to.table: Convert Data from Vector to Table Format

In metafor: Meta-Analysis Package for R

Convert Data from Vector to Table Format

Description

Function to convert summary data in vector format to the corresponding table format. \loadmathjax

Usage

to.table(measure, ai, bi, ci, di, n1i, n2i, x1i, x2i, t1i, t2i,
         m1i, m2i, sd1i, sd2i, xi, mi, ri, ti, sdi, ni, data, slab, subset,
         add=1/2, to="none", drop00=FALSE, rows, cols)

Arguments

measure	a character string to specify the effect size or outcome measure corresponding to the summary data supplied. See ‘Details’ and the documentation of the escalc function for possible options.
ai	vector with the \mjeqn2 \times 22x2 table frequencies (upper left cell).
bi	vector with the \mjeqn2 \times 22x2 table frequencies (upper right cell).
ci	vector with the \mjeqn2 \times 22x2 table frequencies (lower left cell).
di	vector with the \mjeqn2 \times 22x2 table frequencies (lower right cell).
n1i	vector with the group sizes or row totals (first group/row).
n2i	vector with the group sizes or row totals (second group/row).
x1i	vector with the number of events (first group).
x2i	vector with the number of events (second group).
t1i	vector with the total person-times (first group).
t2i	vector with the total person-times (second group).
m1i	vector with the means (first group or time point).
m2i	vector with the means (second group or time point).
sd1i	vector with the standard deviations (first group or time point).
sd2i	vector with the standard deviations (second group or time point).
xi	vector with the frequencies of the event of interest.
mi	vector with the frequencies of the complement of the event of interest or the group means.
ri	vector with the raw correlation coefficients.
ti	vector with the total person-times.
sdi	vector with the standard deviations.
ni	vector with the sample/group sizes.
data	optional data frame containing the variables given to the arguments above.
slab	optional vector with labels for the studies.
subset	optional (logical or numeric) vector to specify the subset of studies that should be included in the array returned by the function.
add	see the documentation of the escalc function.
to	see the documentation of the escalc function.
drop00	see the documentation of the escalc function.
rows	optional vector with row/group names.
cols	optional vector with column/outcome names.

Details

The escalc function describes a wide variety of effect sizes or outcome measures that can be computed for a meta-analysis. The summary data used to compute those measures are typically contained in vectors, each element corresponding to a study. The to.table function takes this information and constructs an array of \mjseqnk tables from these data.

For example, in various fields (such as the health and medical sciences), the response variable measured is often dichotomous (binary), so that the data from a study comparing two different groups can be expressed in terms of a \mjeqn2 \times 22x2 table, such as:

	\ics	outcome 1	\ics	outcome 2	\ics	total
group 1	\ics	ai	\ics	bi	\ics	n1i
group 2	\ics	ci	\ics	di	\ics	n2i

where ai, bi, ci, and di denote the cell frequencies (i.e., the number of individuals falling into a particular category) and n1i and n2i the row totals (i.e., the group sizes).

The cell frequencies in \mjseqnk such \mjeqn2 \times 22x2 tables can be specified via the ai, bi, ci, and di arguments (or alternatively, via the ai, ci, n1i, and n2i arguments). The function then creates the corresponding \mjeqn2 \times 2 \times k2*2*k array of tables. The measure argument should then be set equal to one of the outcome measures that can be computed based on this type of data, such as "RR", "OR", "RD" (it is not relevant which specific measure is chosen, as long as it corresponds to the specified summary data). See the documentation of the escalc function for more details on the types of data formats available.

The examples below illustrate the use of this function.

Value

An array with \mjseqnk elements each consisting of either 1 or 2 rows and an appropriate number of columns.

Author(s)

Wolfgang Viechtbauer [e-mail] https://www.metafor-project.org

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. ⁠https://doi.org/10.18637/jss.v036.i03⁠

See Also

escalc for a function to compute observed effect sizes or outcomes (and corresponding sampling variances) based on similar inputs.

to.long for a function to turn similar inputs into a long format dataset.

Examples

### create tables
dat <- to.table(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
                data=dat.bcg, slab=paste(author, year, sep=", "),
                rows=c("Vaccinated", "Not Vaccinated"), cols=c("TB+", "TB-"))
dat

### create tables
dat <- to.table(measure="IRR", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i,
                data=dat.hart1999, slab=paste(study, year, sep=", "),
                rows=c("Warfarin Group", "Placebo/Control Group"))
dat

### create tables
dat <- to.table(measure="MD", m1i=m1i, sd1i=sd1i, n1i=n1i,
                m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat.normand1999,
                slab=source, rows=c("Specialized Care", "Routine Care"))
dat

metafor documentation built on May 29, 2024, 7:44 a.m.