vcmeta (version 1.5.0)

meta.lm.mean2: Meta-regression analysis for 2-group mean differences

Description

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group mean difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.

Usage

meta.lm.mean2(alpha, m1, m2, sd1, sd2, n1, n2, X)

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

Arguments

alpha

alpha level for 1-alpha confidence

m1

vector of estimated means for group 1

m2

vector of estimated means for group 2

sd1

vector of estimated SDs for group 1

sd2

vector of estimated SDs for group 2

n1

vector of group 1 sample sizes

n2

vector of group 2 sample sizes

X

matrix of predictor values

References

Bonett2009avcmeta

Examples

n1 <- c(65, 30, 29, 45, 50)
n2 <- c(67, 32, 31, 20, 52)
m1 <- c(31.1, 32.3, 31.9, 29.7, 33.0)
m2 <- c(34.1, 33.2, 30.6, 28.7, 26.5)
sd1 <- c(7.1, 8.1, 7.8, 6.8, 7.6)
sd2 <- c(7.8, 7.3, 7.5, 7.2, 6.8)
x1 <- c(4, 6, 7, 7, 8)
x2 <- c(1, 0, 0, 0, 1)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.mean2(.05, m1, m2, sd1, sd2, n1, n2, X)

# Should return:
#    Estimate        SE         t     p         LL        UL  df
# b0   -15.20 3.4097610 -4.457791 0.000 -21.902415 -8.497585 418
# b1     2.35 0.4821523  4.873979 0.000   1.402255  3.297745 418
# b2     2.85 1.5358109  1.855697 0.064  -0.168875  5.868875 418