vcmeta (version 1.5.0)

meta.lm.meanratio2: 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)

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

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

Bonett2020vcmeta

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