R: Meta-Analysis of Studies with Non Statistically-Significant...
meta.nsue
R Documentation
Meta-Analysis of Studies with Non Statistically-Significant Unreported Effects
Description
Conduct a meta-analysis. MetaNSUE is a meta-analytic method that allows an unbiased inclusion of studies with Non Statistically-Significant Unreported Effects (NSUEs).
Usage
## S3 method for class 'nsue'
meta(x, data = data.frame(), formula = ~1, hypotheses = NULL,
n.imp = 50, n.bins = 200, maxiter = 200, tol = 1e-06, ...)
Arguments
x
an object of class "nsue".
data
an optional data frame containing variables to be used as regressors in the maximum likelihood step.
formula
an object of class "formula": a symbolic description of the model to be fitted.
hypotheses
a list of hypotheses, or NULL to test the coefficients of the model.
n.imp
number of imputations of NSUEs.
n.bins
number of bins used in the imputations.
maxiter
maximum number of iterations in the REML estimation of τ^2.
tol
tolerance in the REML estimation of τ^2.
...
other arguments (currently ignored).
Details
Use smc_from_t, smd_from_t, z_from_r or r_in_smd_from_t_means_and_sds1 to create "nsue" objects.
Models for meta.nsue, leave1out.nsue and metalm.meta.nsue are specified symbolically. The formula is a series of terms which specify a linear predictor for x. A formula specification of the form first + second indicates a multiple regression by first and second. A specification of the form first:second indicates the interaction of first with second. The specification first*second is the same as first + second + first:second.
Each hypothesis must be a matrix (or vector) giving linear combinations of coefficients by rows.
Value
meta.nsue returns an object of class "meta.nsue", which is a list containing the following components:
measure
the effect-size measure used.
known
a list with the known effect sizes and their indexs.
unknown
a list with the imputations of NSUEs and their indexs.
y.var
the variances if the effect sizes.
y2var_k1
a constant needed to derive the variances.
y2var_k2
a constant needed to derive the variances.
labels
the labels of the studies.
rm
a list with the expected correlation between repeated-measures studies, a conversion matrix and the study weights.
heterogeneity
a list with τ^2, H^2, I^2 and Q test.
model
a list with the formula, matrix and coefficients of the model.
hypotheses
a list with the matrixs and coefficients of the hypotheses.
The functions print and summary may be used to print the details or a summary of the results. The function subset returns the subset of studies that meets a condition. The generic accessor functions coefficients, fitted.values and residuals extract various useful features of the value returned by meta.nsue.
Author(s)
Joaquim Radua
References
Radua, J., Schmidt, A., Borgwardt, S., Heinz, A., Schlagenhauf, F., McGuire, P., Fusar-Poli, P. (2015)
Ventral striatal activation during reward processing in psychosis. A neurofunctional meta-analysis.
JAMA Psychiatry, 72, 1243–51.
See Also
smc_from_t, smd_from_t, z_from_r and r_in_smd_from_t_means_and_sds1 for creating "nsue" objects.
forest for plotting forest plots.
funnel for plotting funnel plots.
metabias for testing for funnel plot asymmetry.
leave1out for computing leave-one-out diagnostics.
providing 
.