Package 'msSPChelpR'

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
)

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.

Value

df

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")

---

Calculate follow-up time per case until end of follow-up depending on pat_status - tidyverse version

Description

Calculate follow-up time per case until end of follow-up depending on pat_status - tidyverse version

Usage

calc_futime(
  wide_df,
  futime_var_new = "p_futimeyrs",
  fu_end,
  dattype = NULL,
  check = TRUE,
  time_unit = "years",
  status_var = "p_status",
  lifedat_var = NULL,
  fcdat_var = NULL,
  spcdat_var = NULL,
  quiet = FALSE
)

Arguments

wide_df	dataframe in wide format
futime_var_new	Name of the newly calculated variable for follow-up time. Default is p_futimeyrs.
fu_end	end of follow-up in time format YYYY-MM-DD.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
check	Check newly calculated variable p_status by printing frequency table. Default is TRUE.
time_unit	Unit of follow-up time (can be "days", "weeks", "months", "years"). Default is "years".
status_var	Name of the patient status variable that was previously created. Default is p_status.
lifedat_var	Name of variable containing Date of Death. Will override dattype preset.
fcdat_var	Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
spcdat_var	Name of variable containing Date of SPC diagnosis Will override dattype preset.
quiet	If TRUE, warnings and messages will be suppressed. Default is FALSE.

Value

wide_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
msSPChelpR::calc_futime(usdata_wide, 
                        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")

---

Calculate follow-up time per case until end of follow-up depending on pat_status - tidytable version

Description

Calculate follow-up time per case until end of follow-up depending on pat_status - tidytable version

Usage

calc_futime_tt(
  wide_df,
  futime_var_new = "p_futimeyrs",
  fu_end,
  dattype = NULL,
  check = TRUE,
  time_unit = "years",
  status_var = "p_status",
  lifedat_var = NULL,
  fcdat_var = NULL,
  spcdat_var = NULL,
  quiet = FALSE
)

Arguments

wide_df	dataframe or data.table in wide format
futime_var_new	Name of the newly calculated variable for follow-up time. Default is p_futimeyrs.
fu_end	end of follow-up in time format YYYY-MM-DD.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
check	Check newly calculated variable "p_futimeyrs" by printing frequency table. Default is TRUE.
time_unit	Unit of follow-up time (can be "days", "weeks", "months", "years"). Default is "years".
status_var	Name of the patient status variable that was previously created. Default is p_status.
lifedat_var	Name of variable containing Date of Death. Will override dattype preset.
fcdat_var	Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
spcdat_var	Name of variable containing Date of SPC diagnosis Will override dattype preset.
quiet	If TRUE, warnings and messages will be suppressed. Default is FALSE.

Value

wide_df

Examples

#load sample data
data("us_second_cancer")

#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
msSPChelpR::calc_futime_tt(usdata_wide, 
                        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")

---

Calculate age-, sex-, cohort-, region-specific incidence rates from a cohort

Description

Calculate age-, sex-, cohort-, region-specific incidence rates from a cohort

Usage

calc_refrates(
  df,
  dattype = NULL,
  count_var,
  refpop_df,
  calc_totals = FALSE,
  fill_sites = "no",
  region_var = NULL,
  age_var = NULL,
  sex_var = NULL,
  year_var = NULL,
  race_var = NULL,
  site_var = NULL,
  quiet = FALSE
)

