nsue: Calculate Effect Sizes for "meta.nsue" Objects

In metansue: Meta-Analysis of Studies with Non-Statistically Significant Unreported Effects

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")

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).

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.

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.

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

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

metansue documentation built on Sept. 13, 2022, 9:05 a.m.