Figure 1 - Hazard Ratio Analysis of Fiber Intake
The data we are visualizing here has the following structure:
Mortality Hazard for PFS by daily average fiber intake, visualized via partial smoothing spline. Ticks along the x axis represent individual patients.
makeSpline(all_metadata_fiber, all_metadata_fiber$pfs_mo, all_metadata_fiber$pod_status,
all_metadata_fiber$aofib,
xlab = "Daily average fiber intake (g/day)", ylab="Hazard ratio (95% CI)",
title="Smoothing spline for progression-free survival",
knots=2,
centerpoint = 15) +
scale_x_log10()
Mortality Hazard for OS by daily average fiber intake, visualized via partial smoothing spline. Ticks along the x axis represent individual patients.
makeSpline(all_metadata_fiber, all_metadata_fiber$os_mo, all_metadata_fiber$os_status,
all_metadata_fiber$aofib,
xlab = "Daily average fiber intake (g/day)", ylab="Hazard ratio (95% CI)",
title="Smoothing spline for overall survival",
knots=2,
centerpoint = 15) +
scale_x_log10()
Kaplan-Meier PFS by daily average fiber intake, two groups >= 15 grams/day.
kmPFS<-survfit(Surv(pfs_mo, pod_status)~fiber_dichotomized,
type="kaplan-meier", data=all_metadata_fiber)
ggsurvplot(kmPFS,risk.table=TRUE,
xlab="Time (months)",
ylab="Progression-free survival",
pval = TRUE,
legend=c(0.8,0.9),
title="Patients with urothelial or renal cell cancer on ICB",
legend.title="Fiber intake",
legend.labs=c("<15 g/day","15+ g/day"),
#surv.median.line = "hv",
conf.int=TRUE,
break.x.by=6,
axes.offset=FALSE,
xlim=c(0,25),
data = all_metadata_fiber)
Kaplan-Meier OS by daily average fiber intake, two groups >= 15 grams/day.
kmOS<-survfit(Surv(os_mo, os_status)~fiber_dichotomized,
type="kaplan-meier", data=all_metadata_fiber)
ggsurvplot(kmOS,risk.table=TRUE,
xlab="Time (months)",
ylab="Overall survival",
pval = TRUE,
legend="none",
title="Patients with urothelial or renal cell cancer on ICB",
legend.title="Fiber intake",
legend.labs=c("<15 g/day","15+ g/day"),
#surv.median.line = "hv",
conf.int=TRUE,
break.x.by=6,
axes.offset=FALSE,
xlim=c(0,25),
data = all_metadata_fiber)
Univariate HRs for PFS among clinically relavant subgroups.
cox_pfs_all_uv <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
all_metadata_fiber)
summary(cox_pfs_all_uv)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = all_metadata_fiber)
n= 86, number of events= 55
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.8011 0.4488 0.2780 -2.882 0.00395 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.4488 2.228 0.2603 0.7739
Concordance= 0.606 (se = 0.033 )
Likelihood ratio test= 7.82 on 1 df, p=0.005
Wald test = 8.31 on 1 df, p=0.004
Score (logrank) test = 8.74 on 1 df, p=0.003cox_pfs_RC <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
mRCdataFiber)
summary(cox_pfs_RC)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = mRCdataFiber)
n= 48, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -1.0541 0.3485 0.3945 -2.672 0.00754 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.3485 2.869 0.1608 0.7551
Concordance= 0.641 (se = 0.046 )
Likelihood ratio test= 6.46 on 1 df, p=0.01
Wald test = 7.14 on 1 df, p=0.008
Score (logrank) test = 7.8 on 1 df, p=0.005cox_pfs_RC_IpiNivo <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
dataRCIpiNivoFiber)
summary(cox_pfs_RC_IpiNivo)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = dataRCIpiNivoFiber)
