Description Usage Arguments Details Value References See Also Examples

This function takes joint model fits from either `joint`

or `jointModel`

and pools the information from the fits
in the second stage of a two stage meta-analysis (MA).

1 2 3 4 5 6 7 8 |

`fits` |
a list of joint modelling fits. These fits should all be of the same type, with the same model specification. |

`SE` |
a list of the results from |

`longpar` |
a vector of character strings of parameters from the longitudinal sub-model for which meta-analyses should be performed |

`survpar` |
a vector of character strings of parameters from the survival sub-model for which meta-analyses should be performed |

`assoc` |
a logical indicating whether a meta-analysis should be performed for the association parameter(s) |

`studynames` |
a vector of character strings containing the names for the studies present in the dataset that the joint models were fitted to. These character strings if supplied are used to label the meta-analyses performed by the function |

The joint model fits modelled using the `joineR`

package link
the sub-models using shared zero mean random effects (see Henderson et al
(2000)). However the joint model fits modelled using the `JM`

package link the sub-models using sharing structures that involve both
the fixed and random effects. If a parameter specified in survpar is
also present in the fixed effects of the longitudinal sub-model, a direct
effect of the parameter on the risk of an event can be extracted from the
survival sub-model, as well as the overall effect resulting from the sum
of fixed effect in the survival sub-model, and the presence of the
parameter in the longitudinal sub-model, present in the sharing structure
of the joint model. As such, if a parameter specified in
`survpar`

is also present as a fixed effect in the longitudinal
sub-model, and the fixed and random effects make up the sharing structure
linking the sub-models, the overall parameter effect is found by
*β_2 + (α * β_1)*, where *α* is
the association parameter, *β_2* is the coefficient for the
parameter in question from the survival sub-model, and *β_1* is
the coefficient for the parameter in question from the longitudinal
sub-model. For more information about overall effects versus direct
effects see Ibrahim et al (2010), Rizopoulos (2012) and Gould et al
(2015). Because both a direct and an overall effect of the survival
parameters can be extracted from the model, both are present in the
results if the joint models supplied in the fits are fitted using the
`JM`

package.

This function returns a list of results for the two stage MA. These
results are split by the type of parameter being pooled. If the names
of longitudinal parameters were supplied to `longpar`

then an
element named `longMA`

will be present in the results. If the
names of survival parameters were supplied to `survpar`

then if the
supplied joint model fits were fitted using the `joint`

function
from the `joineR`

package, an element named `survMA.direct`

will be present in the results. If the supplied joint model fits were
fitted using the `jointModel`

function from the `JM`

package,
two elements named `survMA.direct`

and `survMA.overall`

will be
present. If `assoc = TRUE`

then an element labelled `assocMA`

will be present in the results.

Each element of each of these components of the results (`longMA`

,
`survMA.direct`

, `assocMA`

...) is of class `metagen`

,
and is the result of using the `metagen`

function on
the results of joint models fitted to multiple studies in the dataset.
This method pools the supplied information in fixed and random MA using
inverse variance weighting. Forest plots can be produced for these
results simply by applying the function `forest`

to the
objects of class `metagen`

/ `meta`

supplied in the results.

Ibrahim et al (2010) Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data. JOURNAL OF CLINICAL ONCOLOGY 28 (10): 2796-2801

Rizopoulos (2012) Joint Models for Longitudinal and Time-to-Event Data With Applications in R. Chapman and Hall/CRC Biostatistics Series

Henderson et al (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics, 1,4, pp. 465–480

Gould et al (2015) Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group. Statistics in Medicine 34(14): 2181–2195. doi:10.1002/sim.6141.

`joint`

, `jointModel`

,
`jointSE`

, `metagen`

1 2 3 4 5 6 7 8 | ```
joineRmodels <- joineRfits[c("joineRfit1", "joineRfit2", "joineRfit3")]
joineRmodelsSE <- joineRfits[c("joineRfit1SE", "joineRfit2SE",
"joineRfit3SE")]
MAjoineRfits <- jointmeta2(fits = joineRmodels, SE = joineRmodelsSE,
longpar = c("time", "treat1"),
survpar = "treat1", assoc = TRUE,
studynames = c("Study 1", "Study 2", "Study 3"))
``` |

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