Description Usage Format Value Author(s) See Also

An object returned by the `jointmeta1`

function, inheriting from class
`jointmeta1`

and representing a fitted joint model for a single
longitudinal and a single time-to-event outcome for data from multiple
studies. Objects of this class have methods for the generic functions
`confint`

, `fixef`

, `formula`

and
`ranef`

. Additionally `rancov`

allows
the user to extract the estimated covariance matrices for the zero mean
random effects.

1 |

An object of class `NULL`

of length 0.

A list with the following components.

`coefficients`

a list with the estimated coefficients. The components of this list are:

`fixed`

the list of fixed effects for sub-models contained in the joint model. The components of this list are:

`longitudinal`

a data frame containing the estimated fixed effect coefficients from the longitudinal sub-model

`survival`

a numeric vector containing the estimated fixed effect coefficients from the longitudinal sub-model

`random`

the list of estimates random effects estimated by the joint model. The components of this list are:

`random_ind`

a list of matrices of length equal to the number of studies in the dataset. Each matrix has number of columns equal to the number of individual level random effects, and number of rows equal to the number of individuals in the study. As

`jointmeta1`

insists on the presence of random effects at the individual level, this item will always be present.`random_stud`

a matrix with number of columns equal to the number of study level random effects, number of rows equal to the number of studies in the dataset. This item is only present if study level random effects are specified in the model fit.

`latent`

a numeric containing the estimates of the latent association parameters for each level of the random effects. The association parameter for the individual level random effects is labelled

`gamma_ind_0`

, and for the study level random effects is labelled`gamma_stud_0`

.

`sigma.e`

a numeric holding the estimate of the variance of the measurement error variance

`rand_cov`

a list containing the covariance matrices for the random effects included in the model. The covariance matrix for the individual level random effects is labelled

`D`

. If study level random effects are included in the model, the covariance matrix for the study level random effects is also included in the list, labelled`A`

.`hazard`

if

`strat = FALSE`

in the function call for`jointmeta1`

then this is a numeric vector containing the common baseline across all studies. If`strat = TRuE`

then this is a list of numeric vectors, each of which is the baseline hazard for each study in the dataset.`loglik`

a list containing the overall likelihood for the joint model (labelled

`jointlhood`

), and the portions of the likelihood attributable to each sub-model (`jointy`

for the longitudinal component and`jointn`

for the survival component).`numIter`

the number of EM algorithm iterations completed during the fitting of the joint model

`convergence`

a logical value, takes a value of

`TRUE`

if convergence was achieved within the set maximum number of iterations,`FALSE`

otherwise.`sharingstrct`

a character string denoting the specified sharing structure used in the joint model. Currently only

`'randprop'`

is supported, denoting zero mean random effects sharing structure (see Wulfsohn and Tsiatis (1997)).`sepests`

A list containing estimates from the separate longitudinal and survival analyses. If separate results are not requested, the elements of the list are set to 'No separate results requested'. However, if separate analyses are requested in the

`jointmeta1`

function call, the components of this list are:`longests`

a list containing estimates from the initial longitudinal fit. The components of this list are:

`beta1`

a data frame of the estimates of the fixed effects from the longitudinal sub-model

`sigma.e`

the value of the variance of the measurement error from the longitudinal sub-model

`D`

the estimate of the covariance matrix for the individual level random effects. Individual level random effects are always included in the joint model

`A`

the estimate of the covariance matrix for the study level random effects. This is only present if study level random effects are specified in the

`jointmeta1`

function call.`log.like.long`

the numeric value of the log likelihood for the initial longitudinal model.

`randstart.ind`

a list of the conditional modes of the individual level random effects in each study given the data and the estimates of the separate longitudinal model parameters

`randstart.ind.cov`

a list of the conditional covariance matrices for each individual for the individual level random effects given the data and the estimates of the separate longitudinal model parameters

`randstart.stud`

a data frame containing the conditional modes of the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the

`jointmeta1`

function call.`randstart.stud.cov`

a list of conditional covariance matrices for each study for the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the

`jointmeta1`

function call.`modelfit`

the initial longitudinal model fit. The model has the same specification as the longitudinal sub-model for the joint model, fitted using the

`lmer`

function from package`lme4`

`survests`

a list containing estimates from the initial survival fit. The components of this list are:

`beta2`

vector of the estimates of the fixed effects included in the survival model.

`haz`

if

`strat = TRUE`

then this is a list of numeric vectors of length equal to the number of studies in the dataset, giving the study specific baseline hazard. If`strat = FALSE`

then the baseline is not stratified by study, and this is one numeric vector giving the common baseline across studies.`rs`

a counter to indicate the last how many unique event times had occured by the individual's survival time - this is for use during further calculation in the joint model EM algorithm. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.

`sf`

the unique event times observed in the dataset. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.

`nev`

a counter of the number of events that occur at each event time.If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.

`log.like.surv`

a numeric containing two values, the log-likelihood with the initial values and the log-likelihood with the final values, see

`coxph.object`

`modelfit`

the initial survival model fit. The model has the same specification as the survival sub-model for the joint model, fitted using the

`coxph`

function from package`survival`

`sep.loglik`

a list containing the log-likelihoods estimated from the separate analyses. It contains three elements, namely

`seplhood`

- the sum of the log-likelihoods from the separate longitudinal and the separate survival analyses,`sepy`

- the log-likelihood from the separate longitudinal analysis,`sepn`

- the log-likelihood from the separate survival analysis.`data`

the

`jointdata`

object containing the data the joint model was fitted to`call`

the function call supplied to the

`jointmeta1`

function.`numstudies`

an integer containing the number of studies present in the data used to fit the joint model

`n.bystudy`

a numeric vector containing the number of individuals present in each study in the data used to fit the joint model. This will be less than the number of individuals in the supplied dataset, if missing data is present in variables included in the model.

`missingids`

the ids of any individuals excluded from the analysis due to missing data

`nobs`

a table containing the number of longitudinal measurements supplied by each study in the data used to fit the model. This will be less than the number of longitudinal measurements in the dataset supplied to the function call, if missing data is present in variables included in the model

Maria Sudell (mesudell@liverpool.ac.uk)

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