Description Usage Arguments Author(s) Examples
Confidence intervals for the Population Attributable Fraction for the approximate method where only mean and variance from a previous study is available.For relative risk inyective functions, the pif is inyective, and intervals can be calculated for log(pif), and then transformed to pif CI.
1 2 3 4 5 |
Xmean |
Mean value of exposure levels from a cross-sectional. |
Xvar |
Variance of the exposure levels. |
thetahat |
Estimator (vector or matrix) of |
thetavar |
Estimator of variance of |
rr |
Function for Relative Risk which uses parameter **Optional** |
cft |
Differentiable function |
deriv.method.args |
|
deriv.method |
|
check_exposure |
Check that exposure |
check_rr |
Check that Relative Risk function |
check_integrals |
Check that counterfactual and relative risk's expected values are well defined for this scenario. |
nsim |
Number of simulations for estimation of variance |
confidence |
Concidence level (0 to 100) default = |
check_thetas |
Checks that theta parameters are correctly inputed |
is_paf |
Boolean forcing evaluation of |
Rodrigo Zepeda-Tello rzepeda17@gmail.com
Dalia Camacho-GarcĂa-FormentĂ daliaf172@gmail.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #Example 1: Exponential Relative Risk
#--------------------------------------------
set.seed(46987)
rr <- function(X,theta){exp(X*theta)}
cft <- function(X){0.4*X}
Xmean <- data.frame(3)
Xvar <- 1
theta <- 0.4
thetavar <- 0.001
pif.confidence.approximate.loglinear(Xmean, Xvar, theta, thetavar, rr, cft,
nsim = 1000)
#Example 2: Multivariate Relative Risk
#--------------------------------------------
X1 <- rnorm(100,3,.5)
X2 <- rnorm(100,4,1)
X <- data.frame(cbind(X1,X2))
Xmean <- t(as.matrix(colMeans(X)))
Xvar <- cov(X)
thetahat <- c(0.12, 0.17)
thetavar <- matrix(c(0.001, 0.00001, 0.00001, 0.004), byrow = TRUE, nrow = 2)
rr <- function(X, theta){exp(theta[1]*X[,1] + theta[2]*X[,2])}
pif.confidence.approximate.loglinear(Xmean, Xvar, thetahat, thetavar,
rr, cft = function(X){0.8*X}, nsim = 100)
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