dosresmeta (version 1.3.0)

grl: Approximating effective-counts as proposed by Greenland & Longnecker

Description

The function grl 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 1992 by Greenland and Longnecker.

Usage

grl(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

cases

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

n

a vector to specify the total number of subjects 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 margins of the table of pseudo-counts equal to the margins of the crude or unadjusted data (Greenland and Longnecker 1992). 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

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

hamling, dosresmeta

Examples

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