simdat2randlevels: Data simulation function for the case with both individual...

In mesudell/joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

Description

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

Usage

simdat2randlevels(k, n, rand_ind, rand_stud, sepassoc, ntms, longmeasuretimes,
  beta1, beta2, gamma, sigb_ind, sigb_stud, vare, theta0, theta1, censoring,
  censlam, truncation, trunctime, q, r)

Arguments

k	the number of studies to be simulated
n	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.
rand_stud	a character string specifying the study level random effects structure. If this is set to NULL or not specified in the function call then no study level random effects are included in the model that the data is simulated from. There are three options if data is to be simulated with random effects at the study level. If a study level random intercept only is to be included, then set rand_stud = 'int'. Else if a study level random treatment assignment term only is to be included then set rand_stud = 'treat'. Finally if both a study level random intercept and a study level random treatment effect is to be included, then set rand_stud = 'inttreat'.
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 total number of 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 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. In each vector of association parameters the first q values are the association parameters for the individual level random effects, and the remaining values are the association parameters for the study level random effects.
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.
sigb_stud	the covariance matrix for the study level random effects. This should have number of rows and columns equal to the number of study level random effects. This should only be specified if rand_stud is specified in the function call.
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	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.
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
q	the number of individual level random effects
r	the number of study level random effects

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.

mesudell/joineRmeta documentation built on Jan. 24, 2020, 6:06 p.m.