ConfoundedMeta (version 1.3.0)

confounded_meta: Estimates and inference for sensitivity analyses

Description

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).

Usage

confounded_meta(.q, .r = NULL, .muB = NULL, .sigB = 0, .yr, .vyr = NULL,
  .t2, .vt2 = NULL, CI.level = 0.95, .tail = NULL)

Arguments

.q

True effect size that is the threshold for "scientific significance"

.r

For Tmin and Gmin, value to which the proportion of large effect sizes is to be reduced

.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 meta-analysis

.vyr

Estimated variance of pooled point estimate from confounded meta-analysis

.t2

Estimated heterogeneity (tau^2) from confounded meta-analysis

.vt2

Estimated variance of tau^2 from confounded meta-analysis

CI.level

Confidence level as a proportion

.tail

above for the proportion of effects above .q; below for the proportion of effects below .q. By default, is set to above for relative risks above 1 and to below for relative risks below 1.

Details

To compute all three point estimates (prop, Tmin, and Gmin) and inference, all arguments must be non-NULL. 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.

Examples

# NOT RUN {
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 )
# }