metasens (version 1.5-3)

summary.copas: Summary method for Copas selection model

Description

Summary method for objects of class copas.

Usage

# S3 method for copas
summary(object, ...)

Value

An object of class "summary.copas" with corresponding print function. The object is a list containing the following components:

slope

Results for points on orthogonal line (a list with elements TE, seTE, lower, upper, statistic, p, level).

publprob

Vector of probabilities of publishing the smallest study.

pval.rsb

P-values for tests on presence of residual selection bias

N.unpubl

Approximate number of studies the model suggests remain unpublished

adjust

Result of Copas selection model adjusted for selection bias (a list with elements TE, seTE, lower, upper, statistic, p, level).

sign.rsb

The significance level for the test of residual selection bias.

pval.rsb.adj

P-value for test on presence of residual selection bias for adjusted effect given in adjust.

N.unpubl.adj

Approximate number of studies the model suggests remain unpublished for adjusted effect given in adjust

random

Results for usual random effects model (a list with elements TE, seTE, lower, upper, statistic, p, level).

sm

A character string indicating underlying summary measure.

ci.lab

Label for confidence interval.

title

Title of meta-analysis / systematic review.

complab

Comparison label.

outclab

Outcome label.

version

Version of R package metasens used to create object.

Arguments

object

An object of class copas.

...

other arguments to the function will be ignored (this option included only to conform with R standards)

Author

James Carpenter , Guido Schwarzer

Details

This function complements the graphical summary of the results of a Copas selection model, generated using plot.copas.

See Also

copas, plot.copas, metabias, metagen

Examples

data(Fleiss1993bin, package = "meta")

# Perform meta analysis, effect measure is odds ratio (OR)
#
m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin, sm = "OR")

# Print summary of Copas analysis
#
summary(copas(m1, level.ma = 0.95))