Last updated: 2023-02-23

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Here we examine the curves by PRS level for CAD by PRS strata:

library(survival)
library(survminer)
Loading required package: ggplot2
Loading required package: ggpubr

Attaching package: 'survminer'
The following object is masked from 'package:survival':

    myeloma
library(ggplot2)
library(data.table)

df_final = data.table(readRDS("~/Dropbox/pheno_dir/output/merged_pheno_censor_final.rds"))

fit <- survfit(Surv(Cad_0_censor_age, ifelse(df_final$Cad_0_Any==2,1,0)) ~cad.prs.lev, data=df_final, id=identifier)

#Survival
ggsurvplot(fit,
           conf.int = TRUE,
           ggtheme = theme_classic(base_size =20))

Version Author Date
dbae012 Your Name 2023-02-15 
gs=ggsurvplot(fit,
           conf.int = TRUE,
           ggtheme = theme_classic(base_size =20),fun = "cumhaz")

Plot mean PRS by age:

library("dplyr")

Attaching package: 'dplyr'
The following objects are masked from 'package:data.table':

    between, first, last
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
df_final$age.cat[df_final$Cad_0_Any==2]=round(df_final$Cad_0_censor_age[df_final$Cad_0_Any==2]/5,0)
d=df_final[df_final$Cad_0_Any==2]%>%group_by(age=round(Cad_0_censor_age/5,0))%>%summarise(cad.mean=mean(cad.prs),sd.cad=sd(na.omit(cad.prs))/sqrt(length(cad.prs)),htn.mean=mean(htn.prs),sd.htn=sd(na.omit(htn.prs))/sqrt(length(cad.prs)),ldl.mean=mean(ldl.prs),sd.ldl=sd(na.omit(ldl.prs))/sqrt(length(cad.prs)),dm2.mean=mean(dm2.prs),sd.dm=sd(na.omit(dm2.prs))/sqrt(length(cad.prs)))
df=data.frame(d[5:18,])

names(df)=c("age","mean","sem","mean","sem","mean","sem","mean","sem")

m=rbind(rbind(rbind(df[,c(1,2,3)],df[,c(1,4,5)]),df[,c(1,6,7)]),df[,c(1,8,9)])

#m$variable=c(rep("CAD",61),rep("HTN",61),rep("LDL",61),rep("DM",61))

m$variable=c(rep("CAD",14),rep("HTN",14),rep("LDL",14),rep("DM",14))
m$age=rep(c(22,27,32,37,42,47,52,57,62,67,72,77,82,85),4)
ggplot(m, aes(color=variable, y=mean, x=age))+xlab("Age of Event") +
  ylab("Mean PRS")+
  geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=.2,size=0.5, position=position_dodge(1)) +
  geom_point(position = position_dodge(1)) +
  expand_limits(y=0) +
  scale_y_continuous(expand=expand_scale(mult=c(0,0.02))) +
  theme_classic()
Warning: `expand_scale()` is deprecated; use `expansion()` instead.

c=df_final[df_final$Cad_0_Any==2]%>%group_by(age=round(Cad_0_censor_age,0))%>%summarise(events=length(Cad_0_censor_age))

ggplot(c[20:80,], aes(x=age, y=events))+geom_bar(position='dodge', stat='identity')+xlab("Age of Event")+ylab("Number of Events")+theme_classic(base_size =20)


sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.0.10      data.table_1.14.2 survminer_0.4.9   ggpubr_0.4.0     
[5] ggplot2_3.3.6     survival_3.4-0   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9       lattice_0.20-45  tidyr_1.2.1      zoo_1.8-11      
 [5] assertthat_0.2.1 rprojroot_2.0.3  digest_0.6.29    utf8_1.2.2      
 [9] R6_2.5.1         backports_1.4.1  evaluate_0.17    highr_0.9       
[13] pillar_1.8.1     rlang_1.0.6      rstudioapi_0.14  whisker_0.4     
[17] car_3.1-0        jquerylib_0.1.4  Matrix_1.5-1     rmarkdown_2.17  
[21] labeling_0.4.2   splines_4.2.1    stringr_1.4.1    munsell_0.5.0   
[25] broom_1.0.1      compiler_4.2.1   httpuv_1.6.6     xfun_0.33       
[29] pkgconfig_2.0.3  htmltools_0.5.3  tidyselect_1.2.0 tibble_3.1.8    
[33] gridExtra_2.3    km.ci_0.5-6      workflowr_1.7.0  fansi_1.0.3     
[37] withr_2.5.0      later_1.3.0      grid_4.2.1       jsonlite_1.8.2  
[41] xtable_1.8-4     gtable_0.3.1     lifecycle_1.0.3  DBI_1.1.3       
[45] git2r_0.30.1     magrittr_2.0.3   KMsurv_0.1-5     scales_1.2.1    
[49] cli_3.4.1        stringi_1.7.8    cachem_1.0.6     carData_3.0-5   
[53] farver_2.1.1     ggsignif_0.6.4   fs_1.5.2         promises_1.2.0.1
[57] bslib_0.4.0      survMisc_0.5.6   generics_0.1.3   vctrs_0.4.2     
[61] tools_4.2.1      glue_1.6.2       purrr_0.3.5      abind_1.4-5     
[65] fastmap_1.1.0    yaml_2.3.5       colorspace_2.0-3 rstatix_0.7.0   
[69] knitr_1.40       sass_0.4.2