vjcitn/prostate-project

A single package synthesizing the work of Yates Coley

#rm(list=ls()) #setwd("")

prostate-project

A single package synthesizing the work of Yates Coley

#Complexities added include: tracking when patients have surgery

1. Install package(s)

install.packages("devtools") devtools::install_github("jbindman/prostate-project")

2. Load in data from csv files for tx.data, demo.data, psa.data, bx.data, each of which must be saved locally. Combine these files into a more useful set of dataframes by calling fillPatientTables on the four files. The function will return three filled dataframes (pt.data, psa.data, bx.full) to the workflow in list called patientDataframes.

#demographic data. one record per patient demo_data<-read.csv("julia-demo-data-IOP.csv") names(demo_data) #psa data. one record per PSA test per patient psa_data<-read.csv("julia-psa-data-IOP.csv") names(psa_data) #bx.data. one record per biopsy per patient bx_data<-read.csv("julia-bx-data-IOP.csv") names(bx_data) #surg.data. one record per treatment received per patient surg_data<-read.csv("julia-surg-data-IOP.csv") names(surg_data)

demo_data<-read.csv("julia-demo-data.csv") psa_data<-read.csv("julia-psa-data.csv") bx_data<-read.csv("julia-bx-data.csv") surg_data<-read.csv("julia-tx-data.csv")

3. Organize data frames from clinical patient sources

We want the patient data to include one record per patient and true GS

We may also put variables in this data frame to ease definition of variables in the PSA and BX data frame (date of dx, age dx, average prostate volume)

Finally, we will order patients based on their observed GS. This is done to make estimation in JAGS easier. We assign a new sequential unique patient identifier for this ordering ("subj")

ptDataframes <- ProstatePackage::fillPatientTables(demo_data, psa_data, bx_data, surg_data, IOP = TRUE)

inputPatient <- ProstatePackage:::printIndividualData(198, ptDataframes)

for (i in 1:200) { printIndividualData(i, ptDataframes) }

3. Prepare data and arguments for RJAGS by calling RJAGS on a required text file required for RJAGS use, as well as the formatted list patient.Dataframes.

Return RJAGS argument prep on formatted patient dataframes

jagsPrep <- RJAGSprep(ptDataframes, TRUE)

4. Execute RJAGS

#independently

5. Preform Distance function closestK(inputPatient, ptDataframes)