AdjAR {pARccs} | R Documentation |
AdjAR
realizes the estimation of (adjusted) attributable risk (AR)
from case-control data via logistic regression by calling the adequate
function which holds the computation
AdjAR(D, E, C = NULL, model)
D |
a vector which holds the case-control state ("1" = case, "0"=control) |
E |
a matrix of the exposure factor/s (all of them have to be dichotomous!) |
C |
a matrix of the confounder/s (all of them have to be categorical!) |
model |
a model formula or an object of class "glm " |
Depending from the entered data AdjAR
accesses to two additional functions:
AR_woC
is selected if there is no variable which is only a confounder,
expressed as C=NULL
.
AR_wC
is selected if there are also variables which only act as confounders,
that means C
is a matrix.
See AR_woC
and AR_wC
for further information about the computation.
AdjAR
returns a matrix containing the attributable risk for every possible (binary)
combination of the exposure factors in E
.
If C=NULL
these are only adjusted to the rest of the exposure factors
(which are not part of the interested combination).
If there are given confounders in C
the attributable risks are additionally adjusted to them.
Also if there are only a single exposure factor/confounder you have to enter a matrix, so this will be a matrix with only one column.
It is also important that the given variables in D
, E
and C
are not defined
as factors.
The names of the variables (outcome, exposure factor/s, confounder/s) in the argument model
have to be identical to the (column-)names of the entered data.
Furthermore all given exposure factors and confounders have to be part of the argument model
.
Validity of the estimation can only be taken for granted for data with simple random sampling, stratified random sampling or frequency-matching of controls.
Christiane Raemsch
Levin, M. (1953) The occurrence of lung cancer in man Acta Unio Internationalis Contra Cancrum 9, 531-41
Bruzzi, P.; Green S.; Byard, D. et al. (1985) Estimating the population attributable risk for multiple risk factors using case-control data American Journal of Epidemiology 122, 904-14
Benichou, J. (1991) Methods of adjustment for estimating the attributable risk in case-control studies: a review Statistics in Medicine 10, 1753-73
##### Computation of the AR for every combination of two ##### ##### exposure factors if there are no confounders ##### set.seed(2007) dicho <- c(0,1) cc_state <- sample(dicho, 100, replace=TRUE) exposure1 <- sample(dicho, 100, replace=TRUE, prob=c(0.7, 0.3)) exposure2 <- sample(dicho, 100, replace=TRUE, prob=c(0.4, 0.6)) relation <- as.formula(cc_state~exposure1+exposure2) data_exp <- cbind(exposure1, exposure2) AR_exposures <- AdjAR(D=cc_state, E=data_exp, model=relation) ##### Computation of the AR for every combination of two ##### ##### exposure factors with adjustment to confounder1 ##### set.seed(2008) cc_state <- sample(dicho, 100, replace=TRUE) exposure1 <- sample(dicho, 100, replace=TRUE, prob=c(0.7, 0.3)) exposure2 <- sample(dicho, 100, replace=TRUE, prob=c(0.4, 0.6)) cat_confounder <- c(0,1,2,3) confounder1 <- sample(cat_confounder, 100, replace=TRUE) data_exp <- cbind(exposure1, exposure2) conf <- matrix(confounder1, ncol=1) colnames(conf) <- c("confounder1") rel_mod <- glm(cc_state~exposure1+exposure2+confounder1, family=binomial) AR_exposures <- AdjAR(cc_state, data_exp, conf, rel_mod)