graphPAF (version 2.0.0)

paf_levin: Implementation of Levin's formula for summary data

Description

Implementation of Levin's formula for summary data

Usage

paf_levin(prev = NULL, RR = NULL, conf_prev = NULL, conf_RR = NULL, digits = 3)

Value

If confidence intervals for prevalence and relative risk are not specified, the estimated PAF. If confidence intervals for prevalence and relative risk are specified, confidence intervals for PAF are estimated using approximate propagation of imprecision. Note that if confidence intervals are supplied as arguments, the algorithm makes assumptions that the point estimate of prevalence is the average of the specified confidence limits for prevalence, that the point estimate for relative risk is the geometric mean of the confidence limits for relative risk, and that the 3 estimators are independent.

Arguments

prev

A vector of estimated prevalence for each non-reference level of risk factor. Can be left unspecified if conf_prev specified.

RR

A vector of estimated relative risk for each non-reference level of risk factor. Can be left unspecified if conf_RR specified.

conf_prev

If risk factor has 2 levels, a numeric vector of length 2 giving confidence limits for prevalence. If risk factor has K>2 levels, a K-1 x 2 matrix giving confidence intervals for prevalence of each non-reference level of risk factor.

conf_RR

If risk factor has 2 levels, a numeric vector of length 2 giving confidence limits for relative risk. If risk factor has K>2 levels, a K-1 x 2 matrix giving confidence intervals for relative risk for each non-reference level of risk factor

digits

integer. The number of significant digits for rounding of PAF estimates and confidence intervals. Default of 3.

References

Ferguson, J., Alvarez-Iglesias, A., Mulligan, M., Judge, C. and O’Donnell, M., 2024. Bias assessment and correction for Levin's population attributable fraction under confounding. European Journal of Epidemiology, In press

Examples

CI_p <- c(0.1,0.3)
CI_RR <- c(1.2, 2)
# calculation without confidence interval
paf_levin(prev=0.2,RR=exp(.5*log(1.2)+.5*log(2)))
# calculation with confidence interval
paf_levin(conf_prev=CI_p,conf_RR=CI_RR)
# add another level to risk factor
# with higher prevalence and RR
# this will increase the PAF
CI_p <- matrix(c(0.1,0.3,0.15, 0.25),nrow=2)
CI_RR <- matrix(c(1.2,2,1.5,3),nrow=2)
paf_levin(conf_prev=CI_p,conf_RR=CI_RR)