Difference between revisions of "SMHS DataSimulation"

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(→‎Simulate New Data to Match the Properties/Characteristics of Observed Data)
 
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===Importing observed data for exploratory analytics===
 
===Importing observed data for exploratory analytics===
 +
 
Using the [[SOCR_Simulated_HELP_Data|SOCR Health Evaluation and Linkage to Primary (HELP) Care Dataset]] we can [https://umich.instructure.com/files/354289/download?download_frd=1 extract some sample data (00_Tiny_SOCR_HELP_Data_Simmulation.csv)].
 
Using the [[SOCR_Simulated_HELP_Data|SOCR Health Evaluation and Linkage to Primary (HELP) Care Dataset]] we can [https://umich.instructure.com/files/354289/download?download_frd=1 extract some sample data (00_Tiny_SOCR_HELP_Data_Simmulation.csv)].
  
−
  # data_1 <- read.csv('C:\\Users\\Dinov\\Desktop\\00_Tiny_SOCR_HELP_Data_Simmulation.csv',as.is=T, header=T)
+
  # 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.csv(file.choose( ))
−
  # data_1 <- read.table('C:\\Users\\Dinov\\Desktop\\00_Tiny_SOCR_HELP_Data_Simmulation.csv', header=TRUE,  sep=",", row.names="ID")
+
  # data_1 <- read.table('00_Tiny_SOCR_HELP_Data_Simmulation.csv', header=TRUE,  sep=",", row.names="ID")
−
+
 
 
  attach(data_1)   
 
  attach(data_1)   
−
  # to ensure all variables are accessible within R, e.g., using “age” instead of data_1$age
+
  # to ensure all variables are accessible within R, e.g., using age instead of data_1$\$$age
−
+
  # i2 maximum number of drinks (standard units) consumed per day (in the past 30 days range 0–184) see also i1
−
  # 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)
 
  # treat randomization group (0=usual care, 1=HELP clinic)
 
  # pcs SF-36 Physical Component Score (range 14-75)
 
  # pcs SF-36 Physical Component Score (range 14-75)
 
  # mcs SF-36 Mental Component Score(range 7-62)
 
  # mcs SF-36 Mental Component Score(range 7-62)
 
  # cesd Center for Epidemiologic Studies Depression scale (range 0–60)
 
  # cesd Center for Epidemiologic Studies Depression scale (range 0–60)
−
  # indtot Inventory of Drug Use Con-sequences (InDUC) total score (range 4–45)
+
  # 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
 
  # pss_fr perceived social supports (friends, range 0–14) see also dayslink
 
  # drugrisk Risk-Assessment Battery(RAB) drug risk score (range0–21)
 
  # drugrisk Risk-Assessment Battery(RAB) drug risk score (range0–21)
 
  # satreat any BSAS substance abuse treatment at baseline (0=no,1=yes)
 
  # satreat any BSAS substance abuse treatment at baseline (0=no,1=yes)
  
 +
===Fragment of the data===
  
 
<center>
 
<center>
Line 38: Line 39:
 
|}
 
|}
 
</center>
 
</center>
−
 
−
===Testing section===
 
  
 
===Testing section===
 
===Testing section===
Line 48: Line 47:
 
  hist(x.norm, main="N(10,20) Histogram")
 
  hist(x.norm, main="N(10,20) Histogram")
 
  hist(x.norm, main="N(10,20) Histogram")
 
  hist(x.norm, main="N(10,20) Histogram")
−
  mean(data_1$age)
+
  mean(data_1$\$$age)
−
  sd(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)
 +
 +
# Define number of subjects
 +
NumSubj <- 282
 +
NumTime <- 4
 +
 
 +
# Define data elements
 +
# Cases
 +
Cases <- c(2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 41, 42, 43, 44, 45, 53, 55, 58, 60, 62, 67, 69, 71, 72, 74, 79, 80, 85, 87, 90, 95, 97, 99, 100, 101, 106, 107, 109, 112, 120, 123, 125, 128, 129, 132, 134, 136, 139, 142, 147, 149, 153, 158, 160, 162, 163, 167, 172, 174, 178, 179, 180, 182, 192, 195, 201, 208, 211, 215, 217, 223, 227, 228, 233, 235, 236, 240, 245, 248, 250, 251, 254, 257, 259, 261, 264, 268, 269, 272, 273, 275, 279, 288, 289, 291, 296, 298, 303, 305, 309, 314, 318, 324, 325, 326, 328, 331, 332, 333, 334, 336, 338, 339, 341, 344, 346, 347, 350, 353, 354, 359, 361, 363, 364, 366, 367, 368, 369,370, 371, 372, 374, 375, 376, 377, 378, 381, 382, 384, 385, 386, 387, 389, 390, 393, 395, 398, 400, 410, 421, 423, 428, 433, 435, 443, 447, 449, 450, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 465, 466, 467, 470, 471, 472, 476, 477, 478, 479, 480, 481, 483, 484, 485, 486, 487, 488, 489, 492, 493, 494, 496, 498, 501, 504, 507, 510, 513, 515, 528, 530, 533, 537, 538, 542, 545, 546, 549, 555, 557, 559, 560, 566, 572, 573, 576, 582, 586, 590, 592, 597, 603, 604, 611, 619, 621, 623, 624, 625, 631, 633, 634, 635, 637, 640, 641, 643, 644, 645, 646, 647, 648, 649, 650, 652, 654, 656, 658, 660, 664, 665, 670, 673, 677, 678, 679, 680, 682, 683, 686, 687, 688, 689, 690, 692)
 +
 
