ConfoundedMeta (version 1.3.0)

sens_table: Tables for sensitivity analyses

Description

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.

Usage

sens_table(.meas, .q, .r = seq(0.1, 0.9, 0.1), .muB = NULL, .sigB = NULL,
  .yr, .t2)

Arguments

.meas

prop, Tmin, or Gmin

.q

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

.r

For Tmin and Gmin, vector of values 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

.t2

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

Details

For .meas=="Tmin" or .meas=="Gmin", arguments .muB and .sigB can be left NULL; .r can also be NULL 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.

Examples

# NOT RUN {
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 ) 

# will have NaNs in cells with Tmin < 1 (no bias needed)
sens_table( .meas="Gmin", .r=0.8, .q=c( log(1.1) ),
.yr=log(1.3), .t2=0.1 )
# }