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. medRxiv. 2022 July 01.
You can install the development version of ALARM
from GitHub using:
remotes::install_github('mattwarkentin/ALARM')
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.
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.