n= 15, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.7864 0.4555 0.7387 -1.065 0.287
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.4555 2.196 0.1071 1.937
Concordance= 0.627 (se = 0.094 )
Likelihood ratio test= 1.04 on 1 df, p=0.3
Wald test = 1.13 on 1 df, p=0.3
Score (logrank) test = 1.19 on 1 df, p=0.3cox_pfs_RC_TKI_IO <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
dataRCioTKIFiber)
summary(cox_pfs_RC_TKI_IO)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = dataRCioTKIFiber)
n= 24, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -1.1926 0.3034 0.6183 -1.929 0.0538 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.3034 3.295 0.09031 1.02
Concordance= 0.635 (se = 0.073 )
Likelihood ratio test= 3.16 on 1 df, p=0.08
Wald test = 3.72 on 1 df, p=0.05
Score (logrank) test = 4.17 on 1 df, p=0.04cox_pfs_UC <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
mUCdataFiber)
summary(cox_pfs_UC)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = mUCdataFiber)
n= 38, number of events= 27
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.3084 0.7346 0.3919 -0.787 0.431
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.7346 1.361 0.3408 1.584
Concordance= 0.53 (se = 0.052 )
Likelihood ratio test= 0.61 on 1 df, p=0.4
Wald test = 0.62 on 1 df, p=0.4
Score (logrank) test = 0.62 on 1 df, p=0.4cox_pfs_UC_NOTavelumab <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
dataUC_NOTavelumabFiber)
summary(cox_pfs_UC_NOTavelumab)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = dataUC_NOTavelumabFiber)
n= 24, number of events= 19
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.03016 0.97029 0.46269 -0.065 0.948
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.9703 1.031 0.3918 2.403
Concordance= 0.473 (se = 0.07 )
Likelihood ratio test= 0 on 1 df, p=0.9
Wald test = 0 on 1 df, p=0.9
Score (logrank) test = 0 on 1 df, p=0.9cox_pfs_UC_avelumab <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized,
dataUCavelumabFiber)
summary(cox_pfs_UC_avelumab)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized,
data = dataUCavelumabFiber)
n= 14, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.1060 0.8994 0.8239 -0.129 0.898
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.8994 1.112 0.1789 4.521
Concordance= 0.527 (se = 0.091 )
Likelihood ratio test= 0.02 on 1 df, p=0.9
Wald test = 0.02 on 1 df, p=0.9
Score (logrank) test = 0.02 on 1 df, p=0.9forestplot_data <- tibble::tibble(mean = c(0.4488,0.3485, 0.4555, 0.3034, 0.7346, 0.97029, 0.8994),
lower = c(0.26032,0.1608, 0.1071, 0.09031, 0.3408, 0.3918, 0.1789),
upper = c( 0.7739,0.7551, 1.937, 1.02, 1.584, 2.403, 4.521),
study = c("All","RCC", "RCC ipi-nivo", "RCC TKI-ICB",
"UC", "UC non-maintenance ICB", "UC maintenance ICB"),
n = c("86","48","15","24","38","24","14"),
events = c("55","28","8","12","27","19","8"),
HR = c("0.45","0.35", "0.46", "0.30", "0.73", "0.97", "0.90"))
forestplot_data |>
forestplot(labeltext = c(study, n, events, HR),
clip = c(0.09, 2.5),
xlog = TRUE,
xlab = "Hazard ratio for PFS (95% CI)",
xticks = c(0.1,0.25,0.5,1.0,2.5),
title = "Univariable progression-free survival subgroup analyis") |>
fp_set_style(box = "royalblue",
line = "darkblue",
summary = "royalblue") |>
fp_add_header(study = c("Subgroup"),
n = c("n"),
events = c("Events"),
HR = c("HR")) 
Multivariate HR for PFS adjusted for age, diagnosis, and performance status.
cox_pfs_all <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized + ageby10 +
ecog_1to5_final + cancer_RCCvsUC_NAME + stage.simplified.factor,
all_metadata_fiber)
summary(cox_pfs_all)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized +
ageby10 + ecog_1to5_final + cancer_RCCvsUC_NAME + stage.simplified.factor,
data = all_metadata_fiber)
n= 86, number of events= 55
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.7851 0.4561 0.2946 -2.665 0.0077 **
ageby10 0.3098 1.3631 0.1463 2.118 0.0342 *
ecog_1to5_final 0.3067 1.3589 0.3375 0.909 0.3635
cancer_RCCvsUC_NAMEUC 0.4615 1.5864 0.2986 1.546 0.1222
stage.simplified.factorStage IV 0.3596 1.4327 1.0407 0.346 0.7297
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.4561 2.1925 0.2560 0.8125
ageby10 1.3631 0.7336 1.0233 1.8157
ecog_1to5_final 1.3589 0.7359 0.7013 2.6332
cancer_RCCvsUC_NAMEUC 1.5864 0.6303 0.8836 2.8482
stage.simplified.factorStage IV 1.4327 0.6980 0.1864 11.0150
Concordance= 0.675 (se = 0.036 )
Likelihood ratio test= 19.37 on 5 df, p=0.002
Wald test = 19.17 on 5 df, p=0.002
Score (logrank) test = 20.29 on 5 df, p=0.001cox_pfs_RC <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized + ageby10 + ecog_1to5_final,
mRCdataFiber) #could not adjust for stage because all patients with RCC were stage IV
summary(cox_pfs_RC)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized +
ageby10 + ecog_1to5_final, data = mRCdataFiber)
n= 48, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -1.1441 0.3185 0.4488 -2.549 0.01079 *
ageby10 0.5361 1.7093 0.2011 2.666 0.00769 **
ecog_1to5_final 0.5315 1.7015 0.4766 1.115 0.26483
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.3185 3.1396 0.1322 0.7676
ageby10 1.7093 0.5850 1.1525 2.5352
ecog_1to5_final 1.7015 0.5877 0.6685 4.3306
Concordance= 0.727 (se = 0.048 )
Likelihood ratio test= 15.03 on 3 df, p=0.002
Wald test = 15.1 on 3 df, p=0.002
Score (logrank) test = 16.22 on 3 df, p=0.001cox_pfs_UC <-coxph(Surv(pfs_mo,pod_status)~ fiber_dichotomized+ ageby10 + ecog_1to5_final + stage.simplified.factor,
mUCdataFiber)
summary(cox_pfs_UC)Call:
coxph(formula = Surv(pfs_mo, pod_status) ~ fiber_dichotomized +
ageby10 + ecog_1to5_final + stage.simplified.factor, data = mUCdataFiber)
n= 38, number of events= 27
coef exp(coef) se(coef) z Pr(>|z|)
fiber_dichotomized -0.3016 0.7397 0.4143 -0.728 0.467
ageby10 0.1800 1.1973 0.1998 0.901 0.367
ecog_1to5_final -0.2445 0.7831 0.5621 -0.435 0.664
stage.simplified.factorStage IV 0.8216 2.2742 1.1013 0.746 0.456
exp(coef) exp(-coef) lower .95 upper .95
fiber_dichotomized 0.7397 1.3520 0.3284 1.666
ageby10 1.1973 0.8352 0.8094 1.771
ecog_1to5_final 0.7831 1.2770 0.2603 2.356
stage.simplified.factorStage IV 2.2742 0.4397 0.2627 19.689
Concordance= 0.551 (se = 0.07 )
Likelihood ratio test= 1.97 on 4 df, p=0.7
Wald test = 1.83 on 4 df, p=0.8
Score (logrank) test = 1.86 on 4 df, p=0.8forestplot_data <- tibble::tibble(mean = c(0.4561, 0.3185, 0.7397),
lower = c(0.2560, 0.1322, 0.3284),
upper = c(0.8125, 0.7676, 1.666),
study = c("All", "RCC", "UC"),
n = c("86","48","38"),
events = c("55","28","27"),
HR = c("0.46","0.32","0.74"))
forestplot_data |>
forestplot(labeltext = c(study, n, events, HR),
clip = c(0.09, 2.5),
xlog = TRUE,
xlab = "Hazard ratio for PFS (95% CI)",
xticks = c(0.1,0.25,0.5,1.0,2.5),
title = "Multivariable progression-free survival subgroup analyis") |>
fp_set_style(box = "royalblue",
line = "darkblue",
summary = "royalblue") |>
fp_add_header(study = c("Subgroup"),
n = c("n"),
events = c("Events"),
HR = c("HR"))