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metansue (version 2.6)

meta: 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 nsue meta(x, formula = ~1, hypothesis = NULL, n.imp = 500, maxiter = 200, tol = 1e-06, ...)

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.

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).

Author

Joaquim Radua

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.

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

Run this code
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))

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