creat any_2

creat any_3

add varaibles

Z Score and Creatinine/Cystatin C

ggplot(final_master, aes(x = z_score, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "Creatinine/Cystatin C",
    title = " Z Score and Creatinine/Cystatin C"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and Creatinine/Cystatin C for male

ggplot(final_master %>% filter(male==1), aes(x = z_score, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "Creatinine/Cystatin C",
    title = " Z Score and Creatinine/Cystatin C"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and Creatinine/Cystatin C for female

ggplot(final_master %>% filter(male==0), aes(x = z_score, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "Creatinine/Cystatin C",
    title = " Z Score and Creatinine/Cystatin C"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and Creatinine/Cystatin C for vertebral_level ==“L3”

ggplot(final_master %>% filter(vertebral_level =="L3"), aes(x = z_score, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "Creatinine/Cystatin C",
    title = " Z Score and Creatinine/Cystatin C"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

# Z Score and Creatinine/Cystatin C for vertebral_level ==“T10”

ggplot(final_master %>% filter(vertebral_level =="T10"), aes(x = z_score, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "Creatinine/Cystatin C",
    title = " Z Score and Creatinine/Cystatin C"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and egfr ratio

ggplot(final_master, aes(x = egfr_ratio_cys_cre, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "egfr ratio",
    title = " Z Score and egfr ratio"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and egfr ratio for male

ggplot(final_master %>% filter(male==1), aes(x = egfr_ratio_cys_cre, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "egfr ratio",
    title = " Z Score and egfr ratio"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and egfr ratio for female

ggplot(final_master %>% filter(male==0), aes(x = egfr_ratio_cys_cre, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "egfr ratio",
    title = " Z Score and egfr ratio"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

Z Score and egfr ratio for vertebral_level ==“L3”

ggplot(final_master %>% filter(vertebral_level =="L3"), aes(x = egfr_ratio_cys_cre, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "egfr ratio",
    title = " Z Score and egfr ratio"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

# Z Score and egfr ratio for vertebral_level ==“T10”

ggplot(final_master %>% filter(vertebral_level =="T10"), aes(x = egfr_ratio_cys_cre, y = cr_cys)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "blue", linewidth = 1) +
  labs(
    x = "Z Score",
    y = "egfr ratio",
    title = " Z Score and egfr ratio"
  ) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )
## `geom_smooth()` using formula = 'y ~ x'

final_master_sarcopenia cmp_time_any_3

final_master_sarcopenia$cmp_status_any_3 <- as.factor(final_master_sarcopenia$cmp_status_any_3)
tidycmprsk::crr(Surv(cmp_time_any_3,cmp_status_any_3) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 87


    None
— —
    30%
3.32 1.24, 8.88 0.017
age 87 1.01 0.95, 1.06 0.8
sex 87


    1
— —
    2
0.72 0.20, 2.50 0.6
cancer_stage 87


    1
— —
    2
5.38 0.52, 56.0 0.2
ecog_score_2 87


    >=1
— —
    1
0.50 0.13, 1.99 0.3
cockcroft 87 1.00 0.98, 1.02 >0.9
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_any_3

final_master_no_sarcopenia$cmp_status_any_3 <- as.factor(final_master_no_sarcopenia$cmp_status_any_3)
tidycmprsk::crr(Surv(cmp_time_any_3,cmp_status_any_3) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 371


    None
— —
    30%
2.34 1.33, 4.10 0.003
age 371 0.99 0.97, 1.02 0.6
sex 371


    1
— —
    2
0.84 0.49, 1.45 0.5
cancer_stage 371


    1
— —
    2
1.16 0.65, 2.06 0.6
ecog_score_2 371


    >=1
— —
    1
1.13 0.69, 1.84 0.6
cockcroft 371 1.00 0.99, 1.01 0.9
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_sarcopenia cmp_time_hosp

final_master_sarcopenia$cmp_status_hosp <- as.factor(final_master_sarcopenia$cmp_status_hosp)
tidycmprsk::crr(Surv(cmp_time_hosp,cmp_status_hosp) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 87


    None
— —
    30%
1.68 0.80, 3.53 0.2
age 87 0.99 0.95, 1.03 0.6
sex 87


    1
— —
    2
0.90 0.35, 2.33 0.8
cancer_stage 87


    1
— —
    2
4.00 1.11, 14.3 0.034
ecog_score_2 87


    >=1
— —
    1
1.21 0.51, 2.89 0.7
cockcroft 87 1.01 1.00, 1.02 0.13
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_hosp

final_master_no_sarcopenia$cmp_status_hosp <- as.factor(final_master_no_sarcopenia$cmp_status_hosp)
tidycmprsk::crr(Surv(cmp_time_hosp,cmp_status_hosp) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 371


