| meta.lm.meanratio2 {vcmeta} | R Documentation |
Meta-regression analysis for 2-group log mean ratios
Description
This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group log mean ratio. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity. The exponentiated slope estimate for a predictor variable describes a multiplicative change in the mean ratio associated with a 1-unit increase in that predictor variable, controlling for all other predictor variables in the model.
Usage
meta.lm.meanratio2(alpha, m1, m2, sd1, sd2, n1, n2, X)
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 |
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
z - z-value
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
exp(Estimate) - the exponentiated estimate
exp(LL) - lower limit of the exponentiated confidence interval
exp(UL) - upper limit of the exponentiated confidence interval
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)
X <- matrix(x1, 5, 1)
meta.lm.meanratio2(.05, m1, m2, sd1, sd2, n1, n2, X)
# Should return:
# Estimate SE LL UL z p
# b0 -0.40208954 0.09321976 -0.58479692 -0.21938216 -4.313351 0
# b1 0.06831545 0.01484125 0.03922712 0.09740377 4.603078 0
# exp(Estimate) exp(LL) exp(UL)
# b0 0.6689208 0.557219 0.8030148
# b1 1.0707030 1.040007 1.1023054