Description Usage Arguments Value Examples

Fit a bivariate generalized linear mixed-effects model (GLMM) for non-differential sensitivity and specificity using the `glmer`

function in `lme4`

.
Lower and upper bounds for Se and Sp can be specified according to the assumptions of the study.

1 |

`data` |
a data frame containing the 2 by 2 data of the diagnostics table of exposure status for every study in a meta-analysis.
It contains at least 4 columns in the data named as following: |

`lower` |
an optional argument specifying the lower bound assumption of Se and Sp. Default to 0.5 (or the lowest Se/Sp of all studies, whichever is lower), which provides the mild assumption that Se and Sp are better than chance. |

`upper` |
an optional argument specifying the upper bound assumption of Se and Sp. Default to 1. |

`id` |
a TRUE of FALSE argument indicating if the supplied data has a |

`...` |
optional parameters passed to glmer. |

It returns an object of class mermod.
Besides generic class methods, `paramEst()`

is implemented in `BayesSenMC`

to get the parameter estimates used in the Bayesian misclassification model functions.

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