creat any_2

creat any_3

add varaibles

new cr_cys_7

new cr_cys_7 color

any_3 final_master_sarcopenia

plot_adj_cif_cr_cys_7(
   data   = final_master_sarcopenia,
   time   = cmp_time_any_3,  
   status = cmp_status_any_3,
   title  = "any_3 final_master_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

any_3 final_master_no_sarcopenia

plot_adj_cif_cr_cys_7_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_any_3,  
   status = cmp_status_any_3,
   title  = "any_3 final_master_no_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

side_effect final_master_sarcopenia

plot_adj_cif_cr_cys_7(
   data   = final_master_sarcopenia,
   time   = cmp_time_side_effect,  # Pass as a quoted string
   status = cmp_status_side_effect,
   title  = "side_effect final_master_sarcopenia"
)

side_effect final_master_no_sarcopenia

plot_adj_cif_cr_cys_7_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_side_effect,  # Pass as a quoted string
   status = cmp_status_side_effect,
   title  = "side_effect final_master_no_sarcopenia"
)

hosp final_master_sarcopenia

plot_adj_cif_cr_cys_7(
   data   = final_master_sarcopenia,
   time   = cmp_time_hosp,  # Pass as a quoted string
   status = cmp_status_hosp,
   title  = "hosp final_master_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_step()`).
## Removed 1 row containing missing values or values outside the scale range
## (`geom_step()`).

hosp final_master_no_sarcopenia

plot_adj_cif_cr_cys_7_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_hosp,  # Pass as a quoted string
   status = cmp_status_hosp,
   title  = "hosp final_master_no_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

#1-KM final_master_sarcopenia 365

#1-KM final_master_sarcopenia 90

#1-KM final_master_no_sarcopenia 365

#1-KM final_master_no_sarcopenia 90

new cys_c_egfr_ge_60

new cys_c_egfr_ge_60 color

any_3 final_master_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60(
   data   = final_master_sarcopenia,
   time   = cmp_time_any_3,  
   status = cmp_status_any_3,
   title  = "any_3 final_master_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

any_3 final_master_no_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_any_3,  
   status = cmp_status_any_3,
   title  = "any_3 final_master_no_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

side_effect final_master_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60(
   data   = final_master_sarcopenia,
   time   = cmp_time_side_effect,  # Pass as a quoted string
   status = cmp_status_side_effect,
   title  = "side_effect final_master_sarcopenia"
)

side_effect final_master_no_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_side_effect,  # Pass as a quoted string
   status = cmp_status_side_effect,
   title  = "side_effect final_master_no_sarcopenia"
)

hosp final_master_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60(
   data   = final_master_sarcopenia,
   time   = cmp_time_hosp,  # Pass as a quoted string
   status = cmp_status_hosp,
   title  = "hosp final_master_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

hosp final_master_no_sarcopenia

plot_adj_cif_cys_c_egfr_ge_60_color(
   data   = final_master_no_sarcopenia,
   time   = cmp_time_hosp,  # Pass as a quoted string
   status = cmp_status_hosp,
   title  = "hosp final_master_no_sarcopenia"
)
## Warning in check_inputs_adjustedcif(data = data, variable = variable, ev_time =
## ev_time, : It is recommended to use the 'adjustedsurv' function when the
## 'event' variable is binary.

#1-KM final_master_sarcopenia 365

#1-KM final_master_sarcopenia 90

#1-KM final_master_no_sarcopenia 365

#1-KM final_master_no_sarcopenia 90

#1 Grade 3 z score

#2 Death z score

#3 platinum_related_hos

#4 Overall Hospitalization

#Table one ## for cre/cys ratio

##dose_1: carbo-AUC ≥ 5 mg/mL/min, or r cisplatin  ≥ 75 mg/m2
table_one <- final_master%>%
  dplyr::select(age,age_cat,sex,race,ethnicity,cancer_stage,
            
                ici,Bevacizumab,Paclitaxel,Pemetrexed,Doxorubicin,Etoposide,Gemcitabine,Pertuzumab,Trastuzumab,Docetaxel,Fluorouracil,Methotrexate,Vinblastine,Administered_alone,
                dm,htn,cirrhosis,cad,hiv,ppi,ace_arb,diu,steroids,statins,nsaids,smoking,thyroid,
                ECOG_Score,ecog_score_2,bmi,bmi_cat,pre_HGB_45days,pre_PLT_45days,pre_ALB_45days,baseline_cystatin_c,baseline_cre,cockcroft,baseline_cre_egfr,baseline_cys_c_egfr,cr_cys_7,sarcopenia)

all_vars <- names(table_one)

num_vars <- c(
  "age","bmi","pre_HGB_45days","pre_PLT_45days","pre_ALB_45days",
  "baseline_cystatin_c","baseline_cre","cockcroft",
  "baseline_cre_egfr","baseline_cys_c_egfr"
)

cat_vars <- setdiff(names(table_one),num_vars)

table_one <- table_one %>% mutate_at(cat_vars,as.factor)



 # T1 <- tableone::CreateTableOne(
 #    vars       = all_vars,
 #    strata     = "sarcopenia",        # variable to stratify by
 #    data       = table_one,
 #    factorVars = cat_vars,      # specify which variables are factors
 #    addOverall = TRUE,
 #    includeNA  = FALSE
 #  )
 # 
t1 <- table_one %>%
  tbl_summary(
    by = sarcopenia,
    statistic = list(
      all_continuous()  ~ "{mean} ({sd})",
      all_categorical() ~ "{n} ({p}%)"
    ),
    digits = all_continuous() ~ 1,
    missing = "ifany"
  ) %>%
  add_overall() %>%
  bold_labels()

t1
Characteristic Overall
N = 4581		 0
N = 3711		 1
N = 871
age 65.2 (12.1) 63.9 (12.4) 71.0 (8.4)
age_cat


    1 194 (42%) 175 (47%) 19 (22%)
    2 228 (50%) 172 (46%) 56 (64%)
    3 36 (7.9%) 24 (6.5%) 12 (14%)
sex


    1 197 (43%) 132 (36%) 65 (75%)
    2 261 (57%) 239 (64%) 22 (25%)
race


    1 370 (81%) 300 (81%) 70 (80%)
    2 18 (3.9%) 15 (4.0%) 3 (3.4%)
    3 18 (3.9%) 11 (3.0%) 7 (8.0%)
    4 16 (3.5%) 15 (4.0%) 1 (1.1%)
    5 36 (7.9%) 30 (8.1%) 6 (6.9%)
ethnicity


    1 387 (84%) 306 (82%) 81 (93%)
    2 39 (8.5%) 37 (10.0%) 2 (2.3%)
    3 32 (7.0%) 28 (7.5%) 4 (4.6%)
cancer_stage


    1 128 (28%) 109 (29%) 19 (22%)
    2 330 (72%) 262 (71%) 68 (78%)
ici


    0 351 (77%) 298 (80%) 53 (61%)
    1 107 (23%) 73 (20%) 34 (39%)
Bevacizumab


    0 443 (97%) 356 (96%) 87 (100%)
    1 15 (3.3%) 15 (4.0%) 0 (0%)
Paclitaxel


    0 283 (62%) 224 (60%) 59 (68%)
    1 175 (38%) 147 (40%) 28 (32%)
Pemetrexed


    0 351 (77%) 288 (78%) 63 (72%)
    1 107 (23%) 83 (22%) 24 (28%)
Doxorubicin


    0 447 (98%) 360 (97%) 87 (100%)
    1 11 (2.4%) 11 (3.0%) 0 (0%)
Etoposide


    0 413 (90%) 333 (90%) 80 (92%)
    1 45 (9.8%) 38 (10%) 7 (8.0%)
Gemcitabine


    0 398 (87%) 334 (90%) 64 (74%)
    1 60 (13%) 37 (10.0%) 23 (26%)
Pertuzumab


    0 432 (94%) 345 (93%) 87 (100%)
    1 26 (5.7%) 26 (7.0%) 0 (0%)
Trastuzumab


    0 430 (94%) 343 (92%) 87 (100%)
    1 28 (6.1%) 28 (7.5%) 0 (0%)
Docetaxel


    0 426 (93%) 340 (92%) 86 (99%)
    1 32 (7.0%) 31 (8.4%) 1 (1.1%)
Fluorouracil


    0 455 (99%) 368 (99%) 87 (100%)
    1 3 (0.7%) 3 (0.8%) 0 (0%)
Methotrexate


    0 455 (99%) 368 (99%) 87 (100%)
    1 3 (0.7%) 3 (0.8%) 0 (0%)
Vinblastine


    0 455 (99%) 368 (99%) 87 (100%)
    1 3 (0.7%) 3 (0.8%) 0 (0%)
Administered_alone


    0 433 (95%) 350 (94%) 83 (95%)
    1 25 (5.5%) 21 (5.7%) 4 (4.6%)
dm


    0 378 (83%) 312 (84%) 66 (76%)
    1 80 (17%) 59 (16%) 21 (24%)
htn


    0 175 (38%) 154 (42%) 21 (24%)
    1 283 (62%) 217 (58%) 66 (76%)
cirrhosis


    0 436 (95%) 356 (96%) 80 (92%)
    1 22 (4.8%) 15 (4.0%) 7 (8.0%)
cad


    0 330 (72%) 279 (75%) 51 (59%)
    1 128 (28%) 92 (25%) 36 (41%)
hiv


    0 434 (95%) 349 (94%) 85 (98%)
    1 24 (5.2%) 22 (5.9%) 2 (2.3%)
ppi


    0 305 (67%) 250 (67%) 55 (63%)
    1 153 (33%) 121 (33%) 32 (37%)
ace_arb


    0 342 (75%) 281 (76%) 61 (70%)
    1 116 (25%) 90 (24%) 26 (30%)
diu


    0 358 (78%) 292 (79%) 66 (76%)
    1 100 (22%) 79 (21%) 21 (24%)
steroids


    0 21 (4.6%) 11 (3.0%) 10 (11%)
    1 437 (95%) 360 (97%) 77 (89%)
statins


    0 308 (67%) 256 (69%) 52 (60%)
    1 150 (33%) 115 (31%) 35 (40%)
nsaids


    0 256 (56%) 201 (54%) 55 (63%)
    1 202 (44%) 170 (46%) 32 (37%)
smoking


    0 214 (47%) 184 (50%) 30 (34%)
    1 244 (53%) 187 (50%) 57 (66%)
thyroid


    0 385 (84%) 312 (84%) 73 (84%)
    1 73 (16%) 59 (16%) 14 (16%)
ECOG_Score


    0 195 (43%) 162 (44%) 33 (38%)
    1 228 (50%) 182 (49%) 46 (53%)
    2 30 (6.6%) 24 (6.5%) 6 (6.9%)
    3 5 (1.1%) 3 (0.8%) 2 (2.3%)
ecog_score_2


    >=1 263 (57%) 209 (56%) 54 (62%)
    1 195 (43%) 162 (44%) 33 (38%)
bmi 27.1 (5.8) 27.6 (5.8) 25.0 (5.2)
bmi_cat


    1 17 (3.7%) 10 (2.7%) 7 (8.0%)
    2 166 (36%) 125 (34%) 41 (47%)
    3 148 (32%) 121 (33%) 27 (31%)
    4 127 (28%) 115 (31%) 12 (14%)
pre_HGB_45days 12.5 (1.6) 12.5 (1.5) 12.5 (1.9)
    Unknown 1 0 1
pre_PLT_45days 289.0 (105.7) 291.2 (101.5) 279.2 (122.3)
    Unknown 1 0 1
pre_ALB_45days 4.1 (0.4) 4.1 (0.4) 4.0 (0.4)
    Unknown 1 0 1
baseline_cystatin_c 1.1 (0.4) 1.1 (0.4) 1.2 (0.4)
baseline_cre 0.9 (0.3) 0.8 (0.2) 1.0 (0.4)
cockcroft 89.2 (35.3) 92.0 (36.4) 77.4 (27.4)
baseline_cre_egfr 86.0 (18.4) 87.0 (18.4) 81.7 (18.1)
baseline_cys_c_egfr 71.4 (23.6) 73.7 (23.7) 61.7 (20.3)
cr_cys_7


    None 325 (71%) 266 (72%) 59 (68%)
    30% 133 (29%) 105 (28%) 28 (32%)
1 Mean (SD); n (%)