simdat1randlevel: Data simulation function for the case with only individual level random effects

Description

Internal function to simulate joint data with random effect only at the individual level. Used inside simjointmeta.

Usage

simdat1randlevel(
  k,
  n,
  rand_ind,
  sepassoc,
  ntms,
  longmeasuretimes,
  beta1,
  beta2,
  gamma,
  sigb_ind,
  vare,
  theta0,
  theta1,
  censoring,
  censlam,
  truncation,
  trunctime,
  q
)

Arguments

the number of studies to be simulated

a vector of length equal to k denoting the number of individuals to simulate per study

rand_ind

a character string specifying the individual level random effects structure. If rand_ind = 'intslope' then there is an individual specific random intercept and random time (slope) term included in the model. If rand_ind = 'int' then the model includes only a individual specific random intercept.

sepassoc

a logical taking value FALSE if proportional association is required, TRUE if a separate association parameter is required for each random effect shared between the sub-models

ntms

the maximum possible number of longitudinal measurements - should equal the length of the supplied longmeasuretimes

longmeasuretimes

a vector giving the exact times of the longitudinal measurement times. If this is not specified in the function call then the measurement times of the longitudinal outcome are set to start at 0 then take integer values up to and including ntms - 1.

beta1

a vector of the fixed effects for the longitudinal sub-model. Here the first element gives the coefficient for a fixed or population intercept, the second gives the coefficient for the binary treatment assignment covariate and the third element gives the covariate for the time (slope) covariate

beta2

the coefficient for the binary treatment assignment covariate

gamma

are the association parameters. If different association parameters are supplied for each study in the dataset, this is a list of vectors each of length equal to the number of individual level random effects. If the same association parameters are supplied for each study in the dataset then this is a vector of length equal to the number of individual level random effects. If separate association parameters are defined for different random effects (i.e. if sepassoc = TRUE) then the elements in each of these vectors are not necessarily identical. If sepassoc = FALSE then the association parameters within each vector are identical.

sigb_ind

the covariance matrix for the individual level random effects. This should have number of rows and columns equal to the number of individual level random effects.

vare

the variance of the measurement error term

theta0

parameter defining the distribution of the survival times. A separate parameter can be defined per study or a common parameter across all studies. See Bender et al 2005 for advice on approximating appropriate values for theta0 and theta1 the using extreme value distribution.

theta1

censoring

a logical indicating whether the simulated survival times should be censored or not

censlam

the lambda parameter controlling the simulated exponentially distributed censoring times. This can either be supplied as one value for all studies simulated, or a vector of length equal to the number of studies in the dataset.

truncation

a logical value to specify whether the simulated survival times should be truncated at a specified time or not.

trunctime

if truncation = TRUE then the survival times will be truncated at the specified trunctime

the number of random effects at the individual level

Value

This function returns a list with three named elements. The first element is named 'longdat', the second 'survdat', the third 'percentevent'. Each of these elements is a list of length equal to the number of studies specified to simulate in the function call.

The element 'longdat' is a list of the simulated longitudinal data sets. Each longitudinal dataset contains the following variables:

id: a numeric id variable
Y: the continuous longitudinal outcome
time: the numeric longitudinal time variable
study: a study membership variable
intercept: an intercept term
treat: a treatment assignment variable to one of two treatment groups
ltime: a duplicate of the longitudinal time variable

The element 'survdat' is a list of the simulated survival data sets. Each survival dataset contains the following variables:

id: a numeric id variable
survtime: the numeric survival times
cens: the censoring indicator
study: a study membership variable
treat: a treatment assignment variable to one of two treatment groups

The element 'percentevent' is a list of the percentage of events over censorings seen in the simulated survival data.