orbbound: Sensitivity Analysis for Outcome Reporting Bias (ORB)

In metasens: Statistical Methods for Sensitivity Analysis in Meta-Analysis

Sensitivity Analysis for Outcome Reporting Bias (ORB)

Description

Implementation of the method by Copas & Jackson (2004) to evaluate outcome reporting bias in meta-analysis. An upper bound for outcome reporting bias is estimated for a given number of studies suspected with outcome reporting bias.

Usage

orbbound(x, k.suspect = 1, tau = x$tau, left = NULL, backtransf = x$backtransf)

Arguments

x	An object of class meta.
k.suspect	Number of studies with suspected outcome reporting bias.
tau	Square-root of between-study variance tau-squared.
left	A logical indicating whether the cause of any selection bias is due to missing studies on the left or right of the funnel plot: left hand side if left=TRUE, right hand side if left=FALSE. If not set, the linear regression test for funnel plot asymmetry (i.e., function metabias(..., meth="linreg")) is used to determine whether studies are missing on the left or right hand side.
backtransf	A logical indicating whether results should be back transformed in printouts and plots. If backtransf=TRUE (default), results for sm="OR" are printed as odds ratios rather than log odds ratio, for example.

Details

This function provides the method by Copas and Jackson (2004) to estimate an upper bound for bias for a given number of studies with suspected outcome reporting bias.

Based on the upper bound of outcome reporting bias, treatment estimates and confidence limits adjusted for bias are calculated.

For comparison, the original meta-analysis is always considered in the sensitivity analysis (i.e. value 0 is always added to k.suspect).

Value

An object of class c("orbbound") with corresponding print and forest function. The object is a list containing the following components:

k.suspect, tau	As defined above.
maxbias	Maximum bias for given values of k.suspect.
common	Adjusted treatment estimates and corresponding quantities for common effect model (a list with elements TE, seTE, lower, upper, z, p, level, df).
random	Adjusted treatment estimates and corresponding quantities for random effects model (a list with elements TE, seTE, lower, upper, z, p, level, df).
left	Whether selection bias expected on left or right
x	Meta-analysis object (i.e. argument x from function call).
call	Function call.
version	Version of R package metasens used to create object.

Author(s)

Guido Schwarzer [e-mail]

References

Copas J, Jackson D (2004): A bound for publication bias based on the fraction of unpublished studies. Biometrics, 60, 146–53

See Also

forest.orbbound, print.orbbound

Examples

data(Fleiss1993bin, package = "meta")

m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin, sm = "OR")

orb1 <- orbbound(m1, k.suspect = 1:5)
print(orb1, digits = 2)
forest(orb1, xlim = c(0.75, 1.5))

# Same result
#
orb2 <- orbbound(m1, k.suspect = 1:5, left = FALSE)
print(orb2, digits = 2)

# Assuming bias in other direction
#
orb3 <- orbbound(m1, k.suspect = 1:5, left = TRUE)
print(orb3, digits = 2)

metasens documentation built on March 7, 2023, 7:51 p.m.