hamling {dosresmeta}

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)

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.

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.

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.

Author(s)

Alessio Crippa, [e-mail]

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)))