SMHS LinearModeling

Scientific Methods for Health Sciences - Linear Modeling

Statistical Software- Pros/Cons Comparison

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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