Arguments

df	dataframe in long 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.
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.
calc_totals	option to calculate totals for all age-groups, all sexes, all years, all races, all sites. Default is FALSE.
fill_sites	option to fill missing sites in observed with incidence rate of 0. Needs to define the coding system used. Can be either "no" for not filling missing sites. "icd2d" for ICD-O-3 2 digit (C00-C80), "icd3d" for ICD-O-3 3digit, "icd10gm2d" for ICD-10-GM 2-digit (C00-C97), "sitewho" for Site SEER WHO coding (no 1-89 categories), "sitewho_b" for Site SEER WHO B recoding (no. 1-111 categories), "sitewho_epi" for SITE SEER WHO coding with additional sums, "sitewhogen" for SITE WHO coding with less categories to make compatible for international rates, "sitewho_num" for numeric coding of Site SEER WHO coding (no 1-89 categories), "sitewho_b_num" for numeric coding of Site SEER WHO B recoding (no. 1-111 categories), "sitewhogen_num" for numeric international rates, c("manual", char_vector) of sites manually defined
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 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.
race_var	optional argument, if rates should be calculated stratified by race. If you want to use this option, provide variable name of df that contains race information. If race_var is provided refpop_df needs to contain the variable "race".
site_var	variable in df that contains information on ICD code of case diagnosis. Cases are usually the second cancers. Default is set if dattype is given.
quiet	If TRUE, warnings and messages will be suppressed. Default is FALSE.

Value

df

Examples

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

us_second_cancer %>%
  #create variable to indicate to be counted as case
  dplyr::mutate(is_case = 1) %>%
  #calculate refrates - warning: these are not realistic numbers, just showing functionality
  calc_refrates(dattype = "seer", , count_var = "is_case", refpop_df = population_us,
               region_var = "registry", age_var = "fc_agegroup", sex_var = "sex", 
               site_var = "t_site_icd")

---

Create variable for groups of malignant neoplasms considered to be histologically 'different' for the purpose of defining multiple tumors, ICD-O-3

Description

Create variable for groups of malignant neoplasms considered to be histologically 'different' for the purpose of defining multiple tumors, ICD-O-3

Usage

histgroup_iarc(df, hist_var, new_var_hist = t_histgroupiarc, version = "3.1")

Arguments

df	dataframe in long or wide format
hist_var	variable in df that contains first 4 digits of tumor histology (without behavior)
new_var_hist	Name of the newly calculated variable for histology groups. Default is t_histgroupiarc.
version	Version of ICD-O-3 classification used. Can be either "3.0" for 2000 publication, "3.1" for 2013 first revision or "3.2" for 2019 second revision. Default is version = "3.1" for ICD-O-3 revision 1, released 2013.

Value

df

Examples

#load sample data
data("us_second_cancer")

us_second_cancer %>% 
   msSPChelpR::histgroup_iarc(., hist_var = t_hist) %>%
   dplyr::select(fake_id, t_hist, t_histgroupiarc)

---

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)

---

Calculate crude incidence rates and cross-tabulate results by break variables; cumulative FU-times as are used as xbreak_var

Description

Calculate crude incidence rates and cross-tabulate results by break variables; cumulative FU-times as are used as xbreak_var

Usage

ir_crosstab_byfutime(
  df,
  dattype = NULL,
  count_var,
  futime_breaks = c(0, 0.5, 1, 5, 10, Inf),
  ybreak_vars,
  collapse_ci = FALSE,
  add_total = "no",
  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.
futime_breaks	vector that indicates split points for follow-up time groups (in years) that will be used as xbreak_var. Default is c(0, .5, 1, 5, 10, Inf) that will result in 5 groups (up to 6 months, 6-12 months, 1-5 years, 5-10 years, 10+ years).
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".
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 %>%
                    #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)
                    
#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_byfutime(usdata_wide,
      dattype = "seer",
      count_var = "count_spc",
      futime_breaks = c(0, .5, 1, 5, 10, Inf),
      ybreak_vars = c("sex.1", "registry.1"),
      collapse_ci = FALSE,
      add_total = "no",
      futime_var = "p_futimeyrs",
      alpha = 0.05)

---

Determine patient status at specific end of follow-up - tidyverse version

Description

Determine patient status at specific end of follow-up - tidyverse version

Usage

pat_status(
  wide_df,
  fu_end = NULL,
  dattype = NULL,
  status_var = "p_status",
  life_var = NULL,
  spc_var = NULL,
  birthdat_var = NULL,
  lifedat_var = NULL,
  lifedatmin_var = NULL,
  fcdat_var = NULL,
  spcdat_var = NULL,
  life_stat_alive = NULL,
  life_stat_dead = NULL,
  spc_stat_yes = NULL,
  spc_stat_no = NULL,
  lifedat_fu_end = NULL,
  use_lifedatmin = FALSE,
  check = TRUE,
  as_labelled_factor = FALSE
)