    None
— —
    30%
1.46 0.94, 2.28 0.10
age 371 1.00 0.98, 1.02 >0.9
sex 371


    1
— —
    2
0.95 0.61, 1.48 0.8
cancer_stage 371


    1
— —
    2
1.65 1.03, 2.63 0.036
ecog_score_2 371


    >=1
— —
    1
0.97 0.66, 1.42 0.9
cockcroft 371 1.00 1.00, 1.01 0.2
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_sarcopenia cmp_time_side_effect

final_master_sarcopenia$cmp_status_side_effect <- as.factor(final_master_sarcopenia$cmp_status_side_effect)
tidycmprsk::crr(Surv(cmp_time_side_effect,cmp_status_side_effect) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 87


    None
— —
    30%
2.08 0.87, 4.96 0.10
age 87 1.00 0.96, 1.04 0.9
sex 87


    1
— —
    2
0.78 0.27, 2.26 0.7
cancer_stage 87


    1
— —
    2
2.78 0.77, 10.0 0.12
ecog_score_2 87


    >=1
— —
    1
1.02 0.37, 2.77 >0.9
cockcroft 87 1.00 0.99, 1.02 0.6
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_side_effect

final_master_no_sarcopenia$cmp_status_side_effect <- as.factor(final_master_no_sarcopenia$cmp_status_side_effect)
tidycmprsk::crr(Surv(cmp_time_side_effect,cmp_status_side_effect) ~ cr_cys_7 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 371


    None
— —
    30%
2.33 1.25, 4.34 0.008
age 371 0.99 0.96, 1.01 0.3
sex 371


    1
— —
    2
0.58 0.31, 1.08 0.086
cancer_stage 371


    1
— —
    2
1.16 0.63, 2.13 0.6
ecog_score_2 371


    >=1
— —
    1
0.78 0.46, 1.33 0.4
cockcroft 371 0.99 0.98, 1.00 0.3
1 HR = Hazard Ratio, CI = Confidence Interval

death coxph final_master_sarcopenia

table(final_master_sarcopenia$death_90,final_master_sarcopenia$cancer_stage)
##    
##      1  2
##   0 19 59
##   1  0  9
coxph(Surv(time_to_death_90,death_90) ~ cr_cys_7 + age + sex + ecog_score_2 + cockcroft, data = final_master_sarcopenia)%>% tbl_regression(exp = TRUE)%>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 87


    None
— —
    30%
3.90 0.90, 17.0 0.069
age 87 0.99 0.90, 1.09 0.8
sex 87


    1
— —
    2
0.37 0.04, 3.24 0.4
ecog_score_2 87


    >=1
— —
    1
0.35 0.04, 3.10 0.3
cockcroft 87 0.99 0.96, 1.02 0.4
1 HR = Hazard Ratio, CI = Confidence Interval

death coxph final_master_no_sarcopenia

coxph(Surv(time_to_death_90,death_90) ~ cr_cys_7 + age + sex + ecog_score_2 + cockcroft, data = final_master_no_sarcopenia)%>% tbl_regression(exp = TRUE)%>% add_n()
Characteristic N HR1 95% CI1 p-value
cr_cys_7 371


    None
— —
    30%
7.04 1.72, 28.8 0.007
age 371 1.01 0.95, 1.09 0.7
sex 371


    1
— —
    2
0.57 0.17, 1.90 0.4
ecog_score_2 371


    >=1
— —
    1
0.37 0.08, 1.75 0.2
cockcroft 371 1.01 1.00, 1.03 0.14
1 HR = Hazard Ratio, CI = Confidence Interval

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"
)

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"
)

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"
)

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"
)

#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"
)

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"
)

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"
)

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"
)

#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 (%)

final_master_sarcopenia cmp_time_any_3

final_master_sarcopenia$cmp_status_any_3 <- as.factor(final_master_sarcopenia$cmp_status_any_3)
tidycmprsk::crr(Surv(cmp_time_any_3,cmp_status_any_3) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 87


    <60
— —
    >=60
0.52 0.21, 1.25 0.14
age 87 1.01 0.95, 1.08 0.7
sex 87


    1
— —
    2
1.00 0.28, 3.53 >0.9
cancer_stage 87


    1
— —
    2
4.39 0.44, 43.5 0.2
ecog_score_2 87


    >=1
— —
    1
0.36 0.09, 1.42 0.14
cockcroft 87 1.01 0.99, 1.03 0.3
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_any_3

final_master_no_sarcopenia$cmp_status_any_3 <- as.factor(final_master_no_sarcopenia$cmp_status_any_3)
tidycmprsk::crr(Surv(cmp_time_any_3,cmp_status_any_3) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 371


    <60
— —
    >=60
0.40 0.21, 0.74 0.004
age 371 1.00 0.97, 1.02 0.7
sex 371


    1
— —
    2
1.13 0.68, 1.88 0.6
cancer_stage 371


    1
— —
    2
1.08 0.59, 1.97 0.8
ecog_score_2 371


    >=1
— —
    1
1.04 0.65, 1.68 0.9
cockcroft 371 1.01 1.00, 1.02 0.2
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_sarcopenia cmp_time_hosp

final_master_sarcopenia$cmp_status_hosp <- as.factor(final_master_sarcopenia$cmp_status_hosp)
tidycmprsk::crr(Surv(cmp_time_hosp,cmp_status_hosp) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 87


