frag.ma.alpha: Assessing Fragility of a Meta-Analysis at Different...

In fragility: Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes

Assessing Fragility of a Meta-Analysis at Different Significance Levels

Description

Produces fragility index or fragility quotient for altering statistical significance of a meta-analysis with a binary outcome at different significance levels.

Usage

frag.ma.alpha(e0, n0, e1, n1, data, measure = "OR",
              alpha.from = 0.005, alpha.to = 0.05, alpha.breaks = 100,
              mod.dir = "both", OR = 1, RR = 1, RD = 0,
              method = "DL", test = "z", ...)

Arguments

e0	a numeric vector or the corresponding column name in the argument data, indicating event counts in group 0 among the studies in the meta-analysis.
n0	a numeric vector or the corresponding column name in the argument data, indicating sample sizes in group 0 among the studies in the meta-analysis.
e1	a numeric vector or the corresponding column name in the argument data, indicating event counts in group 1 among the studies in the meta-analysis.
n1	a numeric vector or the corresponding column name in the argument data, indicating sample sizes in group 1 among the studies in the meta-analysis.
data	an optional data frame containing the dataset of the collected studies in the meta-analysis with a binary outcome. If data is specified, the previous arguments, e0, n0, e1, and n1, should be specified as their corresponding column names in data.
measure	a character string indicating the measure of treatment effect (i.e., effect size) for the binary outcome. It should be one of "OR" (odds ratio, the default), "RR" (relative risk), and "RD" (risk difference).
alpha.from	a numeric value between 0 and 1, indicating the smallest value of the statistical significance levels to be considered (the default is 0.005). It should be smaller than the argument alpha.to.
alpha.to	a numeric value between 0 and 1, indicating the largest value of the statistical significance levels to be considered (the default is 0.05). It should be larger than the argument alpha.from.
alpha.breaks	a positive integer indicating the number of statistical significance levels to be considered (the default is 100). The candidate significance levels are thus equally-spaced between alpha.from and alpha.to.
mod.dir	a character string indicating the direction of the confidence interval change due to event status modifications when the original confidence interval covers the null value (i.e., non-significance altered to significance). It is not used when significance is altered to non-significance. It should be one of "left" (moving to the left side of the null value), "right" (moving to the right side of the null value), "one" (based on the direction of the point estimate of the original overall effect size), and "both" (both directions, the default).
OR	a numeric positive value indicating the value of odds ratio under the null hypothesis (the default is 1). It is used only if the argument measure is "OR".
RR	a numeric positive value indicating the value of relative risk under the null hypothesis (the default is 1). It is used only if the argument measure is "RR".
RD	a numeric value between -1 indicating the value of risk difference under the null hypothesis (the default is 0). It is used only if the argument measure is "RD".
method	a character string specifying the method for performing meta-analysis. It is the same with the argument in frag.ma, and is passed to rma.uni. See details in the manual of the package metafor.
test	a character string specifying how confidence intervals are derived. It is the same with the argument in frag.ma, and is passed to rma.uni. See details in the manual of the package metafor.
...	other arguments that can be passed to rma.uni. See details in the manual of the package metafor.

Value

An object of classes "frag.alpha" and "frag.ma.alpha". The object is a list containing the following components:

data	original data in the form of a data frame with six columns: event counts, non-event counts, and sample sizes in group 0 and those in group 1 among all studies in the meta-analysis.
measure	measure of treatment effect (i.e., effect size).
alphas	different statistical significance levels.
null	value of odds ratio, relative risk, or risk difference (specified by measure) under the null hypothesis. Note that odds ratio and relative risk are presented on a natural logarithmic scale in all output values.
est.ori	the point estimate of the overall effect size based on the original meta-analysis.
se.ori	the standard error of the overall effect size based on the original meta-analysis.
test	the method used to derive confidence intervals by the function rma.uni. It is one of "z", "t", "knha" (Hartung–Knapp–Sidik–Jonkman method), and "adhoc"; the first indicates Wald-type confidence intervals based on the standard normal distribution, while the latter three yield confidence intervals based on the t-distribution.
pval.ori	the p-value of the overall effect size based on the original meta-analysis.
mod.dir	the direction of the confidence interval change due to event status modifications when the original confidence interval covers the null value (i.e., non-significance altered to significance).
FI	fragility indexes at different statistical significance levels in alphas.
FI.avg	average fragility index.
FQ	fragility quotients at different statistical significance levels in alphas.
FQ.avg	average fragility quotient.

References

Benjamin DJ, Berger JO, Johannesson M, Nosek BA, Wagenmakers EJ, Berk R, Bollen KA, Brembs B, Brown L, Camerer C, Cesarini D, Chambers CD, Clyde M, Cook TD, De Boeck P, Dienes Z, Dreber A, Easwaran K, Efferson C, Fehr E, Fidler F, Field AP, Forster M, George EI, Gonzalez R, Goodman S, Green E, Green DP, Greenwald AG, Hadfield JD, Hedges LV, Held L, Ho TH, Hoijtink H, Hruschka DJ, Imai K, Imbens G, Ioannidis JPA, Jeon M, Jones JH, Kirchler M, Laibson D, List J, Little R, Lupia A, Machery E, Maxwell SE, McCarthy M, Moore DA, Morgan SL, Munafo M, Nakagawa S, Nyhan B, Parker TH, Pericchi L, Perugini M, Rouder J, Rousseau J, Savalei V, Schonbrodt FD, Sellke T, Sinclair B, Tingley D, Van Zandt T, Vazire S, Watts DJ, Winship C, Wolpert RL, Xie Y, Young C, Zinman J, Johnson VE (2018). "Redefine statistical significance." Nature Human Behaviour, 2, 6–10. <doi: 10.1038/s41562-017-0189-z>

Ioannidis JPA (2018). "The proposal to lower P value thresholds to .005." JAMA, 319(14), 1429–30. <doi: 10.1001/jama.2018.1536>

Lin L, Chu H (2022). "Assessing and visualizing fragility of clinical results with binary outcomes in R using the fragility package." PLOS ONE, 17(6), e0268754. <doi: 10.1371/journal.pone.0268754>

See Also

frag.ma for assessing fragility of a meta-analysis at a specific significance level; and frag.study.alpha for assessing fragility of an individual study at multiple significance levels

Examples

## Load datasets of meta-analyses on nutrition support
data(dat.ns)

## Assess fragility of the 11th meta-analysis
##  at multiple significance levels
out1 <- frag.ma.alpha(e0, n0, e1, n1, data = dat.ns[dat.ns$ma.id == 11,],
  alpha.from = 0.01, alpha.to = 0.05, alpha.breaks = 5)
out1

## Generate plot to show fragility measures against significance levels;
##  see more options of usage in examples of frag.study.alpha()
plot(out1)
plot(out1, fragility = "FQ")


## Assess fragility of the 6th meta-analysis
##  at multiple significance levels;
##  it takes longer time
out2 <- frag.ma.alpha(e0, n0, e1, n1, data = dat.ns[dat.ns$ma.id == 6,])
out2
plot(out2)

fragility documentation built on Aug. 30, 2022, 1:10 a.m.