Arguments

wide_df	dataframe in wide format
fu_end	end of follow-up in time format YYYY-MM-DD.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
status_var	Name of the newly calculated variable for patient status. Default is p_status.
life_var	Name of variable containing life status. Will override dattype preset.
spc_var	Name of variable containing SPC status. Will override dattype preset.
birthdat_var	Name of variable containing Date of Birth. Will override dattype preset.
lifedat_var	Name of variable containing Date of Death. Will override dattype preset.
lifedatmin_var	Name of variable containing the minimum Date of Death when true DoD is missing. Will override dattype preset. Will only be used if use_lifedatmin = TRUE.
fcdat_var	Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
spcdat_var	Name of variable containing Date of SPC diagnosis Will override dattype preset.
life_stat_alive	Value for alive status in life_var. Will override dattype preset.
life_stat_dead	Value for dead status in life_var. Will override dattype preset.
spc_stat_yes	Value for SPC occurred in spc_var. Will override dattype preset.
spc_stat_no	Value for no SPC in spc_var. Will override dattype preset.
lifedat_fu_end	Date of last FU of alive status in registry data. Will override dattype preset (2017-03-31 for zfkd; 2018-12-31 for seer).
use_lifedatmin	If TRUE, option to use Date of Death from lifedatmin_var when DOD is missing. Default is FALSE.
check	Check newly calculated variable p_status. Default is TRUE.
as_labelled_factor	If TRUE, output status_var as labelled factor variable. Default is FALSE.

Value

wide_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))
                                                              
#now we can run the function
msSPChelpR::pat_status(usdata_wide, 
                       fu_end = "2017-12-31", 
                       dattype = "seer", 
                       status_var = "p_status", 
                       life_var = "p_alive.1", 
                       spc_var = NULL, 
                       birthdat_var = "datebirth.1", 
                       lifedat_var = "datedeath.1",
                       use_lifedatmin = FALSE, 
                       check = TRUE, 
                       as_labelled_factor = FALSE)

---

Determine patient status at specific end of follow-up - tidytable version

Description

Determine patient status at specific end of follow-up - tidytable version

Usage

pat_status_tt(
  wide_df,
  fu_end,
  dattype = NULL,
  status_var = "p_status",
  life_var = NULL,
  spc_var = NULL,
  birthdat_var = NULL,
  lifedat_var = NULL,
  lifedatmin_var = NULL,
  fcdat_var = NULL,
  spcdat_var = NULL,
  life_stat_alive = NULL,
  life_stat_dead = NULL,
  spc_stat_yes = NULL,
  spc_stat_no = NULL,
  lifedat_fu_end = NULL,
  use_lifedatmin = FALSE,
  check = TRUE,
  as_labelled_factor = FALSE
)

Arguments

wide_df	dataframe or data.table in wide format
fu_end	end of follow-up in time format YYYY-MM-DD.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
status_var	Name of the newly calculated variable for patient status. Default is p_status.
life_var	Name of variable containing life status. Will override dattype preset.
spc_var	Name of variable containing SPC status. Will override dattype preset.
birthdat_var	Name of variable containing Date of Birth. Will override dattype preset.
lifedat_var	Name of variable containing Date of Death. Will override dattype preset.
lifedatmin_var	Name of variable containing the minimum Date of Death when true DoD is missing. Will override dattype preset. Will only be used if use_lifedatmin = TRUE.
fcdat_var	Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
spcdat_var	Name of variable containing Date of SPC diagnosis Will override dattype preset.
life_stat_alive	Value for alive status in life_var. Will override dattype preset.
life_stat_dead	Value for dead status in life_var. Will override dattype preset.
spc_stat_yes	Value for SPC occurred in spc_var. Will override dattype preset.
spc_stat_no	Value for no SPC in spc_var. Will override dattype preset.
lifedat_fu_end	Date of last FU of alive status in registry data. Will override dattype preset (2017-03-31 for zfkd; 2018-12-31 for seer).
use_lifedatmin	If TRUE, option to use Date of Death from lifedatmin_var when DOD is missing. Default is FALSE.
check	Check newly calculated variable p_status. Default is TRUE.
as_labelled_factor	If TRUE, output status_var as labelled factor variable. Default is FALSE.

