R Binomial
2023-02-09
Last updated: 2023-05-09
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Knit directory: multistate/
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life transitions
People are complicated. Look at this transition through lifestages for an individual based on diagnostic codes, lab data, and biomarkers. We look at this example overlaying biomarkers for Diabetes.
Multistate modeling
We can think of these life transitions as progressing through a variety of states:
Here we plot transition OR by PRS quantile from each state to CAD for example.
Simplification
Now, we plot just from one two and three risk factor condensed to CAD. We remove all transitions with NAR < 10.
Number at Risk
Raw number of transitions
Rates
Now we plot the raw rates
How about hypertensive:
How about multiple risk factors
How about multiple risk factors
Now we look at adding additional info
Now look at effect of meds and time in state
Absolute Risk (intercept)
| Version | Author | Date |
|---|---|---|
| 9fdb1b6 | Your Name | 2023-04-10 |
All PRS
aprsall = fitfunc(df_frame = dfp,nstates = nstates,ages = ages,mode = "binomial",covariates ="cad.prs+yearsinstate+statin_now+antihtn_now+f.31.0.0+dm2.prs+ldl.prs+htn.prs")
absrisk=function(age,start,stop,cad.prs,yearsinstate,statin,bpmed,sex,dmprs,ldlprs,htnprs){
agename=as.character(age)
mod=aprsall$model_list[[agename]][[stop]][[start]]
if (start=="Health") {
new.frame=as.matrix(data.frame(1,cad.prs,statin,bpmed,sex,dmprs,ldlprs,htnprs))
} else {
new.frame=as.matrix(data.frame(1,cad.prs,yearsinstate,statin,bpmed,sex,dmprs,ldlprs,htnprs))
}
## x%*%B to get log(p/1-p)
pover1minp=exp(new.frame%*%mod[,1])
return(pover1minp/(1+pover1minp))
}
## doesn't make sense
absrisk(age = 50,start = "Dm",stop="Cad",yearsinstate = 10,statin = 0,sex = 1,dmprs = 1,cad.prs = 3,ldlprs = 1,bpmed = 0,htnprs =1) [,1]
[1,] 0.01388373absrisk(age = 50,start = "Health",stop="Cad",yearsinstate = 10,statin = 0,sex = 1,dmprs = 1,cad.prs = 3,ldlprs = 1,bpmed = 0,htnprs =1) [,1]
[1,] 0.01185995absrisk(age = 50,start = "Ht",stop="Cad",yearsinstate = 10,statin = 0,sex = 1,dmprs = 1,cad.prs = 3,ldlprs = 1,bpmed = 0,htnprs =1) [,1]
[1,] 0.009333437absrisk(age = 60,start = "Ht",stop="Cad",yearsinstate = 10,statin = 0,sex = 1,dmprs = 1,cad.prs = 3,ldlprs = 1,bpmed = 0,htnprs =1) [,1]
[1,] 0.008989289absrisk(age = 40,start = "Ht",stop="Cad",yearsinstate = 10,statin = 0,sex = 1,dmprs = 1,cad.prs = 3,ldlprs = 1,bpmed = 0,htnprs =1) [,1]
[1,] 0.007015785
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] plotly_4.10.1 dplyr_1.1.2 RColorBrewer_1.1-3 gridExtra_2.3
[5] ggplot2_3.4.2 data.table_1.14.8
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 lattice_0.21-8 tidyr_1.3.0 rprojroot_2.0.3
[5] digest_0.6.31 utf8_1.2.3 R6_2.5.1 plyr_1.8.8
[9] evaluate_0.20 httr_1.4.5 highr_0.10 pillar_1.9.0
[13] rlang_1.1.1 lazyeval_0.2.2 rstudioapi_0.14 whisker_0.4.1
[17] jquerylib_0.1.4 Matrix_1.5-4 rmarkdown_2.21 labeling_0.4.2
[21] splines_4.2.1 stringr_1.5.0 htmlwidgets_1.6.2 munsell_0.5.0
[25] compiler_4.2.1 httpuv_1.6.9 xfun_0.39 pkgconfig_2.0.3
[29] mgcv_1.8-42 htmltools_0.5.5 tidyselect_1.2.0 tibble_3.2.1
[33] workflowr_1.7.0 fansi_1.0.4 viridisLite_0.4.1 withr_2.5.0
[37] later_1.3.0 nlme_3.1-162 jsonlite_1.8.4 gtable_0.3.3
[41] lifecycle_1.0.3 git2r_0.32.0 magrittr_2.0.3 scales_1.2.1
[45] cli_3.6.1 stringi_1.7.12 cachem_1.0.8 farver_2.1.1
[49] reshape2_1.4.4 fs_1.6.2 promises_1.2.0.1 bslib_0.4.2
[53] ellipsis_0.3.2 generics_0.1.3 vctrs_0.6.2 tools_4.2.1
[57] glue_1.6.2 purrr_1.0.1 crosstalk_1.2.0 fastmap_1.1.1
[61] yaml_2.3.7 colorspace_2.1-0 knitr_1.42 sass_0.4.5