Calculate Age-Standardized Rate (ASR)

create_asr() calculates age-standardized rates (ASRs) using PBCR data. It supports stratification by multiple variables, allows the specification of different standard population structures, and provides flexibility in the inclusion of variance, confidence intervals, and population data.

create_asr(
  x,
  ...,
  event = "fbs",
  std = c("cn2000", "wld85"),
  cancer_type = "big",
  mp = 1e+05,
  decimal = 2,
  show_var = FALSE,
  show_ci = FALSE,
  collapse = TRUE
)

# S3 method for class 'canregs'
create_asr(x, ..., cancer_type = "big", collapse = TRUE)

# S3 method for class 'canreg'
create_asr(x, ..., cancer_type = "big")

# S3 method for class 'fbswicds'
create_asr(
  x,
  ...,
  event = "fbs",
  std = c("cn2000", "wld85"),
  mp = 1e+05,
  decimal = 2,
  show_pop = FALSE,
  show_var = FALSE,
  show_ci = FALSE,
  collapse = TRUE
)

# S3 method for class 'fbswicd'
create_asr(
  x,
  ...,
  event = "fbs",
  std = c("cn2000", "wld85"),
  mp = 1e+05,
  decimal = 2,
  show_pop = FALSE,
  show_var = FALSE,
  show_ci = FALSE
)

Arguments

x: The input data, object with class of 'fbswicd', 'fbswicds', 'canreg', or 'canregs'.
...: One or more variables used for stratification. For example, you can stratify by sex, year, cancer, or just by year. If sex is not passed as a parameter, the output will be the result for the combined gender.
event: A variable used to specify the type of calculation, options are fbs or sws, fbs for cancer incidence, and sws for cancer mortality.
std: Specify the standard population structure in the 'std_pop' data frame used for calculating standardized rates. When calculating standardized rates for multiple standard populations, specify std = c(segi, china).
cancer_type: A character string specifying the classification method used to categorize ICD-10 codes. This determines how ICD-10 codes are classified. Options include "big" (classify ICD-10 codes into 26 cancer categories), "small" (classify ICD-10 codes into 59 cancer categories, more specific categories), "system" (classify ICD-10 codes into organ system), and "gco" (classify ICD-10 code into cancer categories same as classification published by the Global Cancer Observatory). This parameter is only available when the input data is a vector of ICD-10 codes, or object with class of 'canreg' or 'canregs'.
mp: A constant to multiply rates by (e.g. mp=1000 for rates per 1000).
decimal: This parameter specifies the number of decimal places to round the results. The default is 2, which means rates will be rounded to two decimal places.
show_var: Logical value whether output variance or not.
show_ci: Logical value whether output confidence(lower or upper bound) or not.
collapse: Logical value whether output result as asr or asrs.
show_pop: Logical value whether output population or not.

Value

A data frame or tibble contains the age standard rates and CIs.

