PHEindicatormethods DSR function

Georgina Anderson

2018-07-24

Introduction

This vignette documents the method for calculating DSRs using the PHEindicatormethods::phe_dsr function which calculates DSRs and their confidence limits using the Dobson method.

The function can be used to calculate DSRs for grouping single or multiple geographic areas/ genders/ timeperiods/ indicators in a single execution and takes the following arguments as inputs:



Argument Type Definition Default value
data data.frame data.frame containing the data to be standardised none
x unquoted string field name from data containing the observed number of events for each standardisation category (eg ageband) within each grouping set (eg area or indicator) none
n unquoted string field name from data containing the populations for each standardisation category (eg ageband) within each grouping set (eg area or indicator) none
stdpop unquoted string standard populations for each standardisation category (eg age band) specified as a field name from data or a vector. esp2013
stdpoptype quoted string whether the stdpop argument has been specified as a vactor or a field name “vector”
confidence numeric value the required level of confidence expressed as a number between 0.9 and 1 or 90 and 100 0.95
multiplier numeric value the multiplier used to express the final values (eg 100,000 = rate per 100,000 100,000



Note that the European Standard Population 2013 divided into 19 five-year agebands (0-4, 5-9, 10-14, …..90+) is provided in vector format within the package and will be used as the default for the stdpop argument

If multiple DSRs are required from a single data frame then the data frame must be grouped prior to inputting to the function - this is demonstrated below

The following packages must be installed and loaded if not already available

First let’s create some data to play with

In a real situation we’d most likely be sourcing our numerators and denominators from different places so let’s create them separately for now.

pops <- data.frame(indicator = rep(c("Ind1","Ind2","Ind3","Ind4"), each = 19 * 2 * 5),
                   period = rep(2012:2016, each = 19 * 2),
                   region = rep(rep(c("Area1","Area2"),each=19), times = 5),
                   ageband = rep(c(0,5,10,15,20,25,30,35,40,45,50,
                                   55,60,65,70,75,80,85,90),times = 10),
                   pop = sample(10000:20000, 19 * 2 * 5 * 4, replace = TRUE))
head(pops)
#>   indicator period region ageband   pop
#> 1      Ind1   2012  Area1       0 13896
#> 2      Ind1   2012  Area1       5 17041
#> 3      Ind1   2012  Area1      10 13275
#> 4      Ind1   2012  Area1      15 18545
#> 5      Ind1   2012  Area1      20 11353
#> 6      Ind1   2012  Area1      25 18088


deaths <- data.frame(indicator = rep(c("Ind1","Ind2","Ind3","Ind4"), each = 19 * 2 * 5),
                   period = rep(2012:2016, each = 19 * 2),
                   region = rep(rep(c("Area1","Area2"),each=19), times = 5),
                   ageband = rep(c(0,5,10,15,20,25,30,35,40,45,50,
                                   55,60,65,70,75,80,85,90),times = 10),
                   dths = sample(200, 19 * 2 * 5 * 4, replace=TRUE))
head(deaths)
#>   indicator period region ageband dths
#> 1      Ind1   2012  Area1       0    1
#> 2      Ind1   2012  Area1       5  181
#> 3      Ind1   2012  Area1      10  121
#> 4      Ind1   2012  Area1      15    4
#> 5      Ind1   2012  Area1      20   11
#> 6      Ind1   2012  Area1      25  194

Then let’s prepare and validate our data

Our data contains records for 4 different indicators, 5 time periods and 2 geographies so let’s calculate a DSR for each combination - that’s 40 separate DSRs from a single execution of the phe_dsr function……

Prepare the data frame

First we’ll need to join our datasets to create the input data.frame for the function and specify the grouping sets:

Check the data meets the function requirements

It is important that your data meets the following criteria in order for the phe_dsr function to work so it is wise to check this before we move on.

1. Each grouping set within your data must contain an equal number of records.

The phe_dsr function has built in error handling to check this requirement, or you can check your data manually using code like this:

2. If you are supplying your standard population in vector format (stdpoptype=“vector”) then this vector must also contain the same number of records as each grouping set within your data.

In this example we’re going to use the default esp2013 vector that is provided with the PHEindicatormethods package for our standard population - it contains 19 ordered values representing the 5-year age bands 0-4, 5-9, 10-14….85-89, 90+.

The phe_dsr function has built in error handling to check this requirement, or you can check your data manually using code like this:

3. If you are supplying your standard population in vector format (stdpoptype=“vector”) then the standard population and your data (for each grouping set) must be sorted in the same standardisation category order because the function will join these by position. This would normally mean sorting both the standard population vector and the records for each group within your data by age band from youngest to oldest.

