Learn how DCEG applies absolute risk modeling to develop tools to aid clinicians and their patients.
Jump to NCI risk assessment tools for clinicians and individuals:Biostatistics Branch investigators develop statistical and computational tools for epidemiologic and laboratory studies, and distribute those tools to collaborators and the greater scientific community.
A panel of easy-to-interpret estimable APC functions and corresponding Wald tests in R code that can be accessed through a user-friendly web tool.
BCRA is an R package that projects absolute risk of invasive breast cancer according to NCI’s Breast Cancer Risk Assessment Tool (BCRAT) algorithm for specified race/ethnic groups and age intervals.
A SAS macro (commonly referred to as the Gail Model) that projects absolute risk of invasive breast cancer according to NCI’s Breast Cancer Risk Assessment Tool (BCRAT) algorithm for specified race/ethnic groups and age intervals.
A SAS macro that projects absolute invasive breast cancer risk for white women based on measurements of mammographic density and other risk factors.
Software that projects absolute risk for breast, endometrial, and ovarian cancer in Caucasian and African American women.
BLM is an R package for estimating absolute risk and risk differences from cohort data with a binomial linear or LEXPIT regression model.
Software that projects absolute breast cancer risk over defined age intervals for Asian and Pacific Islander American women with specific risk factors.
The CARE Model is a SAS macro that allows researchers to estimate an African American woman's risk of developing invasive breast cancer over specified age intervals.
An executable file (in GAUSS) that projects absolute colon cancer risk (with confidence intervals) according to NCI’s Colorectal Cancer Risk Assessment Tool (CCRAT) algorithm. GAUSS is not needed to run the program.
A SAS macro that projects absolute risk of colon cancer according to NCI’s Colorectal Cancer Risk Assessment Tool (CCRAT) algorithm.
In both the absence and presence of screening, the R package lcrisks, calculates individual risks of lung cancer and lung cancer death based on covariates: age, education, sex, race, smoking intensity/duration/quit-years, Body Mass Index, family history of lung-cancer, and self-reported emphysema. In the presence of CT screening akin to the NLST (3 yearly screens, 5 years of follow-up), it uses the covariates to estimate risk of false-positive CT screen as well as the reduction in risk of lung cancer death and increase in risk of lung cancer screening.
The R package thyroid implements a risk prediction model developed by NCI researchers to calculate the absolute risk of developing a second primary thyroid cancer (SPTC) in individuals who were diagnosed with a cancer during their childhood.
A powerful gene-based test via variable selection for genome-wide association studies.
A panel of easy-to-interpret estimable APC functions and corresponding Wald tests in R code that can be accessed through a user-friendly web tool.
ARTP2 is an R package of biological pathway analysis or pathway meta-analysis for genome-wide association studies (GWAS). It also provides tools for gene-level test as a special case. ARTP2 is an enhanced version of two previously released packages ARTP and AdaJoint.
A subset-based approach improves power and interpretation for combined analysis of genetic assocation studies of heterogeneous traits.
Bayesian model for Detecting Gene Environment interaction
BSR (Bayesian Subset Regression) is an R package that implements the Bayesian subset modeling procedure for high-dimensional generalized linear models.
An R package for testing Calibration of Binary Risk Model (CBRM) using different goodness-of-fit statistics
CGEN (Case-control.Genetics) is an R package for analyzing genetic data on case-control samples, with particular emphasis on novel methods for detecting Gene-Gene and Gene-Environment interactions.
CNVfam is a software package for jointly detecting copy number variations (CNV) in nuclear families genotyped using the Illumina platform.
CompareTests is an R package to estimate agreement and diagnostic accuracy statistics for two diagnostic tests when one is conducted on only a subsample of specimens. A standard test is observed on all specimens.
Software package designed to perform a range of association tests between sets of SNPs and a phenotype.
This is a R package for rapid evaluation of extremely small p-value for resampling-based test (EXPERT).
This is the R package implementing the testing procedure described in the referred manuscript
The iCARE R Package allows researchers to quickly build models for absolute risk, and apply them to estimate an individual's risk of developing disease during a specifed time interval, based on a set of user defined input parameters.
IN.power is an R package for estimating the number of susceptibility SNPs and power of future studies.
Different approaches for handling varied error structures in studies of irradiated populations
MultiAssoc is a MATLAB software package for test of association of a disease with a group of SNPs after accounting for their interaction with another group of SNPs or environmental exposures.
NestedCohort is an R software package for fitting Kaplan-Meier and Cox Models to estimate standardized survival and attributable risks for studies where covariates of interest are observed on only a sample of the cohort.
SegCNV is a software package, implemented in C++, to detect germline copy number variations in SNP array data.
TREAT is an R package for detecting complex joint effects in case-control studies. The test statistic is derived from a tree-structure model by recursive partitioning the data. Ultra-fast algorithm is designed to evaluate the significance of association between candidate gene and disease outcome
POWER V3.0 Software is used for computing sample size and power for binary outcome studies.
PGA is a software package containing algorithms and graphical user interfaces developed in Matlab for power and sample size calculation under various genetic models and statistical constraints.