    <60
— —
    >=60
0.56 0.23, 1.35 0.2
age 87 0.99 0.95, 1.03 0.6
sex 87


    1
— —
    2
1.19 0.43, 3.28 0.7
cancer_stage 87


    1
— —
    2
3.65 1.04, 12.9 0.044
ecog_score_2 87


    >=1
— —
    1
1.06 0.48, 2.38 0.9
cockcroft 87 1.02 1.00, 1.03 0.034
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_hosp

final_master_no_sarcopenia$cmp_status_hosp <- as.factor(final_master_no_sarcopenia$cmp_status_hosp)
tidycmprsk::crr(Surv(cmp_time_hosp,cmp_status_hosp) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 371


    <60
— —
    >=60
0.96 0.58, 1.57 0.9
age 371 1.01 0.99, 1.02 0.6
sex 371


    1
— —
    2
1.07 0.72, 1.60 0.7
cancer_stage 371


    1
— —
    2
1.73 1.06, 2.80 0.027
ecog_score_2 371


    >=1
— —
    1
0.92 0.63, 1.34 0.7
cockcroft 371 1.01 1.00, 1.01 0.076
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_sarcopenia cmp_time_side_effect

final_master_sarcopenia$cmp_status_side_effect <- as.factor(final_master_sarcopenia$cmp_status_side_effect)
tidycmprsk::crr(Surv(cmp_time_side_effect,cmp_status_side_effect) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 87


    <60
— —
    >=60
0.42 0.15, 1.16 0.094
age 87 1.00 0.95, 1.05 >0.9
sex 87


    1
— —
    2
1.17 0.39, 3.56 0.8
cancer_stage 87


    1
— —
    2
2.51 0.72, 8.73 0.2
ecog_score_2 87


    >=1
— —
    1
0.88 0.36, 2.15 0.8
cockcroft 87 1.02 1.00, 1.03 0.10
1 HR = Hazard Ratio, CI = Confidence Interval

final_master_no_sarcopenia cmp_time_side_effect

final_master_no_sarcopenia$cmp_status_side_effect <- as.factor(final_master_no_sarcopenia$cmp_status_side_effect)
tidycmprsk::crr(Surv(cmp_time_side_effect,cmp_status_side_effect) ~ cys_c_egfr_ge_60 + age + sex + cancer_stage + ecog_score_2 + cockcroft,failcode=1,cencode=0, data = final_master_no_sarcopenia) %>% tbl_regression(exp = TRUE) %>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 371


    <60
— —
    >=60
0.61 0.32, 1.16 0.13
age 371 0.99 0.97, 1.02 0.5
sex 371


    1
— —
    2
0.78 0.45, 1.36 0.4
cancer_stage 371


    1
— —
    2
1.17 0.61, 2.22 0.6
ecog_score_2 371


    >=1
— —
    1
0.71 0.42, 1.21 0.2
cockcroft 371 1.00 0.99, 1.01 >0.9
1 HR = Hazard Ratio, CI = Confidence Interval

death coxph final_master_sarcopenia

table(final_master_sarcopenia$death_90,final_master_sarcopenia$cancer_stage)
##    
##      1  2
##   0 19 59
##   1  0  9
coxph(Surv(time_to_death_90,death_90) ~ cys_c_egfr_ge_60 + age + sex + ecog_score_2 + cockcroft, data = final_master_sarcopenia)%>% tbl_regression(exp = TRUE)%>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 87


    <60
— —
    >=60
0.66 0.14, 3.21 0.6
age 87 1.00 0.90, 1.10 >0.9
sex 87


    1
— —
    2
0.47 0.05, 4.26 0.5
ecog_score_2 87


    >=1
— —
    1
0.23 0.03, 1.88 0.2
cockcroft 87 1.0 0.96, 1.03 0.7
1 HR = Hazard Ratio, CI = Confidence Interval

death coxph final_master_no_sarcopenia

coxph(Surv(time_to_death_90,death_90) ~ cys_c_egfr_ge_60 + age + sex + ecog_score_2 + cockcroft, data = final_master_no_sarcopenia)%>% tbl_regression(exp = TRUE)%>% add_n()
Characteristic N HR1 95% CI1 p-value
cys_c_egfr_ge_60 371


    <60
— —
    >=60
0.09 0.02, 0.40 0.001
age 371 1.02 0.96, 1.09 0.6
sex 371


    1
— —
    2
0.96 0.31, 3.00 >0.9
ecog_score_2 371


    >=1
— —
    1
0.33 0.07, 1.53 0.2
cockcroft 371 1.03 1.01, 1.04 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval