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Confidence interval for an average proportion difference in paired-samples studies

Source: R/meta_ave.R
meta.ave.prop.ps.Rd

Computes the estimate, standard error, and confidence interval for an average proportion difference from two or more studies.

Usage

meta.ave.prop.ps(alpha, f11, f12, f21, f22, bystudy = TRUE)

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

bystudy

logical to also return each study estimate (TRUE) or not

Value

Returns a matrix. The first row is the average estimate across all studies. If bystudy is TRUE, there is 1 additional row for each study. The matrix has the following columns:

  • Estimate - estimated effect size

  • SE - standard error

  • 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(17, 28, 19)
f12 <- c(43, 56, 49)
f21 <- c(3, 5, 5)
f22 <- c(37, 54, 39)
meta.ave.prop.ps(.05, f11, f12, f21, f22, bystudy = TRUE)
#>          Estimate         SE        LL        UL
#> Average 0.3809573 0.03000016 0.3221581 0.4397565
#> Study 1 0.3921569 0.05573055 0.2829270 0.5013867
#> Study 2 0.3517241 0.04629537 0.2609869 0.4424614
#> Study 3 0.3859649 0.05479300 0.2785726 0.4933572

# Should return:
#          Estimate         SE        LL        UL
# Average 0.3809573 0.03000016 0.3221581 0.4397565
# Study 1 0.3921569 0.05573055 0.2829270 0.5013867
# Study 2 0.3517241 0.04629537 0.2609869 0.4424614
# Study 3 0.3859649 0.05479300 0.2785726 0.4933572