • observed.outcome: binary indicator of diease status (yes/no)

  • time.of.onset: time (in years) since study entry to the development of disease; for subjects who have not developed disease beyond the observed followup, it is set to Inf observed.followup: number of years the subject is followed up in the study (difference between the age of study exit and age of study entry)

• validation.nested.case.control.data: A simulated example of a case-control study of 2694 women, nested within the full cohort. In addition to the variables given above, it has information on the 15 breast cancer associated SNPs with variable names being the rs-identifiers.

References

(1) Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). Underlying Cause of Death 1999-2011 on CDC WONDER Online Database, released 2014. Data are from the Multiple Cause of Death Files, 1999-2011, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
Accessed at http://wonder.cdc.gov/ucd-icd10.html on Aug 26, 2014.

(2) Michailidou K, Beesley J, Lindstrom S, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nature genetics 2015;47:373-80.

(3) Surveillance, Epidemiology, and End Results (SEER) Program SEER*Stat Database: Incidence - SEER 18 Regs Research Data, Nov 2011 Sub, Vintage 2009 Pops (2000-2009) <Katrina/Rita Population Adjustment> - Linked To County Attributes - Total U.S., 1969-2010 Counties. In: National Cancer Institute D, Surveillance Research Program, Surveillance Systems Branch, ed. SEER18 ed.

(4) 2010 National Health Interview Survey (NHIS) Public Use Data Release, NHIS Survey Description. 2011.
Accessed at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2010/srvydesc.pdf.)

(5) Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Questionnaire. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2010.

Examples

 temp <- data(bc_data, package="iCARE")

 # Display the object names
 temp

wheelerb/iCARE documentation built on May 17, 2019, 2:02 p.m.