meta.lm.propratio2 {vcmeta}

Meta-regression analysis for proportion ratios

Description

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a log proportion 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 proportion ratio associated with a 1-unit increase in that predictor variable, controlling for all other predictor variables in the model.

Usage

meta.lm.propratio2(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

Price RM, Bonett DG (2008). “Confidence intervals for a ratio of two independent binomial proportions.” Statistics in Medicine, 27(26), 5497–5508. ISSN 02776715, doi:10.1002/sim.3376.

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.propratio2(.05, f1, f2, n1, n2, X)

# Should return:
#         Estimate         SE           z     p          LL         UL
# b0  1.4924887636 0.69172794  2.15762393 0.031  0.13672691 2.84825062
# b1  0.0005759509 0.04999884  0.01151928 0.991 -0.09741998 0.09857188
# b2 -1.0837844594 0.59448206 -1.82307345 0.068 -2.24894789 0.08137897
#     exp(Estimate)   exp(LL)   exp(UL)
# b0      4.4481522 1.1465150 17.257565
# b1      1.0005761 0.9071749  1.103594
# b2      0.3383128 0.1055102  1.084782