The phe_dsr function does not have any built in error handling to check this requirement as the function does not require the standardisation category labels to be provided in your data. It is therefore the responsibility of the function user to ensure this requirement is met. If the standardisation category labels are included with your data (as in our example) then the following code can be used to check the requirment manually:

CAUTION: Failure to ensure that this 3rd requirement is met could result in apparent successful execution of the phe_dsr function even though the data may have been incorrectly standardised. This is demonstrated in the sample code below:

Now let’s calculate some DSRs

Now we’re ready to calculate the DSRs using our correctly ordered df data frame.

By default the function will apply 95% confidence, a 100,000 multiplier and will output just 3 fields against each grouping set - the dsr, the lower confidence limit and the upper confidence limit:

phe_dsr(df, dths, pop)
#> # A tibble: 40 x 6
#> # Groups:   indicator, period [20]
#>    indicator period region value lowercl uppercl
#>    <fct>      <int> <fct>  <dbl>   <dbl>   <dbl>
#>  1 Ind1        2012 Area1   689.    654.    725.
#>  2 Ind1        2012 Area2   593.    563.    625.
#>  3 Ind1        2013 Area1   703.    670.    737.
#>  4 Ind1        2013 Area2   660.    625.    696.
#>  5 Ind1        2014 Area1   634.    605.    664.
#>  6 Ind1        2014 Area2   726.    692.    762.
#>  7 Ind1        2015 Area1   770.    734.    807.
#>  8 Ind1        2015 Area2   724.    691.    758.
#>  9 Ind1        2016 Area1   591.    563.    621.
#> 10 Ind1        2016 Area2   746.    713.    780.
#> # ... with 30 more rows

Alternatively, we can add further arguments to specify:

phe_dsr(df, dths, pop, type = "full", confidence = 99.8, multiplier = 10000)
#> # A tibble: 40 x 11
#> # Groups:   indicator, period [20]
#>    indicator period region total_count total_pop value lowercl uppercl
#>    <fct>      <int> <fct>        <int>     <int> <dbl>   <dbl>   <dbl>
#>  1 Ind1        2012 Area1         1775    274256  68.9    63.5    74.6
#>  2 Ind1        2012 Area2         1722    274842  59.3    54.6    64.3
#>  3 Ind1        2013 Area1         1971    286057  70.3    65.1    75.7
#>  4 Ind1        2013 Area2         1548    279184  66.0    60.6    71.7
#>  5 Ind1        2014 Area1         2116    306649  63.4    58.9    68.2
#>  6 Ind1        2014 Area2         1932    297426  72.6    67.3    78.2
#>  7 Ind1        2015 Area1         1991    291382  77.0    71.4    82.9
#>  8 Ind1        2015 Area2         2110    300204  72.4    67.3    77.8
#>  9 Ind1        2016 Area1         1843    289172  59.1    54.7    63.8
#> 10 Ind1        2016 Area2         2140    294442  74.6    69.4    80.0
#> # ... with 30 more rows, and 3 more variables: confidence <chr>,
#> #   statistic <chr>, method <chr>

Alternative Standard Populations

In some cases you may wish to standardise against a different population to the default esp2013 one provided - such as the 1976 European Standard Population or an age and sex standardised population. There are two ways to specify an alternative standard population:

1. Provide the custom standard population as a vector

In the example below, the 1976 European Standard Population (which has 18 age groups) is provided as a vector and then referenced in the function call. To ensure the function works we must also ensure that our data has been broken down into these same 18 age bands (for the purposes of this example I’ve just combined the 85-90 and 90+ age band data into a single 85+ age band from the data.frame we used earlier).

The phe_dsr function can then be executed using a user-defined standard population:

2. Append the standard populations to your data frame before executing the function

In the example below, the esp2013 standard population is appended to our data frame prior to calling the phe_dsr function. The field name can then be specified in the function call. If stdpop is specified as a field name we must also tell the function this by specifying stdpoptype = “field” as below:

And what if the data are not so tidy?

Zero deaths for a specific age band within a small geography

This would be a fairly common scenario - maybe you have Local Authority data and there are no deaths in some of the younger age groups for some of the smaller areas.

Let’s fudge a couple of data frames to represent this. In this example, there are no deaths in the 10-14, 15-20 and 20-14 age bands:

If we simply join these data frames to produce the input data frame required for the phe_dsr function then we get NA values in the Deaths column and the function will return an error:

The NA values must be replaced with zeros before executing the function: