grl {dosresmeta}

Approximating effective-counts as proposed by Greenland & Longnecker

Description

Reconstructs the set of pseudo-numbers (or 'effective' numbers) of cases and non-cases consistent with the input data (log relative risks). The method was first proposed in 1992 by Greenland and Longnecker.

Usage

grl(y, v, cases, n, type, data, tol = 1e-05)

Arguments

y

a vector, defining the (reported) log relative risks.

v

a vector, defining the variances of the reported log relative risks.

cases

a vector, defining the number of cases for each exposure level.

n

a vector, defining the total number of subjects for each exposure level. For incidence-rate data n indicates the amount of person-time within each exposure level.

type

a vector (or a character string), specifying the design of the study. Options are cc, ir, and ci, for case-control, incidence-rate, and cumulative incidence data, respectively.

data

an optional data frame (or object coercible by as.data.frame to a data frame) containing the variables in the previous arguments.

tol

define the tolerance.

Details

The function reconstructs the effective counts corresponding to the multivariable adjusted log relative risks as well as their standard errors. A unique solution is guaranteed by keeping the margins of the table of pseudo-counts equal to the margins of the crude or unadjusted data (Greenland and Longnecker 1992). See the referenced article for a complete description of the algorithm implementation.

Value

The results are returned structured in a matrix

A	approximated number of effective cases.
N	approximated total number of effective subjects.

Author(s)

Alessio Crippa, [e-mail]

References

Greenland, S., Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American journal of epidemiology, 135(11), 1301-1309.

Orsini, N., Li, R., Wolk, A., Khudyakov, P., Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. American journal of epidemiology, 175(1), 66-73.

See Also

covar.logrr, hamling

Examples

## Loading data
data("alcohol_cvd")

## Obtaining pseudo-counts for the first study (id = 1)
grl(y = logrr, v = I(se^2), cases = cases, n = n, type = type,
   data = subset(alcohol_cvd, id == 1))
   
## Obtaining pseudo-counts for all study
by(alcohol_cvd, alcohol_cvd$id, function(x)
   grl(y = logrr, v = I(se^2), cases = cases, n = n, type = type, data = x))

## Restructuring the previous results in a matrix
do.call("rbind", by(alcohol_cvd, alcohol_cvd$id, function(x)
   grl(y = logrr, v = I(se^2), cases = cases, n = n, type = type, data = x)))