rfs.predict {nhs.predict} | R Documentation |
rfs.predict
Description
Calculates 'NHS Predict' v2.1 Recurrence-free survival and therapy benefits
Usage
rfs.predict(
data,
year = 10,
age.start,
screen,
size,
grade,
nodes,
er,
her2,
ki67,
generation,
horm,
traz,
bis
)
Arguments
data |
A dataframe containing patient data with the necessary variables. |
year |
Numeric, Specify the year since surgery for which the predictions are calculated, ranges between 1 and 15. Default at 10. |
age.start |
Numeric, Age at diagnosis of the patient. Range between 25 and 85. |
screen |
Numeric, Clinically detected = 0, Screen detected = 1, Unknown = 2. |
size |
Numeric, Tumor size in millimeters. |
grade |
Numeric, Tumor grade. Values: 1,2,3. Missing=9. |
nodes |
Numeric, Number of positive nodes. |
er |
Numeric, ER status, ER+ = 1, ER- = 0. |
her2 |
Numeric, HER2 status, HER2+ = 1, HER2- = 0. Unknown = 9. |
ki67 |
Numeric, ki67 status, KI67+ = 1, KI67- = 0, Unknown = 9. |
generation |
Numeric, Chemotherapy generation. Values: 0,2,3. If value is missing, default=3. |
horm |
Numeric, Hormone therapy, Yes = 1, No = 0. If value is missing, default= er status. |
traz |
Numeric, Trastuzumab therapy, Yes = 1, No = 0. If value is missing, default= her2 status. |
bis |
Numeric, Bisphosphonate therapy, Yes = 1, No = 0. if value is missing, default=1. |
Value
The function attaches additional columns to the dataframe, matched for patient observation, containing recurrence-free survival at the specified year, plus the additional benefit for each type of therapy.
Examples
data(example_data)
example_data <- rfs.predict(example_data,age.start = age,screen = detection,size = t.size,
grade = t.grade, nodes = nodes, er = er.status, her2 = her2.status,
ki67 = ki67.status, generation = chemo.gen, horm = horm.t,
traz = trastuzumab, bis = bis.t)
data(example_data)
example_data <- rfs.predict(example_data,year = 15, age,detection,t.size,t.grade,
nodes,er.status,her2.status,ki67.status,chemo.gen,horm.t,
trastuzumab,bis.t)