Examples


# calculate ASR based on object with class of `canreg`
data("canregs")
data <- canregs[[1]]
# calculate ASR using default parameter
asr <- create_asr(data, year, sex, cancer)
head(asr)
#> # A tibble: 6 × 12
#>    year   sex cancer no_cases    cr asr_cn2000 asr_wld85 truncr_cn2000
#>   <int> <dbl> <chr>     <dbl> <dbl>      <dbl>     <dbl>         <dbl>
#> 1  2021     0 101          27  3.95       3.23      3.32          6.88
#> 2  2021     0 102           7  1.02       0.78      0.83          1.58
#> 3  2021     0 103          65  9.52       6.88      6.65          7.61
#> 4  2021     0 104         101 14.8       11.2      11.2          16.1 
#> 5  2021     0 105         180 26.4       19.8      19.9          26.5 
#> 6  2021     0 106         127 18.6       14.4      14.6          23.7 
#> # ℹ 4 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>, rank <int>
# calculate ASR using multiple standard population
asr_multi_std <- create_asr(data, year, sex, cancer,
                            std = c("cn82", "cn2000", "wld85"))
head(asr_multi_std)
#> # A tibble: 6 × 14
#>    year   sex cancer no_cases    cr asr_cn82 asr_cn2000 asr_wld85 truncr_cn82
#>   <int> <dbl> <chr>     <dbl> <dbl>    <dbl>      <dbl>     <dbl>       <dbl>
#> 1  2021     0 101          27  3.95     2.4        3.23      3.32        7.39
#> 2  2021     0 102           7  1.02     0.58       0.78      0.83        1.76
#> 3  2021     0 103          65  9.52     4.82       6.88      6.65        8.25
#> 4  2021     0 104         101 14.8      7.83      11.2      11.2        17.4 
#> 5  2021     0 105         180 26.4     13.8       19.8      19.9        28.7 
#> 6  2021     0 106         127 18.6     10.6       14.4      14.6        25.4 
#> # ℹ 5 more variables: truncr_cn2000 <dbl>, truncr_wld85 <dbl>, cumur <dbl>,
#> #   prop <dbl>, rank <int>
# calculate ASR with confidence interval
asr_with_ci <- create_asr(data, year, sex, cancer, show_ci = TRUE)
head(asr_with_ci)
#> # A tibble: 6 × 18
#>    year   sex cancer no_cases    cr cr_lower cr_upper asr_cn2000
#>   <int> <dbl> <chr>     <dbl> <dbl>    <dbl>    <dbl>      <dbl>
#> 1  2021     0 101          27  3.95     2.6      5.75       3.23
#> 2  2021     0 102           7  1.02     0.41     2.11       0.78
#> 3  2021     0 103          65  9.52     7.34    12.1        6.88
#> 4  2021     0 104         101 14.8     12.0     18.0       11.2 
#> 5  2021     0 105         180 26.4     22.6     30.5       19.8 
#> 6  2021     0 106         127 18.6     15.5     22.1       14.4 
#> # ℹ 10 more variables: asr_lower_cn2000 <dbl>, asr_upper_cn2000 <dbl>,
#> #   asr_wld85 <dbl>, asr_lower_wld85 <dbl>, asr_upper_wld85 <dbl>,
#> #   truncr_cn2000 <dbl>, truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>,
#> #   rank <int>
# calculate ASR with population at risk
asr_with_pop <- create_asr(data, year, sex, cancer, show_pop = TRUE)
head((asr_with_pop))
#> # A tibble: 6 × 13
#>    year   sex cancer    pop no_cases    cr asr_cn2000 asr_wld85 truncr_cn2000
#>   <int> <dbl> <chr>   <dbl>    <dbl> <dbl>      <dbl>     <dbl>         <dbl>
#> 1  2021     0 101    683110       27  3.95       3.23      3.32          6.88
#> 2  2021     0 102    683110        7  1.02       0.78      0.83          1.58
#> 3  2021     0 103    683110       65  9.52       6.88      6.65          7.61
#> 4  2021     0 104    683110      101 14.8       11.2      11.2          16.1 
#> 5  2021     0 105    683110      180 26.4       19.8      19.9          26.5 
#> 6  2021     0 106    683110      127 18.6       14.4      14.6          23.7 
#> # ℹ 4 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>, rank <int>
# calculate ASR with variance
asr_with_var <- create_asr(data, year, sex, cancer, show_var = TRUE)
head(asr_with_var)
#> # A tibble: 6 × 15
#>    year   sex cancer no_cases    cr   cr_var asr_cn2000 asr_var_cn2000 asr_wld85
#>   <int> <dbl> <chr>     <dbl> <dbl>    <dbl>      <dbl>          <dbl>     <dbl>
#> 1  2021     0 101          27  3.95 5.79e-11       3.23       3.91e-11      3.32
#> 2  2021     0 102           7  1.02 1.50e-11       0.78       8.73e-12      0.83
#> 3  2021     0 103          65  9.52 1.39e-10       6.88       7.42e-11      6.65
#> 4  2021     0 104         101 14.8  2.16e-10      11.2        1.25e-10     11.2 
#> 5  2021     0 105         180 26.4  3.86e-10      19.8        2.21e-10     19.9 
#> 6  2021     0 106         127 18.6  2.72e-10      14.4        1.66e-10     14.6 
#> # ℹ 6 more variables: asr_var_wld85 <dbl>, truncr_cn2000 <dbl>,
#> #   truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>, rank <int>


# calculate ASR based on object with class of `fbswicd`
# convert object with class of `canreg` to object with class of `fbswicd`
fbsw <- count_canreg(data)
asr <- create_asr(fbsw, event= "sws", year, sex, cancer)

Arguments

Value

See also

Examples