R/ir_crosstab_byfutime.R
ir_crosstab_byfutime.RdCalculate crude incidence rates and cross-tabulate results by break variables; cumulative FU-times as are used as xbreak_var
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
)dataframe in wide format
can be "zfkd" or "seer" or NULL. Will set default variable names if dattype is "seer" or "zfkd". Default is NULL.
variable to be counted as observed case. Should be 1 for case to be counted.
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).
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.
If TRUE upper and lower confidence interval will be collapsed into one column separated by "-". Default is FALSE.
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".
variable in df that contains follow-up time per person (in years). Default is set if dattype is given.
significance level for confidence interval calculations. Default is alpha = 0.05 which will give 95 percent confidence intervals.
df
#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)
#> Long dataset had too many cases per patient. Wide dataset is limited to 2 cases per id as defined in timevar_max option.
#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: 8 × 3
#> p_alive.1 p_status n
#> <chr> <dbl> <int>
#> 1 Alive 1 1768
#> 2 Alive 2 1893
#> 3 Alive 98 288
#> 4 Dead 1 278
#> 5 Dead 2 236
#> 6 Dead 3 2009
#> 7 Dead 4 1000
#> 8 Dead 98 14
#> # A tibble: 5 × 2
#> p_status n
#> <dbl> <int>
#> 1 1 2046
#> 2 2 2129
#> 3 3 2009
#> 4 4 1000
#> 5 98 302
#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")
#> Warning: There were 2 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 = 98`.
#> Caused by warning in `min()`:
#> ! no non-missing arguments to min; returning Inf
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#> # A tibble: 5 × 5
#> p_status mean_futime min_futime max_futime median_futime
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 9.14 0.0438 27.0 8.04
#> 2 2 8.84 0 26.8 7.67
#> 3 3 8.40 0 25.6 7.29
#> 4 4 6.44 0 24.5 5.08
#> 5 98 NaN Inf -Inf NA
#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)
#> Warning: The variable for follow-up time has: 309 missings. These will be omitted when creating the crosstabs.
#> yvar_name yvar_label to 6 months_n_base
#> 1 sex.1 Female 3609
#> 2 sex.1 Male 3568
#> 3 registry.1 SEER Reg 01 - San Francisco-Oakland SMSA 2427
#> 4 registry.1 SEER Reg 02 - Connecticut 1573
#> 5 registry.1 SEER Reg 20 - Detroit (Metropolitan) 2398
#> 6 registry.1 SEER Reg 21 - Hawaii 779
#> to 6 months_observed to 6 months_pyar to 6 months_abs_ir
#> 1 109 1752 6222.35
#> 2 120 1727 6948.03
#> 3 75 1178 6369.30
#> 4 61 760 8031.43
#> 5 69 1164 5925.41
#> 6 24 377 6360.26
#> to 6 months_abs_ir_lci to 6 months_abs_ir_uci 6-12 months_n_base
#> 1 5109.20 7506.01 3425
#> 2 5760.61 8308.14 3372
#> 3 5009.86 7983.98 2306
#> 4 6143.41 10316.71 1477
#> 5 4610.32 7498.98 2282
#> 6 4075.14 9463.57 732
#> 6-12 months_observed 6-12 months_pyar 6-12 months_abs_ir
#> 1 63 1674 3762.84
#> 2 65 1650 3939.45
#> 3 38 1130 3364.15
#> 4 34 719 4728.90
#> 5 45 1118 4024.94
#> 6 11 358 3075.44
#> 6-12 months_abs_ir_lci 6-12 months_abs_ir_uci 1-5 years_n_base
#> 1 2891.47 4814.31 3277
#> 2 3040.39 5021.16 3236
#> 3 2380.67 4617.56 2216
#> 4 3274.90 6608.17 1402
#> 5 2935.82 5385.68 2194
#> 6 1535.25 5502.82 701
#> 1-5 years_observed 1-5 years_pyar 1-5 years_abs_ir 1-5 years_abs_ir_lci
#> 1 503 11045 4554.05 4164.71
#> 2 500 10867 4601.01 4206.51
#> 3 334 7505 4450.20 3985.68
#> 4 221 4713 4689.58 4091.64
#> 5 338 7393 4571.73 4097.28
#> 6 110 2301 4780.17 3928.72
#> 1-5 years_abs_ir_uci 5-10 years_n_base 5-10 years_observed 5-10 years_pyar
#> 1 4969.99 2270 510 8758
#> 2 5022.55 2263 511 8776
#> 3 4953.99 1563 349 6122
#> 4 5350.32 964 220 3656
#> 5 5086.04 1528 346 5928
#> 6 5761.40 478 106 1828
#> 5-10 years_abs_ir 5-10 years_abs_ir_lci 5-10 years_abs_ir_uci
#> 1 5823.20 5328.71 6351.22
#> 2 5822.85 5328.87 6350.31
#> 3 5701.06 5118.55 6331.69
#> 4 6018.15 5249.13 6868.14
#> 5 5836.51 5237.66 6485.07
#> 6 5797.53 4746.54 7011.95
#> 10+ years_n_base 10+ years_observed 10+ years_pyar 10+ years_abs_ir
#> 1 1304 685 7360 9306.76
#> 2 1298 693 7164 9673.73
#> 3 912 479 5110 9374.06
#> 4 532 295 3031 9731.33
#> 5 891 474 4777 9921.71
#> 6 267 130 1605 8098.28
#> 10+ years_abs_ir_lci 10+ years_abs_ir_uci
#> 1 8622.77 10030.58
#> 2 8966.80 10421.57
#> 3 8553.27 10252.36
#> 4 8652.42 10907.58
#> 5 9048.51 10856.44
#> 6 6766.09 9616.05