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Meta-regression analysis for 2-group proportion differences

Source: R/meta_model.R
meta.lm.prop2.Rd

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group proportion difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity.

For more details, see Section 3.4 of Bonett (2021, Volume 5).

Usage

meta.lm.prop2(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

References

Bonett DG, Price RM (2014). “Meta-analysis methods for risk differences.” British Journal of Mathematical and Statistical Psychology, 67(3), 371–387. ISSN 00071102, doi:10.1111/bmsp.12024 .

Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.

Examples

f1 <- c(24, 40, 93, 14, 5)
f2 <- c(12, 9, 28, 3, 1)
n1 <- c(204, 201, 932, 130, 77)
n2 <- c(106, 103, 415, 132, 83)
x1 <- c(4, 4, 5, 3, 26)
x2 <- c(1, 1, 1, 0, 0)
X <- matrix(cbind(x1, x2), 5, 2)
meta.lm.prop2(.05, f1, f2, n1, n2, X)
#>        Estimate          SE      z     p          LL          UL
#> b0  0.089756283 0.034538077  2.599 0.009  0.02206290 0.157449671
#> b1 -0.001447968 0.001893097 -0.765 0.444 -0.00515837 0.002262434
#> b2 -0.034670988 0.034125708 -1.016 0.310 -0.10155615 0.032214170

# Should return:
#        Estimate          SE      z     p          LL          UL
# b0  0.089756283 0.034538077  2.599 0.009  0.02206290 0.157449671
# b1 -0.001447968 0.001893097 -0.765 0.444 -0.00515837 0.002262434
# b2 -0.034670988 0.034125708 -1.016 0.310 -0.10155615 0.032214170