dosresmeta (version 2.0.1)

hamling: Approximating effective-counts as proposed by Hamling

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 2008 by Hamling.

Usage

hamling(y, v, cases, n, type, data)

Value

A list containing the following

y	mean or standardized mean differences for each treatment level, included the referent one (0 by calculation).
v	variances corresponding to the mean or standardized mean differences for each treatment level, included the referent one (0 by calculation)
S	co(variance) matrix for the non-referent mean or standardized mean differences.

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.

Author

Alessio Crippa,

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 ratio non-cases to cases and the fraction of unexposed subjects equal to the unadjusted data (Hamling). See the referenced article for a complete description of the algorithm implementation.

References

Hamling, J., Lee, P., Weitkunat, R., Ambuhl, M. (2008). Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Statistics in medicine, 27(7), 954-970.

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, grl

Examples

## Loading data
data("alcohol_cvd")

## Obtaining pseudo-counts for the first study (id = 1)
hamling(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)
hamling(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)
   hamling(y = logrr, v = I(se^2), cases = cases, n = n, type = type,
      data = x)))