Value

wide_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))
                                                              
#now we can run the function
msSPChelpR::pat_status_tt(usdata_wide, 
                       fu_end = "2017-12-31", 
                       dattype = "seer", 
                       status_var = "p_status", 
                       life_var = "p_alive.1", 
                       spc_var = NULL, 
                       birthdat_var = "datebirth.1", 
                       lifedat_var = "datedeath.1",
                       use_lifedatmin = FALSE, 
                       check = TRUE, 
                       as_labelled_factor = FALSE)

---

US Populations Data

Description

Dataset that contains different standard populations needed to run some package functions

Usage

population_us

Format

A data frame with the following variables:

region

Region / Registry

year

Year group

sex

Sex

age

Age group

race

Race

population_pyar

Population Years used for rate calculation (PYAR)

population_n_per_year

Absolute Population in single years or periods (PYAR / 5 years)]

---

Renumber the time ID per case (i.e. Tumor sequence)

Description

Renumber the time ID per case (i.e. Tumor sequence)

Usage

renumber_time_id(
  df,
  new_time_id_var,
  dattype = NULL,
  case_id_var = NULL,
  time_id_var = NULL,
  diagdat_var = NULL,
  timevar_max = Inf
)

Arguments

df	dataframe
new_time_id_var	Name of the newly calculated variable for time_id. Required.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
case_id_var	String with name of ID variable indicating same patient. E.g. case_id_var="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. time_id_var="SEQ_NUM" for SEER data.
diagdat_var	String with name of variable that indicates date of diagnosis per event. E.g. diagdat_var="t_datediag" for SEER data.
timevar_max	Numeric; default Inf. Maximum number of cases per id. All tumors > timevar_max will be deleted.

Value

df

Examples

data(us_second_cancer)
us_second_cancer %>%
 #only select first 10000 rows so example runs faster
 dplyr::slice(1:10000) %>%
 msSPChelpR::renumber_time_id(new_time_id_var = "t_tumid",
                             dattype = "seer",
                             case_id_var = "fake_id")

---

Renumber the time ID per case (i.e. Tumor sequence) - tidytable version

Description

Renumber the time ID per case (i.e. Tumor sequence) - tidytable version

Usage

renumber_time_id_tt(
  df,
  new_time_id_var,
  dattype = NULL,
  case_id_var = NULL,
  time_id_var = NULL,
  diagdat_var = NULL,
  timevar_max = Inf
)

Arguments

df	dataframe
new_time_id_var	Name of the newly calculated variable for time_id. Required.
dattype	can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
case_id_var	String with name of ID variable indicating same patient. E.g. case_id_var="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. time_id_var="SEQ_NUM" for SEER data.
diagdat_var	String with name of variable that indicates date of diagnosis per event. E.g. diagdat_var="t_datediag" for SEER data.
timevar_max	Numeric; default Inf. Maximum number of cases per id. All tumors > timevar_max will be deleted.

Value

df

Examples

data(us_second_cancer)
us_second_cancer %>%
 #only select first 10000 rows so example runs faster
 dplyr::slice(1:10000) %>%
 msSPChelpR::renumber_time_id_tt(new_time_id_var = "t_tumid",
                             dattype = "seer",
                             case_id_var = "fake_id")

---

Reshape dataset to long format - stats::reshape version

Description

Reshape dataset to long format - stats::reshape version

Usage

reshape_long(wide_df, case_id_var, time_id_var, datsize = Inf, chunks = 1)

Arguments

wide_df	dataframe in wide format
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.
chunks	Numeric; default 1. Technical parameter how the data is split during reshaping.

