confounded_meta {ConfoundedMeta}  R Documentation 
Computes point estimates, standard errors, and confidence interval bounds
for (1) prop
, the proportion of studies with true effect sizes above .q
(or below
.q
for an apparently preventive .yr
) as a function of the bias parameters;
(2) the minimum bias factor on the relative risk scale (Tmin
) required to reduce to
less than .r
the proportion of studies with true effect sizes more extreme than
.q
; and (3) the counterpart to (2) in which bias is parameterized as the minimum
relative risk for both confounding associations (Gmin
).
confounded_meta(.q, .r = NULL, .muB = NULL, .sigB = 0, .yr, .vyr = NULL, .t2, .vt2 = NULL, CI.level = 0.95, .tail = NULL)
.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 
.vyr 
Estimated variance of pooled point estimate from confounded metaanalysis 
.t2 
Estimated heterogeneity (tau^2) from confounded metaanalysis 
.vt2 
Estimated variance of tau^2 from confounded metaanalysis 
CI.level 
Confidence level as a proportion 
.tail 

To compute all three point estimates (prop, Tmin, and Gmin
) and inference, all
arguments must be nonNULL
. To compute only a point estimate for prop
,
arguments .r, .vyr
, and .vt2
can be left NULL
. To compute only
point estimates for Tmin
and Gmin
, arguments .muB, .vyr
, and .vt2
can be left NULL
. To compute inference for all point estimates, .vyr
and
.vt2
must be supplied.
d = metafor::escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=metafor::dat.bcg) m = metafor::rma.uni(yi= d$yi, vi=d$vi, knha=FALSE, measure="RR", method="DL" ) yr = as.numeric(m$b) # metafor returns on log scale vyr = as.numeric(m$vb) t2 = m$tau2 vt2 = m$se.tau2^2 # obtaining all three estimators and inference confounded_meta( .q=log(0.90), .r=0.20, .muB=log(1.5), .sigB=0.1, .yr=yr, .vyr=vyr, .t2=t2, .vt2=vt2, CI.level=0.95 ) # passing only arguments needed for prop point estimate confounded_meta( .q=log(0.90), .muB=log(1.5), .yr=yr, .t2=t2, CI.level=0.95 ) # passing only arguments needed for Tmin, Gmin point estimates confounded_meta( .q=log(0.90), .r=0.20, .yr=yr, .t2=t2, CI.level=0.95 )