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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 26003 35-44 1979 60.64569 48.28666 76.15700 2.175994 1 1011
#> 3 26005 35-44 1979 20.57092 16.59628 23.37615 3.034115 0 9110
#> 4 26007 35-44 1979 24.73026 17.37052 31.64529 1.732446 0 3650
#> 5 26009 35-44 1979 32.19631 26.36770 40.97872 2.203564 0 1763
#> 6 26011 35-44 1979 37.52907 23.49114 48.33818 1.510404 0 1470age_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 26003 35-64 1979 285.1687 251.9023 298.3916 6.134078 12 3105
#> 3 26005 35-64 1979 113.8297 104.2205 125.8577 5.260842 27 23926
#> 4 26007 35-64 1979 152.9456 141.5888 165.1699 6.485954 15 10000
#> 5 26009 35-64 1979 158.5001 143.1010 178.9157 4.425559 11 5152
#> 6 26011 35-64 1979 202.9706 185.8431 224.2937 5.278736 8 4517Now, 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.