Description Usage Arguments Details Examples
Produces table showing the proportion of true effect sizes more extreme than q
across a grid of bias parameters muB
and sigB
(for meas == "prop"
).
Alternatively, produces a table showing the minimum bias factor (for meas == "Tmin"
)
or confounding strength (for meas == "Gmin"
) required to reduce to less than
r
the proportion of true effects more extreme than q
.
1  sens_table(meas, q, r = seq(0.1, 0.9, 0.1), muB = NA, sigB = NA, yr, t2)

meas 

q 
True effect size that is the threshold for "scientific significance" 
r 
For 
muB 
Mean bias factor on the log scale across studies 
sigB 
Standard deviation of log bias factor across studies 
yr 
Pooled point estimate (on log scale) from confounded metaanalysis 
t2 
Estimated heterogeneity (tau^2) from confounded metaanalysis 
For meas=="Tmin"
or meas=="Gmin"
, arguments muB
and
sigB
can be left NA
; r
can also be NA
as
it will default to a reasonable range of proportions. Returns a data.frame
whose rows are values of muB
(for meas=="prop"
) or of r
(for meas=="Tmin"
or meas=="Gmin"
). Its columns are values of
sigB
(for meas=="prop"
) or of q
(for meas=="Tmin"
or meas=="Gmin"
).
Tables for Gmin
will display NaN
for cells corresponding to Tmin
<1,
i.e., for which no bias is required to reduce the effects as specified.
1 2 3 4 5 6 7 8 9 10 11  sens_table( meas="prop", q=log(1.1), muB=c( log(1.1),
log(1.5), log(2.0) ), sigB=c(0, 0.1, 0.2),
yr=log(2.5), t2=0.1 )
sens_table( meas="Tmin", q=c( log(1.1), log(1.5) ),
yr=log(1.3), t2=0.1 )
# Tmin is 1 here because we already have <80% of effects
# below log(1.1) even without any confounding
sens_table( meas="Gmin", r=0.8, q=c( log(1.1) ),
yr=log(1.3), t2=0.1 )

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