ALARM

The {ALARM} package provides a single primary function, predictALARM() to predict the absolute risk of lung cancer mortality based on a set of covariates and a chosen time horizon.

The models used to make predictions are based on the following study (link):

Warkentin MT, Tammemägi MC, Espin-Garcia O, Budhathoki S, Liu G, Hung RJ. Asian Lung Cancer Absolute Risk Models for lung cancer mortality based on China Kadoorie Biobank. J Natl Cancer Inst. 2022; 114(12):1665-1673 doi: 10.1093/jnci/djac176.

Installation

You can install the development version of ALARM from GitHub using:

remotes::install_github('mattwarkentin/ALARM')

Usage

Once the package has been installed, we can load the package and make predictions for the absolute risk of lung cancer mortality by supplying a data.frame with the requisite covariates and a time horizon to predictALARM().

In this example, we will create some example data for two individuals, one ever-smoker and one never-smoker, with similar covariate values, with the exception of smoking history which are set to missing (NA) for the never-smoker. Please see ?validate_data for more details on the expected format of the input data.

library(ALARM)

data <- data.frame(age = 70, sex = 1, fhx_cancer = 1,
                   phx_cancer = 0, fev1fvc = 70, phx_lungdx = 1,
                   hhinc = 3, bmi = 30, 
                   smk_status = c(1, 2), smk_duration = c(NA, 40), 
                   smk_cigpday = c(NA, 20))

Next, we use the predictALARM() function to estimate the absolute risk of lung cancer mortality for a given time horizon, t (e.g., time = 5).

predictALARM(data, time = 5)
#>   age sex fhx_cancer phx_cancer fev1fvc phx_lungdx hhinc bmi smk_status
#> 1  70   1          1          0      70          1     3  30          1
#> 2  70   1          1          0      70          1     3  30          2
#>   smk_duration smk_cigpday  ALARM_pred
#> 1           NA          NA 0.004494043
#> 2           40          20 0.020674301

predictALARM() returns a data.frame that contains all of the columns from the input data, with the addition of a new column, ALARM_pred, which contains the lung cancer mortality absolute risk estimates at the chosen time horizon and conditional on the subjects’ covariates.

Code of Conduct

Please note that the ALARM project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.