meta.lm.odds {vcmeta} | R Documentation |
Meta-regression analysis for odds ratios
Description
This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a log odds 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 odds ratio associated with a 1-unit increase in that predictor variable, controlling for all other predictor variables in the model.
Usage
meta.lm.odds(alpha, f1, f2, n1, n2, X)
Arguments
alpha |
alpha level for 1-alpha confidence |
f1 |
vector of group 1 frequency counts |
f2 |
vector of group 2 frequency counts |
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
References
Bonett DG, Price RM (2015). “Varying coefficient meta-analysis methods for odds ratios and risk ratios.” Psychological Methods, 20(3), 394–406. ISSN 1939-1463, doi:10.1037/met0000032.
Examples
n1 <- c(204, 201, 932, 130, 77)
n2 <- c(106, 103, 415, 132, 83)
f1 <- c(24, 40, 93, 14, 5)
f2 <- c(12, 9, 28, 3, 1)
x1 <- c(4, 4, 5, 3, 26)
x2 <- c(1, 1, 1, 0, 0)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.odds(.05, f1, f2, n1, n2, X)
# Should return:
# Estimate SE z p LL UL
# b0 1.541895013 0.69815801 2.20851868 0.027 0.1735305 2.91025958
# b1 -0.004417932 0.04840623 -0.09126784 0.927 -0.0992924 0.09045653
# b2 -1.071122269 0.60582695 -1.76803337 0.077 -2.2585213 0.11627674
# exp(Estimate) exp(LL) exp(UL)
# b0 4.6734381 1.1894969 18.361564
# b1 0.9955918 0.9054779 1.094674
# b2 0.3426238 0.1045049 1.123307