R/pat_status_tt.R
pat_status_tt.RdDetermine patient status at specific end of follow-up - tidytable version
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
)dataframe or data.table in wide format
end of follow-up in time format YYYY-MM-DD.
can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
Name of the newly calculated variable for patient status. Default is p_status.
Name of variable containing life status. Will override dattype preset.
Name of variable containing SPC status. Will override dattype preset.
Name of variable containing Date of Birth. Will override dattype preset.
Name of variable containing Date of Death. Will override dattype preset.
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.
Name of variable containing Date of Primary Cancer diagnosis. Will override dattype preset.
Name of variable containing Date of SPC diagnosis Will override dattype preset.
Value for alive status in life_var. Will override dattype preset.
Value for dead status in life_var. Will override dattype preset.
Value for SPC occurred in spc_var. Will override dattype preset.
Value for no SPC in spc_var. Will override dattype preset.
Date of last FU of alive status in registry data. Will override dattype preset (2017-03-31 for zfkd; 2018-12-31 for seer).
If TRUE, option to use Date of Death from lifedatmin_var when DOD is missing. Default is FALSE.
Check newly calculated variable p_status. Default is TRUE.
If TRUE, output status_var as labelled factor variable. Default is FALSE.
wide_df
#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)
#> # A tidytable: 11 × 3
#> p_alive.1 p_status n
#> <chr> <dbl> <int>
#> 1 Alive 1 16051
#> 2 Alive 2 17816
#> 3 Alive 97 19
#> 4 Alive 98 2523
#> 5 Dead NA 5
#> 6 Dead 1 2566
#> 7 Dead 2 2086
#> 8 Dead 3 18169
#> 9 Dead 4 8676
#> 10 Dead 97 2
#> 11 Dead 98 147
#> # A tidytable: 7 × 2
#> p_status n
#> <dbl> <int>
#> 1 NA 5
#> 2 1 18617
#> 3 2 19902
#> 4 3 18169
#> 5 4 8676
#> 6 97 21
#> 7 98 2670
#> # A tidytable: 68,060 × 130
#> fake_id registry.1 sex.1 race.1 datebirth.1 t_datediag.1 t_site_icd.1 t_dco.1
#> <chr> <chr> <chr> <chr> <date> <date> <chr> <chr>
#> 1 100004 SEER Reg … Male White 1926-01-01 1992-07-15 C50 histol…
#> 2 100034 SEER Reg … Male White 1979-01-01 2000-06-15 C50 histol…
#> 3 100037 SEER Reg … Fema… White 1938-01-01 1996-01-15 C54 histol…
#> 4 100038 SEER Reg … Male White 1989-01-01 1991-04-15 C50 histol…
#> 5 100039 SEER Reg … Fema… White 1946-01-01 2003-08-15 C50 histol…
#> 6 100047 SEER Reg … Fema… White 1927-01-01 1998-04-15 C50 histol…
#> 7 100057 SEER Reg … Male Black 1961-01-01 2010-04-15 C18 histol…
#> 8 100060 SEER Reg … Fema… White 1947-01-01 2003-08-15 C50 histol…
#> 9 100063 SEER Reg … Fema… Black 1938-01-01 1995-12-15 C50 histol…
#> 10 100073 SEER Reg … Male White 1960-01-01 1993-11-15 C44 histol…
#> # ℹ 68,050 more rows
#> # ℹ 122 more variables: t_hist.1 <int>, fc_age.1 <int>, datedeath.1 <date>,
#> # p_alive.1 <chr>, p_dodmin.1 <date>, fc_agegroup.1 <chr>,
#> # t_yeardiag.1 <chr>, registry.2 <chr>, sex.2 <chr>, race.2 <chr>,
#> # datebirth.2 <date>, t_datediag.2 <date>, t_site_icd.2 <chr>, t_dco.2 <chr>,
#> # t_hist.2 <int>, fc_age.2 <int>, datedeath.2 <date>, p_alive.2 <chr>,
#> # p_dodmin.2 <date>, fc_agegroup.2 <chr>, t_yeardiag.2 <chr>, …