ir_crosstab: Calculate crude incidence rates and crosstabulate results by...

In marianschmidt/msSPChelpR: Helper Functions for Second Primary Cancer Analyses

Calculate crude incidence rates and crosstabulate results by break variables

Description

Calculate crude incidence rates and crosstabulate results by break variables

Usage

ir_crosstab(
  df,
  dattype = NULL,
  count_var,
  xbreak_var = "none",
  ybreak_vars,
  collapse_ci = FALSE,
  add_total = "no",
  add_n_percentages = FALSE,
  futime_var = NULL,
  alpha = 0.05
)

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.
count_var	variable to be counted as observed case. Should be 1 for case to be counted.
xbreak_var	variable from df by which rates should be stratified in columns of result df. Default is "none".
ybreak_vars	variables from df by which rates should be stratified in rows of result df. Multiple variables will result in appended rows in result df. y_break_vars is required.
collapse_ci	If TRUE upper and lower confidence interval will be collapsed into one column separated by "-". Default is FALSE.
add_total	option to add a row of totals. Can be either "no" for not adding such a row or "top" or "bottom" for adding it at the first or last row. Default is "no".
add_n_percentages	option to add a column of percentages for n_base in its respective yvar_group. Can only be used when xbreak_var = "none". Default is FALSE.
futime_var	variable in df that contains follow-up time per person (in years). 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.

Value

df

Examples

#load sample data
data("us_second_cancer")

#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
                    msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id", 
                    time_id_var = "SEQ_NUM", timevar_max = 10)
                    
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
                 dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2)   ~ "No SPC",
                                                       !is.na(t_site_icd.2)   ~ "SPC developed",
                                                       TRUE ~ NA_character_)) %>%
                 dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2)   ~ 1,
                                                              TRUE ~ 0))
                                                              
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
                  msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
                                         status_var = "p_status", life_var = "p_alive.1",
                                         birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
 
#now we can run the function
usdata_wide <- usdata_wide %>%
                 msSPChelpR::calc_futime(., 
                        futime_var_new = "p_futimeyrs", 
                        fu_end = "2017-12-31",
                        dattype = "seer", 
                        time_unit = "years",
                        status_var = "p_status",
                        lifedat_var = "datedeath.1", 
                        fcdat_var = "t_datediag.1", 
                        spcdat_var = "t_datediag.2")
                    
#for example, you can calculate incidence and summarize by sex and registry
msSPChelpR::ir_crosstab(usdata_wide,
      dattype = "seer",
      count_var = "count_spc",
      xbreak_var = "none",
      ybreak_vars = c("sex.1", "registry.1"),
      collapse_ci = FALSE,
      add_total = "no",
      add_n_percentages = FALSE,
      futime_var = "p_futimeyrs",
      alpha = 0.05)

marianschmidt/msSPChelpR documentation built on Feb. 1, 2024, 6:45 a.m.