AF.cs {AF} | R Documentation |
Attributable fraction for cross-sectional sampling designs. NOTE! Deprecated function. Use AFglm
.
Description
AF.cs
estimates the model-based adjusted attributable fraction for data from cross-sectional sampling designs.
Usage
AF.cs(formula, data, exposure, clusterid)
Arguments
formula |
an object of class " |
data |
an optional data frame, list or environment (or object coercible by |
exposure |
the name of the exposure variable as a string. The exposure must be binary (0/1) where unexposed is coded as 0. |
clusterid |
the name of the cluster identifier variable as a string, if data are clustered. |
Details
Af.cs
estimates the attributable fraction for a binary outcome Y
under the hypothetical scenario where a binary exposure X
is eliminated from the population.
The estimate is adjusted for confounders Z
by logistic regression (glm
).
Let the AF be defined as
AF=1-\frac{Pr(Y_0=1)}{Pr(Y=1)}
where Pr(Y_0=1)
denotes the counterfactual probability of the outcome if
the exposure would have been eliminated from the population and Pr(Y = 1)
denotes the factual probability of the outcome.
If Z
is sufficient for confounding control, then Pr(Y_0=1)
can be expressed as
E_Z\{Pr(Y=1\mid{X=0,Z})\}.
The function uses logistic regression to estimate Pr(Y=1\mid{X=0,Z})
, and the marginal sample distribution of Z
to approximate the outer expectation (Sjölander and Vansteelandt, 2012).
If clusterid
is supplied, then a clustered sandwich formula is used in all variance calculations.
Value
AF.est |
estimated attributable fraction. |
AF.var |
estimated variance of |
P.est |
estimated factual proportion of cases; |
P.var |
estimated variance of |
P0.est |
estimated counterfactual proportion of cases if exposure would be eliminated; |
P0.var |
estimated variance of |
object |
the fitted model. Fitted using logistic regression, |
Author(s)
Elisabeth Dahlqwist, Arvid Sjölander
References
Greenland, S. and Drescher, K. (1993). Maximum Likelihood Estimation of the Attributable Fraction from logistic Models. Biometrics 49, 865-872.
Sjölander, A. and Vansteelandt, S. (2011). Doubly robust estimation of attributable fractions. Biostatistics 12, 112-121.
See Also
The new and more general version of the function: AFglm
.
Examples
# Simulate a cross-sectional sample
expit <- function(x) 1 / (1 + exp( - x))
n <- 1000
Z <- rnorm(n = n)
X <- rbinom(n = n, size = 1, prob = expit(Z))
Y <- rbinom(n = n, size = 1, prob = expit(Z + X))
# Example 1: non clustered data from a cross-sectional sampling design
data <- data.frame(Y, X, Z)
# Estimation of the attributable fraction
AF.cs_est <- AF.cs(formula = Y ~ X + Z + X * Z, data = data, exposure = "X")
summary(AF.cs_est)
# Example 2: clustered data from a cross-sectional sampling design
# Duplicate observations in order to create clustered data
id <- rep(1:n, 2)
data <- data.frame(id = id, Y = c(Y, Y), X = c(X, X), Z = c(Z, Z))
# Estimation of the attributable fraction
AF.cs_clust <- AF.cs(formula = Y ~ X + Z + X * Z, data = data,
exposure = "X", clusterid = "id")
summary(AF.cs_clust)