To simulate longitudinal data, we start with a ‘cross-sectional’ data
set and convert it to a time-dependent data set. The original
cross-sectional data set may or may not include time-dependent data in
the columns. In the next example, we measure outcome Y once
before and twice after intervention T in a randomized
trial:
tdef <- defData(varname = "T", dist = "binary", formula = 0.5)
tdef <- defData(tdef, varname = "Y0", dist = "normal", formula = 10, variance = 1)
tdef <- defData(tdef, varname = "Y1", dist = "normal", formula = "Y0 + 5 + 5 * T",
variance = 1)
tdef <- defData(tdef, varname = "Y2", dist = "normal", formula = "Y0 + 10 + 5 * T",
variance = 1)
set.seed(483726)
dtTrial <- genData(500, tdef)
dtTrial## Key: <id>
## id T Y0 Y1 Y2
## <int> <int> <num> <num> <num>
## 1: 1 0 10.416589 14.15416 20.93181
## 2: 2 1 9.355958 21.89776 25.36428
## 3: 3 1 8.252882 19.07631 23.85342
## 4: 4 0 10.859969 16.47553 20.36986
## 5: 5 1 11.286751 21.73117 25.89952
## ---
## 496: 496 1 11.868557 22.61670 26.60868
## 497: 497 1 10.624719 19.50196 24.86331
## 498: 498 1 10.127564 22.24428 26.07868
## 499: 499 0 11.875659 17.94330 23.04687
## 500: 500 0 11.923223 18.39068 22.50220Longitudinal data are created with a call to
addPeriods. If the cross-sectional data
includes time-dependent data, then the number of periods
nPeriods must be the same as the number of time-dependent
columns. If a variable is not declared as one of the
timevars, it will be repeated each time period. In this
example, the treatment indicator T is not specified as a
time-dependent variable. (Note: if there are two time-dependent
variables, it is best to create two data sets and merge them. This will
be shown later in the vignette).
dtTime <- addPeriods(dtTrial, nPeriods = 3, idvars = "id", timevars = c("Y0", "Y1",
"Y2"), timevarName = "Y")
dtTime## Key: <timeID>
## id period T Y timeID
## <int> <int> <int> <num> <int>
## 1: 1 0 0 10.416589 1
## 2: 1 1 0 14.154165 2
## 3: 1 2 0 20.931814 3
## 4: 2 0 1 9.355958 4
## 5: 2 1 1 21.897757 5
## ---
## 1496: 499 1 0 17.943303 1496
## 1497: 499 2 0 23.046868 1497
## 1498: 500 0 0 11.923223 1498
## 1499: 500 1 0 18.390676 1499
## 1500: 500 2 0 22.502203 1500This is what the longitudinal data look like:
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Longitudinal data with varying observation and interval times
It is also possible to generate longitudinal data with varying
numbers of measurement periods as well as varying time intervals between
each measurement period. This is done by defining specific variables in
the data set that define the number of observations per subject and the
average interval time between each observation. nCount
defines the number of measurements for an individual;
mInterval specifies the average time between intervals for
a subject; and vInterval specifies the variance of those
interval times. If vInterval is set to 0 or is not defined,
the interval for a subject is determined entirely by the mean interval.
If vInterval is greater than 0, time intervals are
generated using a gamma distribution with mean and dispersion
specified.
In this simple example, the cross-sectional data generates individuals with a different number of measurement observations and different times between each observation. Data for two of these individuals is printed:
def <- defData(varname = "xbase", dist = "normal", formula = 20, variance = 3)
def <- defData(def, varname = "nCount", dist = "noZeroPoisson", formula = 6)
def <- defData(def, varname = "mInterval", dist = "gamma", formula = 30, variance = 0.01)
def <- defData(def, varname = "vInterval", dist = "nonrandom", formula = 0.07)
dt <- genData(200, def)
dt[id %in% c(8, 121)] # View individuals 8 and 121## Key: <id>
## id xbase nCount mInterval vInterval
## <int> <num> <num> <num> <num>
## 1: 8 18.01478 4 28.10234 0.07
## 2: 121 22.69431 6 33.21150 0.07The resulting longitudinal data for these two subjects can be
inspected after a call to addPeriods. Notice that no
parameters need to be set since all information resides in the data set
itself:
dtPeriod <- addPeriods(dt)
dtPeriod[id %in% c(8, 121)] # View individuals 8 and 121 only## Key: <timeID>
## id period xbase time timeID
## <int> <int> <num> <num> <int>
## 1: 8 0 18.01478 0 41
## 2: 8 1 18.01478 29 42
## 3: 8 2 18.01478 51 43
## 4: 8 3 18.01478 104 44
## 5: 121 0 22.69431 0 691
## 6: 121 1 22.69431 46 692
## 7: 121 2 22.69431 81 693
## 8: 121 3 22.69431 117 694
## 9: 121 4 22.69431 154 695
## 10: 121 5 22.69431 180 696If a time-sensitive measurement is added to the data set …
def2 <- defDataAdd(varname = "Y", dist = "normal", formula = "15 + .1 * time", variance = 5)
dtPeriod <- addColumns(def2, dtPeriod)… a plot of five randomly selected individuals looks like this:
