Calculates the z or z(log) values for laboratory measurement standardisation
as proposed in Hoffmann 2017 et al. for a complete data.frame.
data.frame, with the columns: "age", numeric, "sex", factor
and more user defined numeric columns that should be z/z(log) transformed.
data.frame, reference table, has to have the columns:
"age", numeric (same units as in age, e.g. days or years, age of 0
matches all ages),
"sex", factor (same levels for male and female as sex and a special level
"both"), "param", character with the laboratory parameter name that have
to match the column name in x, "lower" and "upper", numeric for the
lower and upper reference limits.
numeric, probabilities of the lower and upper reference limit,
default: c(0.025, 0.975) (spanning 95 %). Has to be of length 2 for
numeric or a two-column matrix with as many rows as elements in x.
logical, should z (log = FALSE, default) or
z(log) (log = TRUE) calculated?
data.frame, with the columns: "age", "sex" and all numeric
columns z/zlog transformed. If a column name is missing in limits$param
a warning is thrown and the column is set to NA.
This is a wrapper function for z() and lookup_limits(). Please find
the details for the z/z(log) calculation at z().
zlog_df is an alias for z_df(..., log = TRUE).
Georg Hoffmann, Frank Klawonn, Ralf Lichtinghagen, and Matthias Orth. 2017. "The Zlog-Value as Basis for the Standardization of Laboratory Results." LaboratoriumsMedizin 41 (1): 23–32. doi:10.1515/labmed-2016-0087 .
l <- data.frame(
param = c("alb", "bili"),
age = c(0, 0),
sex = c("both", "both"),
units = c("mg/l", "µmol/l"),
lower = c(35, 2),
upper = c(52, 21)
)
x <- data.frame(
age = 40:48,
sex = rep(c("female", "male"), c(5, 4)),
# from Hoffmann et al. 2017
alb = c(42, 34, 38, 43, 50, 42, 27, 31, 24),
bili = c(11, 9, 2, 5, 22, 42, 37, 200, 20)
)
z_df(x, l)
#> age sex alb bili
#> 1 40 female -0.345876 -0.103156
#> 2 41 female -2.190548 -0.515780
#> 3 42 female -1.268212 -1.959964
#> 4 43 female -0.115292 -1.341028
#> 5 44 female 1.498796 2.166276
#> 6 45 male -0.345876 6.292516
#> 7 46 male -3.804636 5.260956
#> 8 47 male -2.882300 38.889812
#> 9 48 male -4.496388 1.753652
zlog_df(x, l)
#> age sex alb bili
#> 1 40 female -0.15472223 0.8819855
#> 2 41 female -2.24698167 0.5474516
#> 3 42 female -1.14569028 -1.9599640
#> 4 43 female 0.07826303 -0.4324351
#> 5 44 female 1.57162335 2.0375165
#> 6 45 male -0.15472223 3.1154950
#> 7 46 male -4.52949253 2.9041899
#> 8 47 male -3.16160843 5.7172179
#> 9 48 male -5.69571148 1.8786269