dosresmeta (version 1.3.2)

hamling: Approximating effective-counts as proposed by Hamling

Description

The function hamling reconstructs the set of pseudo-numbers (or "effective" numbers) of cases and non-cases consistent with the input data (log relative risks) for either a single or multiple summarized data. The method was proposed in 2008 by Hamling.

Usage

hamling(logrr, v, cases, n, type, id, data, se, lb, ub, order = TRUE, alpha = 0.05)

Arguments

logrr

a vector to specify the reported log relative risks.

v

a vector to specify the variances of the reported log relative risks. Alternatively the user can provide the standard error in the se argument, or the confidence interval for the reported relative risks in the lb and ub arguments.

cases

a vector to specify the number of cases for each exposure level.

n

a vector to specify the total number of subject for each exposure level. For incidence-rate data n indicates the amount of person-time for each exposure level.

type

a vector (or a string) to specify the study-specific design. The values for case-control, incidence-rate, and cumulative incidence data are cc, ir, and ci (or 1, 2, and 3), respectively.

id

an optional vector to specify the id variable for the studies included in the analysis.

data

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

se

an optional vector to specify the standard error of the reported log relative risks; needed if v is not provided.

lb

an optional vector to specify the lower bound of the confidence interval for the reported relative risks; needed if v and se are not provided.

ub

an optional vector to specify the upper bound of the confidence interval for the reported relative risks; needed if v and se are not provided.

order

a logical value to specify if the vectors need to be sorted. See details.

alpha

a scalar to specify the alpha nominal value used in the published data, by defaul equal to .05.

Value

The results are returned structured in a data frame.

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). The function requires the data to be sorted by id and in such a way that the referent values correspond to the first record for each study. This is automatically done by the function when order = TRUE (default).

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.

Examples

data("alcohol_cvd")
hamling(logrr = logrr, se = se, cases = cases, n = n, type = type,
 id = id, data = alcohol_cvd)