FUN(weights = c(0.1, 0.25, 0.25, 0.2, 0.2)) which may be stored in advance: w <- c(0.1, 0.25, 0.25, 0.2, 0.2) FUN(weights = w) The order of the weights matters. popEpi functions with direct
adjusting enabled match the supplied weights to the adjusting variables
as follows: If the adjusting variable is a factor, the order
of the levels is used. Otherwise, the alphabetic order of the unique
values is used (try sort to see how it works). For clarity
and certainty we recommend using factor or numeric variables
when possible. character variables should be avoided: to see why,
try sort(15:9) and sort(as.character(15:9)). It is also possible to supply a character string corresponding
to one of the age group standardization schemes integrated into popEpi: weights = "internal" to use internally
computed weights, i.e. usually simply the counts of subjects / person-time
experienced in each stratum. E.g. FUN(weights = "world_2000_18of5") will use the world standard population from 2000 as
weights for 18 age groups, that your adjusting variable is
assumed to contain. The adjusting variable must be coded in this case as
a numeric variable containing 1:18 or as a factor with
18 levels (coded from the youngest to the oldest age group).w = 0.5 and the age group 0-4 has
w = 0.1, the "grand" weight for men aged 0-4 is 0.5*0.1).
The "grand" weights are then used for adjusting after ensuring they
sum to one. When using multiple adjusting variables, you
are allowed to pass either a named list of
weights or a data.frame of weights. E.g. WL <- list(agegroup = age_w, sex = sex_w) FUN(weights = WL) where age_w and sex_w are numeric vectors. Given the
conditions explained in the previous section are satisfied, you may also do
e.g. WL <- list(agegroup = "world_2000_18of", sex = sex_w) FUN(weights = WL) and the world standard pop is used as weights for the age groups as outlined
in the previous section. Sometimes using a data.frame can be clearer (and it is fool-proof
as well). To do this, form a data.frame that repeats the levels
of your adjusting variables by each level of every other adjusting variable,
and assign the weights as a column named "weights". E.g. wdf <- data.frame(sex = rep(0:1, each = 18), agegroup = rep(1:18, 2)) wdf$weights <- rbinom(36, size = 100, prob = 0.25) FUN(weights = wdf) If you want to use the counts of subjects in strata as the weights,
one way to do this is by e.g. wdf <- as.data.frame(x$V1, x$V2, x$V3)
names(wdf) <- c("V1", "V2", "V3", "weights")E
by the set of adjusting variables A, to which a set of weights
W is applicable. The weighted average over A is then the
direct-adjusted estimate of E (E*).To enable both quick and easy as well as more rigorous usage of direct standardization with weights, the weights arguments in popEpi can be supplied in several ways. Ability to use the different ways depends on the number of adjusting variables.
The weights are always handled internally to sum to 1, so they do not need to be scaled in this manner when they are supplied. E.g. counts of subjects in strata may be passed.
Source of the 1976 European standard population:
Waterhouse, J.,Muir, C.S.,Correa, P.,Powell, J., eds (1976). Cancer Incidence in Five Continents, Vol. III. IARC Scientific Publications, No. 15, Lyon, IARC
A comparison of the 1966 vs. 2000 world standard populations in 5-year age groups: http://www3.ha.org.hk/cancereg/e_asr.asp
Source of 2000 world standard population in 1-year age groups: http://seer.cancer.gov/stdpopulations/stdpop.singleages.html
flexible_arguments