grl {dosresmeta}  R Documentation 
Reconstructs the set of pseudonumbers (or 'effective' numbers) of cases and noncases consistent with the input data (log relative risks). The method was first proposed in 1992 by Greenland and Longnecker.
grl(y, v, cases, n, type, data, tol = 1e05)
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 incidencerate data 
type 
a vector (or a character string), specifying the design of the study. Options are

data 
an optional data frame (or object coercible by 
tol 
define the tolerance. 
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 pseudocounts 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.
The results are returned structured in a matrix
A  approximated number of effective cases. 
N  approximated total number of effective subjects. 
Alessio Crippa, alessio.crippa@ki.se
Greenland, S., Longnecker, M. P. (1992). Methods for trend estimation from summarized doseresponse data, with applications to metaanalysis. American journal of epidemiology, 135(11), 13011309.
Orsini, N., Li, R., Wolk, A., Khudyakov, P., Spiegelman, D. (2012). Metaanalysis for linear and nonlinear doseresponse relations: examples, an evaluation of approximations, and software. American journal of epidemiology, 175(1), 6673.
## Loading data
data("alcohol_cvd")
## Obtaining pseudocounts 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 pseudocounts 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)))