Meta-regression analysis for paired-samples proportion differences

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

Usage

meta.lm.prop.ps(alpha, f11, f12, f21, f22, X)

Arguments

alpha

alpha level for 1-alpha confidence

f11

vector of frequency counts in cell 1,1

f12

vector of frequency counts in cell 1,2

f21

vector of frequency counts in cell 2,1

f22

vector of frequency counts in cell 2,2

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 (2012). “Adjusted wald confidence interval for a difference of binomial proportions based on paired data.” Journal of Educational and Behavioral Statistics, 37(4), 479–488. ISSN 1076-9986, doi:10.3102/1076998611411915 .

Examples

f11 <- c(40, 20, 25, 30)
f12 <- c(3, 2, 2, 1)
f21 <- c(7, 6, 8, 6)
f22 <- c(26, 25, 13, 25)
x1 <- c(1, 1, 4, 6)
x2 <- c(1, 1, 0, 0)
X <- matrix(cbind(x1, x2), 4, 2)
meta.lm.prop.ps(.05, f11, f12, f21, f22, X)
#>       Estimate         SE          z     p          LL         UL
#> b0 -0.21113402 0.21119823 -0.9996960 0.317 -0.62507494 0.20280690
#> b1  0.02185567 0.03861947  0.5659236 0.571 -0.05383711 0.09754845
#> b2  0.12575138 0.17655623  0.7122455 0.476 -0.22029248 0.47179524

# Should return: 
#       Estimate         SE          z     p          LL         UL
# b0 -0.21113402 0.21119823 -0.9996960 0.317 -0.62507494 0.20280690
# b1  0.02185567 0.03861947  0.5659236 0.571 -0.05383711 0.09754845
# b2  0.12575138 0.17655623  0.7122455 0.476 -0.22029248 0.47179524