SMHS DataSimulation
Scientific Methods for Health Sciences - Data Simulation
Importing observed data for exploratory analytics
Using the SOCR Health Evaluation and Linkage to Primary (HELP) Care Dataset we can extract some sample data (00_Tiny_SOCR_HELP_Data_Simmulation.csv).
# data_1 <- read.csv('00_Tiny_SOCR_HELP_Data_Simmulation.csv',as.is=T, header=T)
# data_1 = read.csv(file.choose( ))
# data_1 <- read.table('00_Tiny_SOCR_HELP_Data_Simmulation.csv', header=TRUE, sep=",", row.names="ID")
attach(data_1)
# to ensure all variables are accessible within R, e.g., using age instead of data_1$\$$age
# i2 maximum number of drinks (standard units) consumed per day (in the past 30 days range 0–184) see also i1
# treat randomization group (0=usual care, 1=HELP clinic)
# pcs SF-36 Physical Component Score (range 14-75)
# mcs SF-36 Mental Component Score(range 7-62)
# cesd Center for Epidemiologic Studies Depression scale (range 0–60)
# indtot Inventory of Drug Use Con-sequences (InDUC) total score (range 4–45)
# pss_fr perceived social supports (friends, range 0–14) see also dayslink
# drugrisk Risk-Assessment Battery(RAB) drug risk score (range0–21)
# satreat any BSAS substance abuse treatment at baseline (0=no,1=yes)
==='"`UNIQ--h-2--QINU`"'Fragment of the data===
<center>
{| class="wikitable" style="text-align:center; " border="1"
|-
! ID ||i2 ||age ||treat ||homeless ||pcs ||mcs ||cesd ||indtot ||pss_fr ||drugrisk ||sexrisk ||satreat ||female ||substance ||racegrp
|-
| 1 ||0 ||25 ||0 ||0 ||49 ||7 ||46 ||37 ||0 ||1 ||6 ||0 ||0 ||cocaine ||black
|-
| 2 ||18 ||31 ||0 ||0 ||48 ||34 ||17 ||48 ||0 ||0 ||11 ||0 ||0 ||alcohol ||white
|-
| 3 ||39 ||36 ||0 ||0 ||76 ||9 ||33 ||41 ||12 ||19 ||4 ||0 ||0 ||heroin ||black
|-
| … || || || || || || || || || || || || || || ||
|-
| 100 ||81 ||22 ||0 ||0 ||37 ||17 ||19 ||30 ||3 ||0 ||10 ||0 ||0 ||alcohol ||other
|}
</center>
==='"`UNIQ--h-3--QINU`"'Testing section===
summary(data_1)
x.norm <- rnorm(n=200, m=10, sd=20)
hist(x.norm, main="N(10,20) Histogram")
hist(x.norm, main="N(10,20) Histogram")
mean(data_1$\$$age)
sd(data_1$\$$age)
Simulate New Data to Match the Properties/Characteristics of Observed Data
- i2 [0: 184]
- age m=34,sd=12
- treat {0,1}
- homeless {0,1}
- pcs 14-75
- mcs 7-62
- cesd 0–60
- indtot 4-45
- pss_fr 0-14
- drugrisk 0-21
- sexrisk
- satreat (0=no,1=yes)
- female (0=no,1=yes)
- racegrp (black, white, other)
# Demographics variables Sex <- ifelse(runif(NumSubj)<.5,0,1) Weight <- as.integer(rnorm(NumSubj, 80,10)) Age <- as.integer(rnorm(NumSubj, 62,10))
# Diagnosis:
Dx <- c(rep("PD", 100), rep("HC", 100), rep("SWEDD", 82))
# Genetics chr12_rs34637584_GT <- c(ifelse(runif(100)<.3,0,1), ifelse(runif(100)<.6,0,1), ifelse(runif(82)<.4,0,1)) # NumSubj Bernoulli trials chr17_rs11868035_GT <- c(ifelse(runif(100)<.7,0,1), ifelse(runif(100)<.4,0,1), ifelse(runif(82)<.5,0,1)) # NumSubj Bernoulli trials
# Clinical # rpois(NumSubj, 15) + rpois(NumSubj, 6) UPDRS_part_I <- c( ifelse(runif(100)<.7,0,1)+ifelse(runif(100)<.7,0,1), ifelse(runif(100)<.6,0,1)+ ifelse(runif(100)<.6,0,1), ifelse(runif(82)<.4,0,1)+ ifelse(runif(82)<.4,0,1) ) UPDRS_part_II <- c(sample.int(20, 100, replace=T), sample.int(14, 100, replace=T), sample.int(18, 82, replace=T) ) UPDRS_part_III <- c(sample.int(30, 100, replace=T), sample.int(20, 100, replace=T), sample.int(25, 82, replace=T) )
# Time: VisitTime – done automatically below in aggregator
# Data (putting all components together)
sim_PD_Data <- cbind(
rep(Cases, each= NumTime), # Cases
rep(L_caudate_ComputeArea, each= NumTime), # Imaging
rep(Sex, each= NumTime), # Demographics
rep(Weight, each= NumTime),
rep(Age, each= NumTime),
rep(Dx, each= NumTime), # Dx
rep(chr12_rs34637584_GT, each= NumTime), # Genetics
rep(chr17_rs11868035_GT, each= NumTime),
rep(UPDRS_part_I, each= NumTime), # Clinical
rep(UPDRS_part_II, each= NumTime),
rep(UPDRS_part_III, each= NumTime),
rep(c(0,6,12,18), NumSubj) # Time
)
# Assign the column names colnames(sim_PD_Data) <- c( "Cases", "L_caudate_ComputeArea", "Sex", "Weight", "Age", "Dx", "chr12_rs34637584_GT", "chr17_rs11868035_GT", "UPDRS_part_I", "UPDRS_part_II", "UPDRS_part_III", "Time" )
# some QC summary(sim_PD_Data) dim(sim_PD_Data) head(sim_PD_Data)
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- SOCR Home page: http://www.socr.umich.edu
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