Last data update: 2014.03.03

R: Calculate Effect Sizes for "meta.nsue" Objects
smc_from_tR Documentation

Calculate Effect Sizes for “meta.nsue” Objects

Description

These function calculate various effect sizes and output objects of class "nsue", ready to be used by meta and leave1out.

Usage

smc_from_t(t, n, alpha = 0.05, labels = "study", rm.r = 0.3)
smd_from_t(t, n1, n2, alpha = 0.05, labels = "study", rm.r = 0.3)
z_from_r(r, n, alpha = 0.05, labels = "study", rm.r = 0.3)
r_in_smd_from_t_means_and_sds1(t,
    n1, mean1.pre, sd1.pre, mean1.post, sd1.post,
    n2, mean2.pre, sd2.pre, mean2.post, sd2.post,
    alpha = 0.05, labels = "study", r.range = c(0, 0.99), rm.r = 0.3)
r_in_smd_from_t_means_and_sds2(x, maxiter = 200, tol = 1e-6)

Arguments

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).

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.

mean1.pre

a vector to specify the means of the first group (e.g. patients) of the studies before the treatment.

sd1.pre

a vector to specify the standard deviations of the first group (e.g. patients) of the studies before the treatment.

mean1.post

a vector to specify the means of the first group (e.g. patients) of the studies after the treatment.

sd1.post

a vector to specify the standard deviations of the first group (e.g. patients) of the studies after the treatment.

mean2.pre

a vector to specify the means of the second group (e.g. patients) of the studies before the treatment.

sd2.pre

a vector to specify the standard deviations of the second group (e.g. patients) of the studies before the treatment.

mean2.post

a vector to specify the means of the second group (e.g. patients) of the studies after the treatment.

sd2.post

a vector to specify the standard deviations of the second group (e.g. patients) of the studies after the treatment.

alpha

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

labels

a vector to specify the names of the studies.

r.range

range of pre-post correlations.

rm.r

the expected correlation coefficient between repeated-measures.

x

an object of class "nsue".

maxiter

maximum number of iterations in the REML estimation of τ^2.

tol

tolerance in the REML estimation of τ^2.

Details

Use smc_from_t for calculating the 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 calculating the 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 z_from_r for calculating the Fisher's r-to-z transformed correlations coefficients from the Pearson correlation coefficients (r), e.g. in studies examining the association between two quantitative (normally-distributed) variables.

Use r_in_smd_from_t_means_and_sds1 and meta for estimating the missing pre-post correlations in a meta-analysis of the pre-post differences, e.g. when you only have the means and standard deviations before and after a treatment. Afterwards, use r_in_smd_from_t_means_and_sds2 to conduct the meta-analysis of the pre-post differences. Please see Harrison et al for details.

Value

smc_from_t, smd_from_t, z_from_r and r_in_smd_from_t_means_and_sds1 return objects of class "nsue".

The function print may be used to print a summary of the results.

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

measure

the effect-size measure used.

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.

y.var

the variances of 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.

Note

Studies with the same label will be considered to be repeated-measures, and will thus have lower weight during the MLE step, be imputed accounting for the correlation, and combined before meta-analysis.

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.

Harrison, A., Fernandez de la Cruz, L., Enander, J., Radua, J., Mataix-Cols, D. (2016) Cognitive-behavioral therapy for body dysmorphic disorder: A systematic review and meta-analysis of randomized controlled trials. Clinical Psychology Review, in Press.

See Also

meta for conducting a meta-analysis.

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)
x <- smc_from_t(t, n)
x
meta(x)

Results