metafor (version 1.9-2)

to.table: Convert Data from Vector to Table Format

Description

The function can be used to convert summary data in vector format to the corresponding table format.

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 indicating the effect size or outcome measure corresponding to the summary data supplied. See below and the documentation of the escalc function for more details.

ai

vector to specify the $2 \times 2$ table frequencies (upper left cell).

bi

vector to specify the $2 \times 2$ table frequencies (upper right cell).

ci

vector to specify the $2 \times 2$ table frequencies (lower left cell).

di

vector to specify the $2 \times 2$ table frequencies (lower right cell).

n1i

vector to specify the group sizes or row totals (first group/row).

n2i

vector to specify the group sizes or row totals (second group/row).

x1i

vector to specify the number of events (first group).

x2i

vector to specify the number of events (second group).

t1i

vector to specify the total person-times (first group).

t2i

vector to specify the total person-times (second group).

m1i

vector to specify the means (first group or time point).

m2i

vector to specify the means (second group or time point).

sd1i

vector to specify the standard deviations (first group or time point).

sd2i

vector to specify the standard deviations (second group or time point).

xi

vector to specify the frequencies of the event of interest.

mi

vector to specify the frequencies of the complement of the event of interest or the group means.

ri

vector to specify the raw correlation coefficients.

ti

vector to specify the total person-times.

sdi

vector to specify the standard deviations.

ni

vector to specify the sample/group sizes.

data

optional data frame containing the variables given to the arguments above.

slab

optional vector with unique labels for the studies.

subset

optional vector indicating the subset of studies that should be used. This can be a logical vector or a numeric vector indicating the indices of the studies to include.

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.

Value

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

Details

The escalc function describes a wide variety of effect size and 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 $k$ 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 $2 \times 2$ table, such as: lccc{ outcome 1 outcome 2 total group 1 ai bi n1i group 2 ci di n2i } where ai, bi, ci, and di denote the cell frequencies (i.e., the number of people falling into a particular category) and n1i and n2i the row totals (i.e., the group sizes). The cell frequencies in $k$ such $2 \times 2$ 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 $2 \times 2 \times k$ 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.

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.

See Also

escalc, to.long

Examples

### load BCG vaccine data
data(dat.bcg)

### 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

### load data from Hart et al. (1999)
data(dat.hart1999)

### 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

### load data from Normand (1999)
data(dat.normand1999)

### 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