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Confidence interval for a log-linear contrast of mean ratios from paired-samples studies

Source: R/meta_comp.R
meta.lc.meanratio.ps.Rd

Computes the estimate, standard error, and confidence interval for a log-linear contrast of paired-sample mean ratios from two or more studies. A Satterthwaite adjustment to the degrees of freedom is used to improve the accuracy of the confidence interval. Equality of variances within or across studies is not assumed.

For more details, see Section 3.2 of Bonett (2021, Volume 5).

Usage

meta.lc.meanratio.ps(alpha, m1, m2, sd1, sd2, cor, n, v)

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

v

vector of contrast coefficients

Value

Returns 1-row matrix with the following columns:

  • Estimate - estimated log-linear contrast

  • SE - standard error of log-linear contrast

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

  • exp(Estimate) - exponentiated log-linear contrast

  • exp(LL) - lower limit of the exponentiated confidence interval

  • exp(UL) - upper limit of the exponentiated confidence interval

  • df - degrees of freedom

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 .

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

Examples

m1 <- c(53, 60, 53, 57)
m2 <- c(55, 62, 58, 61)
sd1 <- c(4.1, 4.2, 4.5, 4.0)
sd2 <- c(4.2, 4.7, 4.9, 4.8)
cor <- c(.72, .78, .81, .85)
n <- c(30, 50, 30, 70)
v <- c(.5, .5, -.5, -.5)
meta.lc.meanratio.ps(.05, m1, m2, sd1, sd2, cor, n, v)
#>           Estimate       SE         LL         UL exp(Estimate)  exp(LL)
#> Contrast 0.0440713 0.008265 0.02767047 0.06047213      1.045057 1.028057
#>           exp(UL)    df
#> Contrast 1.062338 98.38

# Should return:
#           Estimate       SE         LL         UL exp(Estimate)
# Contrast 0.0440713 0.008265 0.02767047 0.06047213      1.045057
#           exp(LL)  exp(UL)    df
# Contrast 1.028057 1.062338 98.38