ln_quantile_value.RdThe function ln_quantile_value calculates lab values at a specified quantile, using the default units for that lab. The function ln_patients_quantile_value does the same calculation for a specific group of patients.
Default units for a lab can be obtained using ln_lab_default_units.
If no quantile data is available for a particular lab, age, and sex, the function returns 'NA'.
It should be noted that the values of extreme quantiles (e.g. >0.95 or <0.05 on low resolution, >0.99 or <0.01 on high resolution) may not be reliable, as they may represent outliers in the data.
Note that ln_quantile_value returns values for all combinations of age, sex, and lab, while ln_patients_quantile_value returns values for a specific set of patients, similar to ln_normalize.
ln_quantile_value(
quantiles,
age,
sex,
lab,
reference = "Clalit",
allow_edge_quantiles = FALSE
)
ln_patients_quantile_value(
quantiles,
age,
sex,
lab,
reference = "Clalit",
allow_edge_quantiles = FALSE
)a vector of quantiles (in the range 0-1) to compute the lab value for, or a vector with a quantile for each patient when running ln_patients_quantile_value.
a vector of ages to compute the lab values for or a vector with an age for each patient when running ln_patients_quantile_value. Note that the age should be in years, and would be floored to the nearest integer.
the sexes to compute the lab values for, or a vector with a sex for each patient when running ln_patients_quantile_value. Note that for ln_quantile_value this parameter can only be either: "male", "female" or c("male", "female")
The lab name.
the reference distribution to use. Can be either "Clalit" or "UKBB" or "Clalit-demo". Please download the Clalit and UKBB reference distributions using ln_download_data().
If TRUE (default) then the function will return the value for the edge quantiles (<0.01 or >0.99) even though they are not reliable. If FALSE then the function will return NA for those quantiles. Note that for the "Clalit-demo" reference, the threshold would be <0.05 or >0.95.
ln_quantile_value returns a data frame which contains the values for each combination of quantile, age and sex.
The data frame has the the following columns:
age: age in years
sex: "male" or "female"
quantile: he quantile
value: the lab value
unit: the lab unit
lab: the lab name
ln_patients_quantile_value returns a vector of value per patient.
# \donttest{
ln_quantile_value(c(0.05, 0.5, 0.95), 50, "male", "WBC")
#> age sex quantile value unit lab
#> 1 50 male 0.05 4.584943 x10E3/uL WBC
#> 2 50 male 0.50 7.182971 x10E3/uL WBC
#> 3 50 male 0.95 11.789079 x10E3/uL WBC
ln_quantile_value(
c(0, 0.05, 0.1, 0.4, 0.5, 0.6, 0.9, 1),
c(50, 60),
c("male", "female"),
"Glucose"
)
#> age sex quantile value unit lab
#> 1 50 male 0.00 NA mg/dL Glucose
#> 2 50 male 0.05 76.63210 mg/dL Glucose
#> 3 50 male 0.10 80.92743 mg/dL Glucose
#> 4 50 male 0.40 90.84690 mg/dL Glucose
#> 5 50 male 0.50 92.98665 mg/dL Glucose
#> 6 50 male 0.60 95.83511 mg/dL Glucose
#> 7 50 male 0.90 111.93415 mg/dL Glucose
#> 8 50 male 1.00 NA mg/dL Glucose
#> 9 60 male 0.00 NA mg/dL Glucose
#> 10 60 male 0.05 77.98954 mg/dL Glucose
#> 11 60 male 0.10 81.92832 mg/dL Glucose
#> 12 60 male 0.40 92.60682 mg/dL Glucose
#> 13 60 male 0.50 94.97742 mg/dL Glucose
#> 14 60 male 0.60 97.86284 mg/dL Glucose
#> 15 60 male 0.90 113.95915 mg/dL Glucose
#> 16 60 male 1.00 NA mg/dL Glucose
#> 17 50 female 0.00 NA mg/dL Glucose
#> 18 50 female 0.05 75.29256 mg/dL Glucose
#> 19 50 female 0.10 78.77304 mg/dL Glucose
#> 20 50 female 0.40 87.97072 mg/dL Glucose
#> 21 50 female 0.50 90.91937 mg/dL Glucose
#> 22 50 female 0.60 92.91284 mg/dL Glucose
#> 23 50 female 0.90 106.98740 mg/dL Glucose
#> 24 50 female 1.00 NA mg/dL Glucose
#> 25 60 female 0.00 NA mg/dL Glucose
#> 26 60 female 0.05 77.70802 mg/dL Glucose
#> 27 60 female 0.10 80.54148 mg/dL Glucose
#> 28 60 female 0.40 90.41370 mg/dL Glucose
#> 29 60 female 0.50 92.59323 mg/dL Glucose
#> 30 60 female 0.60 95.86970 mg/dL Glucose
#> 31 60 female 0.90 110.90176 mg/dL Glucose
#> 32 60 female 1.00 NA mg/dL Glucose
# }
# on the demo data
# \dontshow{
ln_quantile_value(c(0.05, 0.5, 0.95), 50, "male", "WBC", reference = "Clalit-demo")
#> age sex quantile value unit lab
#> 1 50 male 0.05 4.584943 x10E3/uL WBC
#> 2 50 male 0.50 7.182971 x10E3/uL WBC
#> 3 50 male 0.95 11.789079 x10E3/uL WBC
# }
# \donttest{
hemoglobin_data$quantile <- ln_normalize(
hemoglobin_data$value,
hemoglobin_data$age,
hemoglobin_data$sex,
"Hemoglobin"
)
hemoglobin_data$value1 <- ln_patients_quantile_value(
hemoglobin_data$quantile,
hemoglobin_data$age,
hemoglobin_data$sex,
"Hemoglobin"
)
head(hemoglobin_data)
#> age sex value quantile value1
#> 1 20 male 9.39 0.01882213 9.39
#> 2 20 male 14.03 0.18674720 14.03
#> 3 20 male 14.44 0.27947363 14.44
#> 4 20 male 15.80 0.75195053 15.80
#> 5 20 female 12.06 0.24249167 12.06
#> 6 20 female 12.89 0.57451617 12.89
# }
# \dontshow{
hemoglobin_data$quantile <- ln_normalize(
hemoglobin_data$value,
hemoglobin_data$age,
hemoglobin_data$sex,
"Hemoglobin",
reference = "Clalit-demo"
)
hemoglobin_data$value1 <- ln_patients_quantile_value(
hemoglobin_data$quantile,
hemoglobin_data$age,
hemoglobin_data$sex,
"Hemoglobin",
reference = "Clalit-demo"
)
head(hemoglobin_data)
#> age sex value quantile value1
#> 1 20 male 9.39 0.03838718 NA
#> 2 20 male 14.03 0.18914947 14.03
#> 3 20 male 14.44 0.28589076 14.44
#> 4 20 male 15.80 0.75032395 15.80
#> 5 20 female 12.06 0.24222206 12.06
#> 6 20 female 12.89 0.55270091 12.89
# }