Simulates a dataset with correlated time-varying covariates with an exchangeable correlation structure. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster.
make_one_dataset(n, obs, n.TBins, pcor, wcor, parameters, cat.parameters)The number of clusters.
The number of observations per cluster.
Number of time-varying binary variables.
The across-subject correlation matrix. See Details.
The within-subject correlation matrix. See Details.
A dataframe containing the general simulation parameters. See Details.
A dataframe containing parameters for the categorical variables. See Details.
SPECIFYING THE PARAMETERS MATRIX
The matrix parameters contains parameters required to generate all non-categorical variables.
It must contain column names name, type, across.mean, across.SD, across.var, within.var, prop,
and error.SD. (To see an example, use data(params).) Each variable to be generated requires
either one or two rows in parameters, depending on the variable type.
The possible variable types and their corresponding specifications are:
Static binary variables do not change over time within a cluster. For example, if clusters
are subjects, sex would be a static binary variable. Generating such a variable requires a single row
of type static.binary with prop corresponding to the proportion of clusters for which the
variable equals 1 and all other columns set to NA. (The correct standard deviation will automatically
be computed later.) For example, if the variable is an indicator for a subject's being male, then prop
specifies the proportion of males to be generated.
Time-varying binary variables can change within a cluster over time, as for
an indicator for whether a subject is currently taking the study drug. These variables require two rows in
parameters. The first row should be of type static.binary with prop representing
the proportion of clusters for which the time-varying binary variable is 1 at least once
(and all other columns set to NA). For example, this row in parameters could represent the
proportion of subjects who ever take the study drug ("ever-users").
The second row should be of type subject.prop with across.mean representing, for clusters
that ever have a 1 for the binary variable, the proportion of observations within the cluster for which
the variable is equal to 1. (All other columns should be set to NA.) For example, this this row in
parameters could
represent the proportion of observations for which an ever-user is currently taking the drug. To indicate
which pair of variables go together, the subject.prop should have the same name as the static.binary
variable, but with the suffix _s appended (for example, the former could be named drug_s and
the latter drug).
Normal variables are normally distributed within a cluster such that the within-cluster
means are themselves also normally distributed in the population of clusters. Generating a normal variable requires
specification of the population mean (across.mean) and standard deviation (across.SD) as well as of
the within-cluster standard deviation (within.SD). To generate a static continuous variable, simply set
within.SD to be extremely small (e.g., $1 * 10^-7$) and all corresponding correlations in matrix
wcor to 0.
Time-function variables are linear functions of time (with normal error) within each cluster such
that the within-cluster baseline values are normally distributed in the population of clusters. Generating a
time-function variable requires two entries. The first entry should be of type time.function and
specifies the population mean (across.mean) and standard deviation (across.SD) of the within-cluster
baseline values as well as the error standard deviation (error.SD). The second entry should be of
type normal and should have the same name as the time.function entry, but with the "_s" suffix.
This entry specifies the mean (across.mean) and standard deviation (across.SD) of the within-cluster
slopes.
SPECIFYING THE CATEGORICAL PARAMETERS MATRIX
The matrix cat.parameters contains parameters required to generate the single categorical variable,
if any.
It must contain column names level, parameter,
and beta. (To see an example, use data(cat.params).)
The reference level: Each categorical variable must have exactly one "reference" level. The reference level should have one
row in cat.parameters for which parameters is set to NA and beta is set
to ref. For example, in the example file cat.params specifying parameters to generate a
subject's race, the reference level is white.
Other levels: Other levels of the categorical variable will have one or more rows. One row with parameter set to intercept
and beta set to a numeric value
represents the intercept term in the corresponding multinomial model. Any subsequent rows, with parameters set to
names of other variables in the dataset and beta set to numeric values,
represents other coefficients in the corresponding multinomial models.
SPECIFYING THE POPULATION CORRELATION MATRIX
Matrix pcor specifies the population (i.e., across-cluster) correlation matrix. It should have the same
number of rows and columns as parameters as well as the same variable names and ordering of variables.
SPECIFYING THE WITHIN-CLUSTER CORRELATION MATRIX
Matrix wcor specifies the within-cluster correlation matrix. The order of the variables listed in this file should be
consistent with the order in params and pcor. However, static.binary and subject.prop variables
should not be included in wcor since they are static within a cluster. Static continuous variables should be included,
but all the correlations should be set to zero.
# NOT RUN {
data = make_one_dataset(n=10, obs=10, n.TBins=2, pcor=pcor, wcor=wcor,
parameters=complete_parameters(params, n=10), cat.parameters=cat.params)$data
# }
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