R/calc_futime.R
calc_futime.RdCalculate follow-up time per case until end of follow-up depending on pat_status - tidyverse version
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
)dataframe in wide format
Name of the newly calculated variable for follow-up time. Default is p_futimeyrs.
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.
Check newly calculated variable p_status by printing frequency table. Default is TRUE.
Unit of follow-up time (can be "days", "weeks", "months", "years"). Default is "years".
Name of the patient status variable that was previously created. Default is p_status.
Name of variable containing Date of Death. Will override dattype preset.
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.
If TRUE, warnings and messages will be suppressed. 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))
#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")
#> # A tibble: 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 1 2566
#> 6 Dead 2 2086
#> 7 Dead 3 18169
#> 8 Dead 4 8676
#> 9 Dead 97 2
#> 10 Dead 98 147
#> 11 Dead NA 5
#> # A tibble: 7 × 2
#> p_status n
#> <dbl> <int>
#> 1 1 18617
#> 2 2 19902
#> 3 3 18169
#> 4 4 8676
#> 5 97 21
#> 6 98 2670
#> 7 NA 5
#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")
#> Warning: There were 6 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `min_futime = min(.data[["p_futimeyrs"]], na.rm = TRUE)`.
#> ℹ In group 5: `p_status = 97`.
#> Caused by warning in `min()`:
#> ! no non-missing arguments to min; returning Inf
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 5 remaining warnings.
#> # A tibble: 7 × 5
#> p_status mean_futime min_futime max_futime median_futime
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 9.23 0.0438 27.0 8.04
#> 2 2 8.93 0 26.9 7.76
#> 3 3 8.65 0 25.9 7.54
#> 4 4 6.46 0 25.3 5.33
#> 5 97 NaN Inf -Inf NA
#> 6 98 NaN Inf -Inf NA
#> 7 NA NaN Inf -Inf NA
#> # A tibble: 68,060 × 131
#> 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 123 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>, …