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.
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
References
Bonett DG, Price RM (2020). “Confidence intervals for ratios of means and medians.” Journal of Educational and Behavioral Statistics, 45(6), 750–770. ISSN 1076-9986, doi:10.3102/1076998620934125 .
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)
#> Estimate SE z p LL UL exp(Estimate)
#> b0 -0.40208954 0.09321976 -4.313351 0 -0.58479692 -0.21938216 0.6689208
#> b1 0.06831545 0.01484125 4.603078 0 0.03922712 0.09740377 1.0707030
#> exp(LL) exp(UL)
#> b0 0.557219 0.8030148
#> b1 1.040007 1.1023054
# 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