Meta-regression analysis for odds ratios

Source: R/meta_model.R

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.

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)
#>        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

# 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