In the previous vignette, we discussed the model setup process
in-depth. But how do we get our estimates once we’ve run our model? In
this vignette, we discuss extracting estimates from our model object
with the get_estimates() function, and how to
age-standardize those estimates with age_standardize().
get_estimates() functionIn the RSTr introductory vignette, we generated age-standardized
estimates for lambda based on our example Michigan dataset.
To extract rates from an RSTr object, we can simply run
get_estimates():
estimates <- get_estimates(mod_mst, rates_per = 1e5)
head(estimates)
#> county group year medians ci_lower ci_upper rel_prec events population
#> 1 26001 35-44 1979 24.17566 18.90347 35.37604 1.467631 1 964
#> 2 26001 35-44 1980 29.95289 23.47562 36.20030 2.353922 1 995
#> 3 26001 35-44 1981 21.06546 16.47818 27.91682 1.841605 0 988
#> 4 26001 45-54 1979 115.12711 95.94252 135.68586 2.896765 0 968
#> 5 26001 45-54 1980 107.96123 97.34824 127.52960 3.577084 4 980
#> 6 26001 45-54 1981 106.66256 83.54648 121.54097 2.807317 2 973age_standardization() functionIn many cases, we will want to age-standardize our estimates based on
some (or all) age groups in our dataset. In our Michigan dataset, we
have six ten-year age groups over which we can standardize; let’s
age-standardize from ages 35-64. For RSTr objects,
age_standardize() takes in four arguments:
RSTr_obj: The RSTr model object created
with *car();
std_pop: A vector of standard
populations associated with the age groups of interest. Since our
Michigan data is from 1979-1988, we can use 1980 standard populations
from NIH.
It is recommended that you use the standard population that is most
closely associated with your dataset;
new_name: The name of your new standard population
group; and
groups: A vector of names matching each
group of interest. To age-standardize by all groups in a dataset, leave
this argument blank.
Once we have our std_pop vector, we can age-standardize
our estimates:
std_pop <- c(113154, 100640, 95799)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "35-64", groups = c("35-44", "45-54", "55-64"))
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 2200
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64
#> Estimates suppressed: NoNotice now that the mod_mst object indicates we have
age-standardized our estimates and the names of our age-standardized
group. We can also add on to our list of age-standardized estimates by
simply specifying a different group:
std_pop <- c(68775, 34116, 9888)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "65up", groups = c("65-74", "75-84", "85+"))
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 2200
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64 65up
#> Estimates suppressed: NoIf we want to generate estimates for all groups, i.e. 35 and
up, we can omit the groups argument and expand
std_pop to include all of our populations:
std_pop <- c(113154, 100640, 95799, 68775, 34116, 9888)
mod_mst <- age_standardize(mod_mst, std_pop, new_name = "35up")
mod_mst
#> RSTr object:
#>
#> Model name: mstcar_example
#> Model type: MSTCAR
#> Data likelihood: binomial
#> Estimate Credible Interval: 95%
#> Number of geographic units: 83
#> Number of samples: 2200
#> Estimates age-standardized: Yes
#> Age-standardized groups: 35-64 65up 35up
#> Estimates suppressed: No
mst_estimates_as <- get_estimates(mod_mst)
head(mst_estimates_as)
#> county group year medians ci_lower ci_upper rel_prec events population
#> 1 26001 35-64 1979 154.4839 139.2431 170.0486 5.014813 7 3353
#> 2 26001 35-64 1980 149.1580 137.7706 158.5573 7.175656 12 3421
#> 3 26001 35-64 1981 132.5922 119.5103 144.0410 5.405147 7 3431
#> 4 26001 35up 1979 422.7422 379.6836 452.2813 5.823079 25 5268
#> 5 26001 35up 1980 408.3825 363.8984 434.1419 5.813812 37 5424
#> 6 26001 35up 1981 397.3618 373.4317 433.0654 6.663372 31 5510Now, get_estimates(mod_mst) shows the age-standardized
estimates as opposed to our non-standardized estimates. Should you want
to see the non-standardized estimates instead, you can set the argument
standardized = FALSE.
In this vignette, we explored the get_estimates()
function and investigated age-standardization with the
age_standardize() function. Age-standardization is one of
the most important features of the RSTr package; using just a few
arguments, we can easily generate estimates across our population
groups.