meta.lm.mean.ps {vcmeta}

Meta-regression analysis for paired-samples mean differences

Description

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

Usage

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

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

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

• df - degrees of freedom

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.mean.ps(.05, m1, m2, sd1, sd2, cor, n, X)

# Should return:
#    Estimate        SE        t     p        LL        UL  df
# b0     8.00 1.2491990 6.404104 0.000 5.5378833 10.462117 217
# b1     0.85 0.3796019 2.239188 0.026 0.1018213  1.598179 217