metansue (version 2.6)

nsue: Calculate Effect Sizes for “meta.nsue” Objects

Description

These functions create objects of class "nsue", ready to be used by meta and leave1out.

Usage

nsue(y, y_lo = -y_up, y_up, aux, y2var, mi, backtransf = .backtransf_identity,
     measure = "effect size", labels = "study")
smc_from_t(t, n, alpha = 0.05, labels = "study")
smd_from_t(t, n1, n2, alpha = 0.05, labels = "study")
zcor_from_r(r, n, alpha = 0.05, labels = "study")

Value

nsue, smc_from_t, smd_from_t, and zcor_from_r return objects of class "nsue".

The function print may be used to print a summary of the results. The function subset returns the subset of studies that meets a condition.

An object of class "nsue" is a list containing the following components:

y

the effect-sizes.

y_lo

the effect-sizes corresponding to the lower statistical threshold.

y_up

the effect-sizes corresponding to the upper statistical threshold.

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.

Users can create their objects of class "nsue" for effect sizes not included in the package.

Arguments

y

a vector to specify the effect-sizes. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs).

t

a vector to specify the t-values of the studies. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs).

r

a vector to specify the correlation coefficients of the studies. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs).

y_lo

a vector to specify the effect-sizes corresponding to the lower statistical threshold.

y_up

a vector to specify the effect-sizes corresponding to the upper statistical threshold.

aux

a data.frame to specify information required for y2var, mi and / or backtransf.

n

a vector to specify the sample sizes of the studies.

n1

a vector to specify the sample sizes of the first group (e.g. patients) of studies.

n2

a vector to specify the sample sizes of the second group (e.g. controls) of the studies.

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

a vector to specify the labels of the studies.

alpha

a vector to specify the p-value thresholds used in the studies (e.g. 0.05).

Author

Joaquim Radua

Details

Use nsue for creating an object of class "nsue".

Use smc_from_t for creating an object of class "nsue" for standardized mean changes from the t-values of the paired Student t-tests, e.g. in repeated-measures studies analyzing the amount of change in within a group.

Use smd_from_t for creating an object of class "nsue" for standardized mean differences from t-values of the two-sample Student t-tests, e.g. in studies comparing a quantitative (normally-distributed) variable between two groups.

Use zcor_from_r for creating an object of class "nsue" for Pearson correlation coefficients (using the Fisher's transform), e.g. in studies examining the association between two quantitative (normally-distributed) variables.

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

meta for conducting a meta-analysis.

leave1out for computing leave-one-out diagnostics.

Examples

# Standardized mean change in one sample:
t <- c(3.4, NA, NA, NA, 3.2, 2.8, 2.1, 3.1, 2.0, 3.4)
n <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22)
smc <- smc_from_t(t, n)
m0 <- meta(smc)
smc
m0

# Standardized mean difference between two samples:
t <- c(4.8, 3.2, NA, NA, NA, 3.2, 2.0, 2.3, 2.7, 3.1)
n1 <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22)
n2 <- c(38, 20, 22, 25, 20, 28, 25, 30, 18, 23)
smd <- smd_from_t(t, n1, n2)
m1 <- meta(smd)
smd
m1

# Pearson correlation:
r <- c(0.80, NA, NA, NA, 0.32, 0.45, 0.53, 0.67, 0.74, 0.56)
n <- c(40, 22, 13, 12, 28, 22, 27, 28, 15, 23)
zr <- zcor_from_r(r, n)
m2 <- meta(zr)
zr
m2