hMeanChiSqCI {ReplicationSuccess} | R Documentation |
A confidence interval by inverting the harmonic mean chi-squared test based on study-specific estimates and standard errors.
hMeanChiSqCI(thetahat, se, w=rep(1, length(thetahat)), alternative="two.sided", level=0.95)
thetahat |
A vector of parameter estimates. |
se |
A vector of standard errors. |
w |
A vector of weights. |
alternative |
Either |
level |
The level of the confidence interval. Defaults to 0.95. |
If alternative
is "none"
, then the function may return a
set of (non-overlapping) confidence intervals. The output then is a vector
of length 2n where n is the number of confidence intervals.
The p-value from the harmonic mean chi-squared test
Leonhard Held
Held, L. (2020). The harmonic mean chi-squared test to substantiate scientific findings. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69, 697-708. https://doi.org/10.1111/rssc.12410
## Example from Fisher (1999) as discussed in Held (2020) pvalues <- c(0.0245, 0.1305, 0.00025, 0.2575, 0.128) lower <- c(0.04, 0.21, 0.12, 0.07, 0.41) upper <- c(1.14, 1.54, 0.60, 3.75, 1.27) se <- ci2se(lower, upper, ratio=TRUE) estimate <- ci2estimate(lower, upper, ratio=TRUE) ## two-sided CI1 <- hMeanChiSqCI(thetahat=estimate, se=se, w=1/se^2, alternative="two.sided") CI2 <- hMeanChiSqCI(thetahat=estimate, se=se, w=1/se^2, alternative="two.sided", level=0.99875) ## one-sided CI1b <- hMeanChiSqCI(thetahat=estimate, se=se, w=1/se^2, alternative="less", level=0.975) CI2b <- hMeanChiSqCI(thetahat=estimate, se=se, w=1/se^2, alternative="less", level=1-0.025^2) ## confidence intervals on hazard ratio scale print(round(exp(CI1),2)) print(round(exp(CI2),2)) print(round(exp(CI1b),2)) print(round(exp(CI2b),2))