msSPChelpR (version 0.9.1)

asir: Calculate age-standardized incidence rates

Description

Calculate age-standardized incidence rates

Usage

asir(
  df,
  dattype = NULL,
  std_pop = "ESP2013",
  truncate_std_pop = FALSE,
  futime_src = "refpop",
  summarize_groups = "none",
  count_var,
  stdpop_df = standard_population,
  refpop_df = population,
  region_var = NULL,
  age_var = NULL,
  sex_var = NULL,
  year_var = NULL,
  site_var = NULL,
  futime_var = NULL,
  pyar_var = NULL,
  alpha = 0.05
)

Value

df

Arguments

df

dataframe in wide format

dattype

can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.

std_pop

can be either "ESP2013, ESP1976, WHO1960, WHO2000

truncate_std_pop

if TRUE standard population will be truncated for all age-groups that do not occur in df

futime_src

can be either "refpop" or "cohort". Default is "refpop".

summarize_groups

option to define summarizing stratified groups. Default is "none". If you want to define variables that should be summarized into one group, you can chose from region_var, sex_var, year_var. Define multiple summarize variables by summarize_groups = c("region", "sex", "year")

count_var

variable to be counted as observed case. Should be 1 for case to be counted.

stdpop_df

df where standard population is defined. It is assumed that stdpop_df has the columns "sex" for biological sex, "age" for age-groups, "standard_pop" for name of standard population (e.g. "European Standard Population 2013) and "population_n" for size of standard population age-group. stdpop_df must use the same category coding of age and sex as age_var and sex_var.

refpop_df

df where reference population data is defined. Only required if option futime = "refpop" is chosen. It is assumed that refpop_df has the columns "region" for region, "sex" for biological sex, "age" for age-groups (can be single ages or 5-year brackets), "year" for time period (can be single year or 5-year brackets), "population_pyar" for person-years at risk in the respective age/sex/year cohort. refpop_df must use the same category coding of age, sex, region, year and site as age_var, sex_var, region_var, year_var and site_var.

region_var

variable in df that contains information on region where case was incident. Default is set if dattype is given.

age_var

variable in df that contains information on age-group. Default is set if dattype is given.

sex_var

variable in df that contains information on biological sex. Default is set if dattype is given.

year_var

variable in df that contains information on year or year-period when case was incident. Default is set if dattype is given.

site_var

variable in df that contains information on ICD code of case diagnosis. Default is set if dattype is given.

futime_var

variable in df that contains follow-up time per person (in years) in cohort (can only be used with futime_src = "cohort"). Default is set if dattype is given.

pyar_var

variable in refpop_df that contains person-years-at-risk in reference population (can only be used with futime_src = "refpop") Default is set if dattype is given.

alpha

significance level for confidence interval calculations. Default is alpha = 0.05 which will give 95 percent confidence intervals.

Examples

#load sample data
data("us_second_cancer")
data("standard_population")
data("population_us")

#make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
                    #only use sample
                    dplyr::filter(as.numeric(fake_id) < 200000) %>%
                    msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id", 
                    time_id_var = "SEQ_NUM", timevar_max = 2)
                    
#create count variable
usdata_wide <- usdata_wide %>%
                    dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2)   ~ 1,
                    TRUE ~ 0))
 
#remove cases for which no reference population exists
usdata_wide <- usdata_wide %>%
                    dplyr::filter(t_yeardiag.2 %in% c("1990 - 1994", "1995 - 1999", "2000 - 2004",
                                                       "2005 - 2009", "2010 - 2014"))
                    

#now we can run the function
msSPChelpR::asir(usdata_wide,
      dattype = "seer",
      std_pop = "ESP2013",
      truncate_std_pop = FALSE,
      futime_src = "refpop",
      summarize_groups = "none",
      count_var = "count_spc",
      refpop_df = population_us,
      region_var = "registry.1", 
      age_var = "fc_agegroup.1",
      sex_var = "sex.1",
      year_var = "t_yeardiag.2", 
      site_var = "t_site_icd.2",
      pyar_var = "population_pyar")