Description Usage Arguments Value Note Author(s) References See Also Examples
Function that calculates the Population Attributable Fraction
paf
with linear Relative Risk function rr
given by
rr(X, theta) = theta[1] + theta[2]*X[,1] + theta[3]*X[,2] + ... + theta[n+1]*X[,n].
1 2 3 4 5  paf.linear(X, thetahat, method = "empirical",
weights = rep(1/nrow(as.matrix(X)), nrow(as.matrix(X))), Xvar = var(X),
deriv.method.args = list(), deriv.method = c("Richardson", "complex"),
adjust = 1, n = 512, ktype = "gaussian", bw = "SJ",
check_exposure = TRUE, check_rr = TRUE, check_integrals = TRUE)

X 
Random sample ( 
thetahat 
Asymptotically consistent or Fisher consistent estimator ( **Optional** 
method 
Either 
weights 
Normalized survey 
Xvar 
Variance of exposure levels (for 
deriv.method.args 

deriv.method 

adjust 
Adjust bandwith parameter (for 
n 
Number of equally spaced points at which the density (for

ktype 

bw 
Smoothing bandwith parameter (for

check_exposure 

check_rr 

check_integrals 

paf Estimate of Population Attributable Fraction with linear relative risk.
The "approximate"
method should be the last choice. In practice
"empirical"
should be preferred as convergence is faster than
"kernel"
for most functions. In addition, the scope of
"kernel"
is limited as it does not work with multivariate exposure
data X
.
paf.linear
is a wrapper for paf
with linear
relative risk.
Rodrigo ZepedaTello [email protected]
Dalia CamachoGarc<c3><ad>aForment<c3><ad> [email protected]
Vander Hoorn, S., Ezzati, M., Rodgers, A., Lopez, A. D., & Murray, C. J. (2004). Estimating attributable burden of disease from exposure and hazard data. Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. Geneva: World Health Organization, 212940.
See paf
for Population Attributable Fraction (with
arbitrary relative risk), and pif
for Potential Impact Fraction
estimation.
See paf.exponential
for PAF with readytouse exponential
relative risk function.
For more information on kernels see density
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  #Example 1: Univariate relative risk
#
set.seed(18427)
X < data.frame(Exposure = rnorm(100,3,.5))
thetahat < c(1, 0.12) #Linear risk given by 1 + 0.12*X
paf.linear(X, thetahat)
#This is the same as doing:
paf(X, thetahat, rr = function(X, theta){X*theta[2] + theta[1]})
#Same example with kernel method
paf.linear(X, thetahat, method = "kernel")
#Same example with approximate method
Xmean < data.frame(mean(X[,"Exposure"]))
Xvar < var(X)
paf.linear(Xmean, thetahat, method = "approximate", Xvar = Xvar)
#Example 2: Multivariate relative risk
#
X < data.frame(Exposure = rnorm(100,2,.7), Covariate = rnorm(100,4,1))
theta < c(1, 0.3,0.1)
paf.linear(X, theta) #Linear risk given by 1 + 0.3*X1 + 0.1*X2
#Example 3: Polynomial relative risk
#
X < runif(100)
X2 < X^2
X3 < X^3
matX < data.frame(X,X2,X3)
theta < c(1, 0.3,0.1, 0.4)
paf.linear(matX,theta) #Polynomial risk: 1 + 0.3*X + 0.1*X^2 + 0.4*X^3

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