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Chapter 3.2
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Table 4. Structure of the data.frame with the data to be used in R: id is identifi cation of patient, time is time
of the visit in months, age is age at baseline, dosis is dosis of UDCA medication, bili, albu, ap are obtained
values of longitudinal markers.
id time age dosis bili albu ap
1 0.00 52.38 600 0.64 1.22 3.24
1 1.84 52.38 600 0.50 1.14 1.69
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and τ corresponding to visit times of these new patients (given in column time of
the data.frame DataNew) are computed using:
clust ¡- GLMM˙longitDA(mod = list(mod0, mod1),
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w.prior = c(0.8, 0.2),
y = DataNew[, c(”bili”, ”albu”, ”lap”)],
id = DataNew[, ”id”],
time = DataNew[, ”time”],
xz.common = TRUE,
x = list(bili = DataNew[, c(”age”, ”dosis”)],
albu = DataNew[, c(”age”, ”dosis”)],
lap = DataNew[, c(”age”, ”dosis”)]),
z = list(bili = DataNew[, ”time”],
albu = DataNew[, ”time”],
lap = DataNew[, ”time”]))
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1 119.69 52.38 1200 NA 1.26 1.25
2 0.00 52.72 600 4.14 1.19 9.91
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Note that for each patient and each time τ when Pg,i(τ) is computed, the above
mentioned code considers the whole history of a particular patient by time τ by
checking the variables id and time. The resulting object clust is a list with
components named ident (identification information), marg, cond, ranef (matrices
with G columns containing computed values of Pg,i for the three discrimination
approaches described in this paper). For more details, see documentation of the
package mixAK.
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