pif.confidence.linear: Confidence Intervals for the Potential Impact Fraction using...

In INSP-RH/pif: Potential Impact Fraction and Population Attributable Fraction for Cross-Sectional Data

Description

Function that calculates approximate confidence intervals to the potential impact fraction.

Usage

pif.confidence.linear(X, thetahat, rr, thetavar, cft = NA,
  weights = rep(1/nrow(as.matrix(X)), nrow(as.matrix(X))), confidence = 95,
  nsim = 1000, check_thetas = TRUE, check_exposure = TRUE,
  check_cft = TRUE, check_rr = TRUE, check_integrals = TRUE,
  is_paf = FALSE)

Arguments

X	Random sample (data.frame) which includes exposure and covariates.
thetahat	Estimator (vector) of theta for the Relative Risk function.
rr	function for Relative Risk which uses parameter theta. The order of the parameters shound be rr(X, theta).
thetavar	Estimator of variance of thetahat **Optional**
cft	Function cft(X) for counterfactual. Leave empty for the Population Attributable Fraction PAF where counterfactual is 0 exposure
weights	Survey weights for the random sample X
confidence	Confidence level % (default 95)
nsim	Number of simulations for estimation of variance
check_thetas	Check that theta parameters are correctly inputed
check_exposure	Check that exposure X is positive and numeric
check_rr	Check that Relative Risk function rr equals 1 when evaluated at 0
check_integrals	Check that counterfactual and relative risk's expected values are well defined for this scenario
is_paf	Boolean forcing evaluation of paf.

Author(s)

Rodrigo Zepeda-Tello [e-mail]

Dalia Camacho-García-Formentí [e-mail]

Examples

## Not run: 
#Example with risk given by HR (PAF)
set.seed(18427)
X <- as.data.frame(rnorm(100,3,.5))
thetahat <- 0.12
thetavar <- 0.1
pif.confidence.linear(X, thetahat, function(X, theta){exp(theta*X)}, 
                      thetavar, nsim = 100)

#Example with linear counterfactual
cft      <- function(X){0.3*X}
pif.confidence.linear(X, thetahat, function(X, theta){exp(theta*X)}, 
                     thetavar, cft, nsim = 100)

#Example with theta and X multivariate
set.seed(18427)
X1 <- rnorm(100, 3,.5)
X2 <- rnorm(100,3,.5)
X  <- as.data.frame(as.matrix(cbind(X1,X2)))
thetahat <- c(0.1, 0.03)
thetavar <- matrix(c(0.1, 0, 0, 0.05), byrow = TRUE, nrow = 2)
rr       <- function(X, theta){
           .X <- as.matrix(X, ncol = 2)
           exp(theta[1]*.X[,1] + theta[2]*.X[,2])
           }
cft <- function(X){0.5*X}
pif.confidence.linear(X, thetahat, rr, thetavar, cft) 

## End(Not run)

INSP-RH/pif documentation built on May 7, 2019, 6:01 a.m.