hMeanChiSq: harmonic mean chi-squared test

In ReplicationSuccess: Design and Analysis of Replication Studies

harmonic mean chi-squared test

Description

p-values and confidence intervals from the harmonic mean chi-squared test.

Usage

hMeanChiSq(
  z,
  w = rep(1, length(z)),
  alternative = c("greater", "less", "two.sided", "none"),
  bound = FALSE
)

hMeanChiSqMu(
  thetahat,
  se,
  w = rep(1, length(thetahat)),
  mu = 0,
  alternative = c("greater", "less", "two.sided", "none"),
  bound = FALSE
)

hMeanChiSqCI(
  thetahat,
  se,
  w = rep(1, length(thetahat)),
  alternative = c("two.sided", "greater", "less", "none"),
  conf.level = 0.95
)

Arguments

z	Numeric vector of z-values.
w	Numeric vector of weights.
alternative	Either "greater" (default), "less", "two.sided", or "none". Specifies the alternative to be considered in the computation of the p-value.
bound	If FALSE (default), p-values that cannot be computed are reported as NaN. If TRUE, they are reported as "> bound".
thetahat	Numeric vector of parameter estimates.
se	Numeric vector of standard errors.
mu	The null hypothesis value. Defaults to 0.
conf.level	Numeric vector specifying the conf.level of the confidence interval. Defaults to 0.95. summarize the gamma values, i.e., the local minima of the p-value function between the thetahats. Defaults is a vector of 1s.

Value

hMeanChiSq: returns the p-values from the harmonic mean chi-squared test based on the study-specific z-values.

hMeanChiSqMu: returns the p-value from the harmonic mean chi-squared test based on study-specific estimates and standard errors.

hMeanChiSqCI: returns a list containing confidence interval(s) obtained by inverting the harmonic mean chi-squared test based on study-specific estimates and standard errors. The list contains:

CI	Confidence interval(s).

If the alternative is "none", the list also contains:

gamma

Local minima of the p-value function between the thetahats.

Author(s)

Leonhard Held, Florian Gerber

References

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssc.12410")}

Examples

## 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 = lower, upper = upper, ratio = TRUE)
thetahat <- ci2estimate(lower = lower, upper = upper, ratio = TRUE)

## hMeanChiSq() --------
hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           alternative = "less")
hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           alternative = "two.sided")
hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           alternative = "none")

hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           w = 1 / se^2, alternative = "less")
hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           w = 1 / se^2, alternative = "two.sided")
hMeanChiSq(z = p2z(p = pvalues, alternative = "less"),
           w = 1 / se^2, alternative = "none")


## hMeanChiSqMu() --------
hMeanChiSqMu(thetahat = thetahat, se = se, alternative = "two.sided")
hMeanChiSqMu(thetahat = thetahat, se = se, w = 1 / se^2,
             alternative = "two.sided")
hMeanChiSqMu(thetahat = thetahat, se = se, alternative = "two.sided",
             mu = -0.1)

## hMeanChiSqCI() --------
## two-sided
CI1 <- hMeanChiSqCI(thetahat = thetahat, se = se, w = 1 / se^2,
                    alternative = "two.sided")
CI2 <- hMeanChiSqCI(thetahat = thetahat, se = se, w = 1 / se^2,
                    alternative = "two.sided", conf.level = 0.99875)
## one-sided
CI1b <- hMeanChiSqCI(thetahat = thetahat, se = se, w = 1 / se^2,
                     alternative = "less", conf.level = 0.975)
CI2b <- hMeanChiSqCI(thetahat = thetahat, se = se, w = 1 / se^2,
                     alternative = "less", conf.level = 1 - 0.025^2)

## confidence intervals on hazard ratio scale
print(exp(CI1$CI), digits = 2)
print(exp(CI2$CI), digits = 2)
print(exp(CI1b$CI), digits = 2)
print(exp(CI2b$CI), digits = 2)


## example with confidence region consisting of disjunct intervals
thetahat2 <- c(-3.7, 2.1, 2.5)
se2 <- c(1.5, 2.2, 3.1)
conf.level <- 0.95; alpha <- 1 - conf.level
muSeq <- seq(-7, 6, length.out = 1000)
pValueSeq <- hMeanChiSqMu(thetahat = thetahat2, se = se2,
                          alternative = "none", mu = muSeq)
(hm <- hMeanChiSqCI(thetahat = thetahat2, se = se2, alternative = "none"))

plot(x = muSeq, y = pValueSeq, type = "l", panel.first = grid(lty = 1),
     xlab = expression(mu), ylab = "p-value")
abline(v = thetahat2, h = alpha, lty = 2)
arrows(x0 = hm$CI[, 1], x1 = hm$CI[, 2], y0 = alpha,
       y1 = alpha, col = "darkgreen", lwd = 3, angle = 90, code = 3)
points(hm$gamma, col = "red", pch = 19, cex = 2)

ReplicationSuccess documentation built on April 3, 2023, 5:11 p.m.