Value

long df

Examples

data(us_second_cancer)

#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

#now we can reshape long again
msSPChelpR::reshape_long(usdata_wide_sample,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM")

---

Reshape dataset to wide format - tidyr version

Description

Reshape dataset to wide format - tidyr version

Usage

reshape_long_tidyr(wide_df, case_id_var, time_id_var, datsize = Inf)

Arguments

wide_df	dataframe
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.

Value

long_df

Examples

data(us_second_cancer)

#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

#now we can reshape long again
msSPChelpR::reshape_long_tidyr(usdata_wide_sample,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM")

---

Reshape dataset to wide format - tidytable version

Description

Reshape dataset to wide format - tidytable version

Usage

reshape_long_tt(wide_df, case_id_var, time_id_var, datsize = Inf)

Arguments

wide_df	dataframe
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.

Value

long_df

Examples

data(us_second_cancer)

#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

#now we can reshape long again
msSPChelpR::reshape_long_tt(usdata_wide_sample,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM")

---

Reshape dataset to wide format

Description

Reshape dataset to wide format

Usage

reshape_wide(
  df,
  case_id_var,
  time_id_var,
  timevar_max = 6,
  datsize = Inf,
  chunks = 10
)

Arguments

df	dataframe
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
timevar_max	Numeric; default 6. Maximum number of cases per id. All tumors > timevar_max will be deleted before reshaping.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.
chunks	Numeric; default 10. Technical parameter how the data is split during reshaping.

Value

df

Examples

data(us_second_cancer)

msSPChelpR::reshape_wide(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

---

Reshape dataset to wide format - tidyr version

Description

Reshape dataset to wide format - tidyr version

Usage

reshape_wide_tidyr(
  df,
  case_id_var,
  time_id_var,
  timevar_max = 6,
  datsize = Inf
)

Arguments

df	dataframe
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
timevar_max	Numeric; default 6. Maximum number of cases per id. All tumors > timevar_max will be deleted before reshaping.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.

Value

df

Examples

data(us_second_cancer)

msSPChelpR::reshape_wide_tidyr(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

---

Reshape dataset to wide format - tidytable version

Description

Reshape dataset to wide format - tidytable version

Usage

reshape_wide_tt(df, case_id_var, time_id_var, timevar_max = 6, datsize = Inf)

Arguments

df	dataframe
case_id_var	String with name of ID variable indicating same patient. E.g. idvar="PUBCSNUM" for SEER data.
time_id_var	String with name of variable that indicates diagnosis per patient. E.g. timevar="SEQ_NUM" for SEER data.
timevar_max	Numeric; default 6. Maximum number of cases per id. All tumors > timevar_max will be deleted before reshaping.
datsize	Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.

Value

wide_df

Examples

data(us_second_cancer)

msSPChelpR::reshape_wide_tt(us_second_cancer,
                         case_id_var = "fake_id", 
                         time_id_var = "SEQ_NUM", 
                         timevar_max = 2,
                         datsize = 10000)

---

Calculate standardized incidence ratios with custom grouping variables stratified by follow-up time

Description

Calculate standardized incidence ratios with custom grouping variables stratified by follow-up time

Usage

sir_byfutime(
  df,
  dattype = NULL,
  ybreak_vars = "none",
  xbreak_var = "none",
  futime_breaks = c(0, 0.5, 1, 5, 10, Inf),
  count_var,
  refrates_df = rates,
  calc_total_row = TRUE,
  calc_total_fu = TRUE,
  region_var = NULL,
  age_var = NULL,
  sex_var = NULL,
  year_var = NULL,
  race_var = NULL,
  site_var = NULL,
  futime_var = NULL,
  expect_missing_refstrata_df = NULL,
  alpha = 0.05,
  quiet = FALSE
)

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.
ybreak_vars	variables from df by which SIRs should be stratified in result df. Multiple variables will result in appended rows in result df. Careful: do not chose any variables that are dependent on occurrence of count_var (e.g. Histology of second cancer). If y_break_vars = "none", no stratification is performed. Default is "none".
xbreak_var	One variable from df by which SIRs should be stratified as a second dimension in result df. This variable will be added as a second stratification dimension to ybreak_vars and all variables will be calculated for subpopulations of x and y combinations. Careful: do not chose any variables that are dependent on occurrence of count_var (e.g. Year of second cancer). If y_break_vars = "none", no stratification is performed. Default is "none".
futime_breaks	vector that indicates split points for follow-up time groups (in years) that will be used as xbreak_var. Default is c(0, .5, 1, 5, 10, Inf) that will result in 5 groups (up to 6 months, 6-12 months, 1-5 years, 5-10 years, 10+ years). If you don't want to split by follow-up time, use futime_breaks = "none".
count_var	variable to be counted as observed case. Cases are usually the second cancers. Should be 1 for case to be counted.
refrates_df	df where reference rate from general population are defined. It is assumed that refrates_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), "incidence_crude_rate" for incidence rate in the respective age/sex/year cohort.The variable "race" is additionally required if the option "race_var" is used. refrates_df must use the same category coding of age, sex, region, year and t_site as age_var, sex_var, region_var, year_var and site_var.
calc_total_row	option to calculate a row of totals. Can be either FALSE for not adding such a row or TRUE for adding it at the first row. Default is TRUE.
calc_total_fu	option to calculate totals for follow-up time. Can be either FALSE for not adding such a column or TRUE for adding. Default is TRUE.
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 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.
race_var	optional argument, if SIR should be calculated stratified by race. If you want to use this option, provide variable name of df that contains race information. If race_var is provided refrates_df needs to contain the variable "race".
site_var	variable in df that contains information on site or subsite (e.g. ICD code, SEER site code or others that matches t_site in refrates_df) of case diagnosis. Cases are usually the second cancers. Default is set if dattype is given.
futime_var	variable in df that contains follow-up time per person between date of first cancer and any of death, date of event (case), end of FU date (in years; whatever event comes first). Default is set if dattype is given.
expect_missing_refstrata_df	optional argument, if strata with missing refrates are expected, because incidence rates of value 0 are not explicit, but missing from refrates_df. It is assumed that expect_missing_refstrata_df is a data.frame 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), and "t_site" for The variable "race" is additionally required if the option "race_var" is used. refrates_df must use the same category coding of age, sex, region, year and t_site as age_var, sex_var, region_var, year_var and site_var.
alpha	significance level for confidence interval calculations. Default is alpha = 0.05 which will give 95 percent confidence intervals.
quiet	If TRUE, warnings and messages will be suppressed. Default is FALSE.

Examples

#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run: 
usdata_wide %>%
  sir_byfutime(
        dattype = "seer",
        ybreak_vars = c("race.1", "t_dco.1"),
        xbreak_var = "none",
        futime_breaks = c(0, 1/12, 2/12, 1, 5, 10, Inf),
        count_var = "count_spc",
        refrates_df = us_refrates_icd2,
        calc_total_row = TRUE,
        calc_total_fu = TRUE,
        region_var = "registry.1",
        age_var = "fc_agegroup.1",
        sex_var = "sex.1",
        year_var = "t_yeardiag.1",
        site_var = "t_site_icd.1", #using grouping by second cancer incidence
        futime_var = "p_futimeyrs",
        alpha = 0.05)
        
## End(Not run)

---

Calculate Ratio of two SIRs or SMRs

Description

Calculate ratio of two SIRs by providing observed and expected counts to sir_ratio The related functions sir_ratio_lci and sir_ratio_uci can also calculate lower and upper estimates of the confidence interval Calculations are based on formulas suggested by Breslow & Day 1987

Usage

sir_ratio(o1, o2, e1, e2)

sir_ratio_lci(o1, o2, e1, e2, alpha = 0.05)

sir_ratio_uci(o1, o2, e1, e2, alpha = 0.05)

Arguments

o1	observed count for SIR 1
o2	observed count for SIR 2
e1	expected count for SIR 1
e2	observed count for SIR 2
alpha	alpha significance level for confidence interval calculations. Default is alpha = 0.05 which will give 95 percent confidence intervals.

Value

num numeric value of SIR / SMR estimate

References

Breslow NE, Day NE. Statistical Methods in Cancer Research Volume II: The Design and Analysis of Cohort Studies. Lyon, France: IARC; 1987. (IARC Scientific Publications IARC Scientific Publications No. 82). Available from: http://publications.iarc.fr/Book-And-Report-Series/Iarc-Scientific-Publications/Statistical-Methods-In-Cancer-Research-Volume-II-The-Design-And-Analysis-Of-Cohort-Studies-1986

Examples

#provide the two expected and observed count to get the ratio of SIRs/SMRs
msSPChelpR::sir_ratio(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123)

#calculate lower confidence limit
msSPChelpR::sir_ratio_lci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)

#calculate upper confidence limit
msSPChelpR::sir_ratio_uci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)

#functions can be easily used inside dplyr::mutate function
library(dplyr)
test_df <- data.frame(sir_oth = c(1.07, 1.36, 0.96), 
                  sir_smo = c(1.49, 1.81, 1.41),
                  observed_oth = c(2140, 748, 1392),
                  expected_oth = c(1993, 550, 1443),
                  observed_smo = c(3158, 744, 2414),
                  expected_smo = c(2123, 412, 1711))

test_df %>%
  mutate(smo_ratio = sir_ratio(observed_oth, observed_smo, expected_oth, expected_smo),
         smo_ratio_lci = sir_ratio_lci(observed_oth, observed_smo, expected_oth, expected_smo),
         smo_ratio_uci = sir_ratio_uci(observed_oth, observed_smo, expected_oth, expected_smo))

---

Standard Populations Data

Description

Dataset that contains different standard populations needed to run some package functions

Usage

standard_population

Format

A data frame with the following variables:

standard_pop

Standard Population

sex

Sex

age

Age group

population_n

Absolute Population number in standard population age group

group_proportion

Proportion of age-group in gender-specific total population

---

Summarize detailed SIR results

Description

Summarize detailed SIR results

Usage

summarize_sir_results(
  sir_df,
  summarize_groups,
  summarize_site = FALSE,
  output = "long",
  output_information = "full",
  add_total_row = "no",
  add_total_fu = "no",
  collapse_ci = FALSE,
  shorten_total_cols = FALSE,
  fubreak_var_name = "fu_time",
  ybreak_var_name = "yvar_name",
  xbreak_var_name = "none",
  site_var_name = "t_site",
  alpha = 0.05
)

Arguments

sir_df	dataframe with stratified sir results created using the sir or sir_byfutime functions
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 age, sex, region, year. Define multiple summarize variables e.g. by summarize_groups = c("region", "sex", "year")
summarize_site	If TRUE results will be summarized over all t_site categories. Default is FALSE.
output	Define the format of the output. Can be either "nested" for nested dataframe with fubreak_var and xbreak_var in separate sub_tables (purrr). Or "wide" for wide format where fubreak_var and xbreak_var are appended as columns. Or "long" for long format where sir_df is not reshaped, but just summarized (ybreak_var, xbreak_var and fubreak_var remain in rows). Default is "long".
output_information	option to define information to be presented in final output table. Default is "full" information, i.e. all variables from from sir_df. "reduced" is observed, expected, sir, sir_ci / sir_lci+sir_uci, pyar, n_base. "minimal" is observed, expected, sir, sir_ci. Default is "full".
add_total_row	option to add a row of totals. Can be either "no" for not adding such a row or "start" or "end" for adding it at the first or last row or "only" for only showing totals and no yvar. Default is "no".
add_total_fu	option to add totals for follow-up time. Can be either "no" for not adding such a column or "start" or "end" for adding it at the first or last column or "only" for only showing follow-up time totals. Default is "no".
collapse_ci	If TRUE upper and lower confidence interval will be collapsed into one column separated by "-". Default is FALSE.
shorten_total_cols	Shorten text in all results columns that start with "Total". Default == FALSE.
fubreak_var_name	Name of variable with futime stratification. Default is "fu_time".
ybreak_var_name	Name of variable with futime stratification. Default is "yvar_name".
xbreak_var_name	Name of variable with futime stratification. Default is "xvar_name".
site_var_name	Name of variable with site stratification. Default is "t_site".
alpha	significance level for confidence interval calculations. Default is alpha = 0.05 which will give 95 percent confidence intervals.

Examples

#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run: 
summarize_sir_results(.,
    summarize_groups = c("region", "age", "year", "race"),
    summarize_site = TRUE,
    output = "long",  output_information = "minimal",
    add_total_row = "only",  add_total_fu = "no",
    collapse_ci = FALSE,  shorten_total_cols = TRUE,
    fubreak_var_name = "fu_time", ybreak_var_name = "yvar_name",
    xbreak_var_name = "none", site_var_name = "t_site",
    alpha = 0.05
    )
    
## End(Not run)

---

US Reference Rates for Cancer Data (ICD-O 2digit code)

Description

Synthetic dataset of reference incidence rates for the US population to demonstrate package functions Cancer site is coded using ICD-O 2digit code

Usage

us_refrates_icd2

Format

A data frame with the following variables:

t_site

Tumor Site

region

Region / Region groups

year

Year / Periods

sex

Sex

age

Age / Age groups

race

Race

comment

Comment

incidence_cases

Incident Cases (raw count)

incidence_crude_rate

Incidence Rate (crude rate)

population_pyar

Population Years used for rate calculation (PYAR)

population_n_per_year

Absolute Population number used for rate calculation (PYAR / 5 years)

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US Second Cancer Data

Description

Synthetic dataset of patients with cancer to demonstrate package functions

Usage

us_second_cancer

Format

A data frame with the following variables:

fake_id

ID of patient

SEQ_NUM

Original tumor sequence

registry

SEER registry

sex

Biological sex of patient

race

Race

datebirth

Date of birth

t_datediag

Date of diagnosis of tumor

t_site_icd

Primary site of tumor in ICD-O coding

t_hist

Histology, i.e. ICD-O-3-Code on tumor morphology (4 digits)

t_dco

Tumor diagnosis is based on Death Certificate only

fc_age

Age at first primary cancer in years

datedeath

Date of death

p_alive

Patient alive at end of follow-up 2019

p_dodmin

Minimum Date of Death if datedeath is missing

fc_agegroup

Age group of first cancer diagnosis

t_yeardiag

Time period of diagnosis of tumor

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Determine vital status at end of follow-up depending on pat_status - tidyverse version

Description

Determine vital status at end of follow-up depending on pat_status - tidyverse version

Usage

vital_status(
  wide_df,
  status_var = "p_status",
  life_var_new = "p_alive",
  check = TRUE,
  as_labelled_factor = FALSE
)

Arguments

wide_df	dataframe in wide format
status_var	Name of the patient status variable that was previously created. Default is p_status.
life_var_new	Name of the newly calculated variable for patient vital status. Default is p_alive.
check	Check newly calculated variable life_var_new by printing frequency table. Default is TRUE.
as_labelled_factor	If true, output life_var_new as labelled factor variable. Default is FALSE.

Value

wide_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
msSPChelpR::vital_status(usdata_wide, 
                        status_var = "p_status",
                        life_var_new = "p_alive_new", 
                        check = TRUE, 
                        as_labelled_factor = FALSE)

---

Determine vital status at end of follow-up depending on pat_status - tidytable version

Description

Determine vital status at end of follow-up depending on pat_status - tidytable version

Usage

vital_status_tt(
  wide_df,
  status_var = "p_status",
  life_var_new = "p_alive",
  check = TRUE,
  as_labelled_factor = FALSE
)

Arguments

wide_df	dataframe or data.table in wide format
status_var	Name of the patient status variable that was previously created. Default is p_status.
life_var_new	Name of the newly calculated variable for patient vital status. Default is p_alive.
check	Check newly calculated variable life_var_new by printing frequency table. Default is TRUE.
as_labelled_factor	If true, output life_var_new as labelled factor variable. Default is FALSE.

Value

wide_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
msSPChelpR::vital_status_tt(usdata_wide, 
                        status_var = "p_status",
                        life_var_new = "p_alive_new", 
                        check = TRUE, 
                        as_labelled_factor = FALSE)

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