This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group proportion difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.
meta.lm.prop2(alpha, f1, f2, n1, n2, X)alpha level for 1-alpha confidence
vector of group 1 frequency counts
vector of group 2 frequency counts
vector of group 1 sample sizes
vector of group 2 sample sizes
matrix of predictor values
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
z - z-value
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Bonett DG, Price RM (2014). “Meta-analysis methods for risk differences.” British Journal of Mathematical and Statistical Psychology, 67(3), 371--387. ISSN 00071102, doi:10.1111/bmsp.12024 .
f1 <- c(24, 40, 93, 14, 5)
f2 <- c(12, 9, 28, 3, 1)
n1 <- c(204, 201, 932, 130, 77)
n2 <- c(106, 103, 415, 132, 83)
x1 <- c(4, 4, 5, 3, 26)
x2 <- c(1, 1, 1, 0, 0)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.prop2(.05, f1, f2, n1, n2, X)
#> Estimate SE z p LL UL
#> b0 0.089756283 0.034538077 2.5987632 0.009 0.02206290 0.157449671
#> b1 -0.001447968 0.001893097 -0.7648672 0.444 -0.00515837 0.002262434
#> b2 -0.034670988 0.034125708 -1.0159786 0.310 -0.10155615 0.032214170
# Should return:
# Estimate SE z p LL UL
# b0 0.089756283 0.034538077 2.5987632 0.009 0.02206290 0.157449671
# b1 -0.001447968 0.001893097 -0.7648672 0.444 -0.00515837 0.002262434
# b2 -0.034670988 0.034125708 -1.0159786 0.310 -0.10155615 0.032214170