meta.lm.stdmean.ps {vcmeta}

Meta-regression analysis for paired-samples standardized mean differences

Description

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a paired-samples standardized mean difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.

Usage

meta.lm.stdmean.ps(alpha, m1, m2, sd1, sd2, cor, n, X, stdzr)

Arguments

alpha

alpha level for 1-alpha confidence

m1

vector of estimated means for measurement 1

m2

vector of estimated means for measurement 2

sd1

vector of estimated SDs for measurement 1

sd2

vector of estimated SDs for measurement 2

cor

vector of estimated correlations for paired measurements

n

vector of sample sizes

X

matrix of predictor values

stdzr

• set to 0 for square root unweighted average variance standardizer

• set to 1 for measurement 1 SD standardizer

• set to 2 for measurement 2 SD standardizer

Value

Returns a matrix. The first row is for the intercept with one additional row per predictor. The matrix has the following columns:

• Estimate - OLS estimate

• SE - standard error

• t - t-value

• p - p-value

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

References

Bonett DG (2009). “Meta-analytic interval estimation for standardized and unstandardized mean differences.” Psychological Methods, 14(3), 225–238. ISSN 1939-1463, doi:10.1037/a0016619.

Examples

n <- c(65, 30, 29, 45, 50)
cor <- c(.87, .92, .85, .90, .88)
m1 <- c(20.1, 20.5, 19.3, 21.5, 19.4)
m2 <- c(10.4, 10.2, 8.5, 10.3, 7.8)
sd1 <- c(9.3, 9.9, 10.1, 10.5, 9.8)
sd2 <- c(7.8, 8.0, 8.4, 8.1, 8.7)
x1 <- c(2, 3, 3, 4, 4)
X <- matrix(x1, 5, 1)
meta.lm.stdmean.ps(.05, m1, m2, sd1, sd2, cor, n, X, 0)

# Should return:
#      Estimate         SE         z     p         LL        UL
# b0 1.01740253 0.25361725 4.0115667 0.000  0.5203218 1.5144832
# b1 0.04977943 0.07755455 0.6418635 0.521 -0.1022247 0.2017836