- Implemented functions for various model risk predictions.
- Microsimulation framework.
- Metastasis extension.
devtools::install_github("rickstra/BCNatHist/BCNatHist", build = TRUE)
or
install.packages("https://github.com/rickstra/BCNatHist/raw/main/BCNatHist_1.0.tar.gz", repos = NULL)
foreach, doParallel, itertools, Rcpp, RcppArmadillo.
Start by defining a natural history model by calling DefineBCModel. Each of the four submodels (onset, growth, sympt, sens) can be specified with a formula, e.g. onset = ~ x1 + x2. Make sure that the variable names exist in the intended data set.
model <- DefineBCModel(onset = ~ x1 + x2, sens = ~ x3)
Once defined, the model can be fitted to the data using maximum likelihood by calling EstimateBCModel. The function returns the model object updated with the fitted parameter values and the estimated covariance matrix (by default).
model <- EstimateBCModel(model, data)
The estimated parameter values can then be summarized, including Wald confidence intervals based on the estimated covariance matrix.
summary(model)
If the parameter values are already known or otherwise to be fixed as par_values, they can be manually added to the model by overwriting the model$par variable, or added when defining the model using DefineBCmodel. When overriding parameter values, also change the model$fitted variable to TRUE.
model$par <- par_values
model$fitted <- TRUE
# or
model <- DefineBCModel(start.par = par_values)
model$fitted <- TRUE
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[2]
Strandberg R, Czene K, Eriksson M, Hall P, Humphreys K.
Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort.
Cancer Epidemiol Biomarkers Prev (2022) 31(3):569–577.
doi: 10.1158/1055-9965.EPI-21-1011.