04: Generating Estimates: Age-standardization

Overview

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().

The get_estimates() function

In 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():

mod_mst <- mstcar(name = "my_test_model", data = miheart, adjacency = miadj)
# For computational reasons, full model fitting is not run during CRAN checks.
# When building on CRAN, this vignette loads a pre-fitted example model included with the package.
# The pkgdown website shows the full model-fitting workflow.
example_dir <- system.file("extdata", package = "RSTr")
mod_mst <- load_model("mstcar_example", example_dir)
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 41.09875 31.76009 47.11024 2.677417      1        964
#> 2  26003 35-44 1979 61.79864 50.74198 80.73919 2.060146      1       1011
#> 3  26005 35-44 1979 23.44843 18.67969 28.12923 2.481436      0       9110
#> 4  26007 35-44 1979 38.04293 26.32669 48.99415 1.678306      0       3650
#> 5  26009 35-44 1979 36.87313 31.41068 44.70636 2.773316      0       1763
#> 6  26011 35-44 1979 36.02715 32.00151 47.83611 2.275216      0       1470

The age_standardization() function

In 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:

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: 3000 
#> Estimates age-standardized: Yes 
#> Age-standardized groups: 35-64 
#> Estimates suppressed: No

Notice 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: 3000 
#> Estimates age-standardized: Yes 
#> Age-standardized groups: 35-64 65up 
#> Estimates suppressed: No

If 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: 3000 
#> 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 202.9310 166.2747 242.0648 2.677538      7       3353
#> 2  26003 35-64 1979 256.0684 214.3235 348.2249 1.912365     12       3105
#> 3  26005 35-64 1979 126.2409 112.8141 147.4087 3.649160     27      23926
#> 4  26007 35-64 1979 174.0603 154.6135 209.3011 3.182813     15      10000
#> 5  26009 35-64 1979 184.0658 163.5568 205.2774 4.411868     11       5152
#> 6  26011 35-64 1979 207.9429 185.7425 231.7124 4.523455      8       4517

Now, 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.

Final thoughts

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.