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Meta-regression analysis for paired-samples log mean ratios

Source: R/meta_model.R
meta.lm.meanratio.ps.Rd

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

Usage

meta.lm.meanratio.ps(alpha, m1, m2, sd1, sd2, cor, n, X)

Arguments

alpha

alpha level for 1-alpha confidence

m1

vector of estimated means for measurement 1

m2

vector of estimated means for measurement 2

sd1

vector of estimated SDs for measurement 1

sd2

vector of estimated SDs for measurement 2

cor

vector of estimated correlations for paired measurements

n

vector of 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 (2020). “Confidence intervals for ratios of means and medians.” Journal of Educational and Behavioral Statistics, 45(6), 750–770. ISSN 1076-9986, doi:10.3102/1076998620934125 .

Examples

n <- c(65, 30, 29, 45, 50)
cor <- c(.87, .92, .85, .90, .88)
m1 <- c(20.1, 20.5, 19.3, 21.5, 19.4)
m2 <- c(10.4, 10.2, 8.5, 10.3, 7.8)
sd1 <- c(9.3, 9.9, 10.1, 10.5, 9.8)
sd2 <- c(7.8, 8.0, 8.4, 8.1, 8.7)
x1 <- c(2, 3, 3, 4, 4)
X <- matrix(x1, 5, 1)
meta.lm.meanratio.ps(.05, m1, m2, sd1, sd2, cor, n, X)
#>      Estimate         SE        z     p           LL        UL exp(Estimate)
#> b0 0.50957008 0.13000068 3.919749 0.000  0.254773424 0.7643667      1.664575
#> b1 0.07976238 0.04133414 1.929697 0.054 -0.001251047 0.1607758      1.083030
#>      exp(LL)  exp(UL)
#> b0 1.2901693 2.147634
#> b1 0.9987497 1.174422

# Should return: 
#      Estimate         SE           LL        UL        z     p
# b0 0.50957008 0.13000068  0.254773424 0.7643667 3.919749 0.000
# b1 0.07976238 0.04133414 -0.001251047 0.1607758 1.929697 0.054
#     exp(Estimate)   exp(LL)  exp(UL)
# b0       1.664575 1.2901693 2.147634
# b1       1.083030 0.9987497 1.174422