 +
# Imaging Biomarkers
 +
L_caudate_ComputeArea <- rpois(NumSubj, 600)
 +
L_caudate_Volume <- rpois(NumSubj, 800)
 +
R_caudate_ComputeArea <- rpois(NumSubj, 893)
 +
R_caudate_Volume <- rpois(NumSubj, 1000)
 +
L_putamen_ComputeArea <- rpois(NumSubj, 900)
 +
L_putamen_Volume <- rpois(NumSubj, 1400)
 +
R_putamen_ComputeArea <- rpois(NumSubj, 1300)
 +
R_putamen_Volume <- rpois(NumSubj, 3000)
 +
L_hippocampus_ComputeArea <- rpois(NumSubj, 1300)
 +
L_hippocampus_Volume <- rpois(NumSubj, 3200)
 +
R_hippocampus_ComputeArea <- rpois(NumSubj, 1500)
 +
R_hippocampus_Volume <- rpois(NumSubj, 3800)
 +
cerebellum_ComputeArea <- rpois(NumSubj, 16700)
 +
cerebellum_Volume <- rpois(NumSubj, 14000)
 +
L_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
 +
L_lingual_gyrus_Volume <- rpois(NumSubj, 11000)
 +
R_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
 +
R_lingual_gyrus_Volume <- rpois(NumSubj, 12000)
 +
L_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3600)
 +
L_fusiform_gyrus_Volume <- rpois(NumSubj, 11000)
 +
R_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3300)
 +
R_fusiform_gyrus_Volume <- rpois(NumSubj, 10000)
 +
 
 +
# 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)
 +
 
 +
 
 +
.....
 +
 
 +
<center>[[Image:SMHS_DataSimulation_Fig1.png|500px]] </center>
 +
 
  
 +
....
  
  

Latest revision as of 15:50, 7 August 2016

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)
# Define number of subjects
NumSubj <- 282
NumTime <- 4

# Define data elements
# Cases
Cases <- c(2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 41, 42, 43, 44, 45, 53, 55, 58, 60, 62, 67, 69, 71, 72, 74, 79, 80, 85, 87, 90, 95, 97, 99, 100, 101, 106, 107, 109, 112, 120, 123, 125, 128, 129, 132, 134, 136, 139, 142, 147, 149, 153, 158, 160, 162, 163, 167, 172, 174, 178, 179, 180, 182, 192, 195, 201, 208, 211, 215, 217, 223, 227, 228, 233, 235, 236, 240, 245, 248, 250, 251, 254, 257, 259, 261, 264, 268, 269, 272, 273, 275, 279, 288, 289, 291, 296, 298, 303, 305, 309, 314, 318, 324, 325, 326, 328, 331, 332, 333, 334, 336, 338, 339, 341, 344, 346, 347, 350, 353, 354, 359, 361, 363, 364, 366, 367, 368, 369,370, 371, 372, 374, 375, 376, 377, 378, 381, 382, 384, 385, 386, 387, 389, 390, 393, 395, 398, 400, 410, 421, 423, 428, 433, 435, 443, 447, 449, 450, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 465, 466, 467, 470, 471, 472, 476, 477, 478, 479, 480, 481, 483, 484, 485, 486, 487, 488, 489, 492, 493, 494, 496, 498, 501, 504, 507, 510, 513, 515, 528, 530, 533, 537, 538, 542, 545, 546, 549, 555, 557, 559, 560, 566, 572, 573, 576, 582, 586, 590, 592, 597, 603, 604, 611, 619, 621, 623, 624, 625, 631, 633, 634, 635, 637, 640, 641, 643, 644, 645, 646, 647, 648, 649, 650, 652, 654, 656, 658, 660, 664, 665, 670, 673, 677, 678, 679, 680, 682, 683, 686, 687, 688, 689, 690, 692)

# Imaging Biomarkers
L_caudate_ComputeArea <- rpois(NumSubj, 600)
L_caudate_Volume <- rpois(NumSubj, 800)
R_caudate_ComputeArea <- rpois(NumSubj, 893)
R_caudate_Volume <- rpois(NumSubj, 1000)
L_putamen_ComputeArea <- rpois(NumSubj, 900)
L_putamen_Volume <- rpois(NumSubj, 1400)
R_putamen_ComputeArea <- rpois(NumSubj, 1300)
R_putamen_Volume <- rpois(NumSubj, 3000)
L_hippocampus_ComputeArea <- rpois(NumSubj, 1300)
L_hippocampus_Volume <- rpois(NumSubj, 3200)
R_hippocampus_ComputeArea <- rpois(NumSubj, 1500)
R_hippocampus_Volume <- rpois(NumSubj, 3800)
cerebellum_ComputeArea <- rpois(NumSubj, 16700)
cerebellum_Volume <- rpois(NumSubj, 14000)
L_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
L_lingual_gyrus_Volume <- rpois(NumSubj, 11000)
R_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
R_lingual_gyrus_Volume <- rpois(NumSubj, 12000)
L_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3600)
L_fusiform_gyrus_Volume <- rpois(NumSubj, 11000)
R_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3300)
R_fusiform_gyrus_Volume <- rpois(NumSubj, 10000)

# 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)


.....

SMHS DataSimulation Fig1.png


....


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