nlmeNDiff {BayesSenMC}

Non-differential Generalized Linear Mixed Effects Model

Description

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.

Usage

nlmeNDiff(data, lower = 0.5, upper = 1, id = FALSE, ...)

Arguments

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: n11 indicates the true positives, n01 the false positives, n00 the true negatives and n10 the false negatives. Each column is a vector of same length, which is the number of meta-analysis study results used in the model.

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 sid column that gives same studies the same subject ID. Default to FALSE, which assumes that all studies have different IDs.

...

optional parameters passed to glmer.

Value

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.

Examples

data(bd_meta)

mod <- nlmeNDiff(bd_meta, lower = 0)