meta {metansue}

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

meta(x, ...)

## S3 method for class 'nsue'
meta(x, formula = ~1, hypothesis = NULL,
    n.imp = 500, maxiter = 200, tol = 1e-06, ...)

Arguments

x

an object of class "nsue".

formula

an object of class "formula": a symbolic description of the model to be fitted.

hypothesis

a hypothesis, or NULL to test the main coefficient of the model.

n.imp

number of imputations of NSUEs.

maxiter

maximum number of iterations in the REML estimation of \tau^2.

tol

tolerance in the REML estimation of \tau^2.

...

other arguments (currently ignored).

Details

Use nsue, smc_from_t, smd_from_t or zcor_from_r to create objects of class "nsue".

Models for meta and leave1out 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 returns an object of class "meta.nsue", which is a list containing the following components:

aux

information required for y2var, mi and / or backtransf.

y2var

a function to derive the variances of the effect sizes.

mi

a function to multiply impute effect sizes.

backtransf

a function to back-transform the effect sizes.

measure

a description of the effect-size measure used.

labels

the labels of the studies.

known

a list with the known effect sizes and their indexs.

unknown

a list with the imputations of NSUEs and their indexs.

model

a list with the formula, matrix and coefficients of the model.

heterogeneity

a list with \tau^2, H^2, I^2 and Q test.

hypothesis

the matrixs and coefficients of the hypothesis.

The functions print and summary may be used to print the details or a summary of the results. The generic accessor functions coefficients, fitted.values and residuals extract various useful features of the value returned by meta.

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, doi:10.1001/jamapsychiatry.2015.2196.

Albajes-Eizagirre, A., Solanes, A, Radua, J. (2019) Meta-analysis of non-statistically significant unreported effects. Statistical Methods in Medical Research, 28, 3741–54, doi:10.1177/0962280218811349.

See Also

nsue, smc_from_t, smd_from_t and zcor_from_r for creating objects of class "nsue".

forest for plotting forest plots.

funnel for plotting funnel plots.

metabias for testing for funnel plot asymmetry.

leave1out for computing leave-one-out diagnostics.

Examples

t <- c(3.4, NA, NA, NA, NA, 2.8, 2.1, 3.1, 2.0, 3.4)
n <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22)
meta(smc_from_t(t, n))