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| | ==[[SMHS| Scientific Methods for Health Sciences]] - Linear Modeling == | | ==[[SMHS| Scientific Methods for Health Sciences]] - Linear Modeling == |
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| − | ===Statistical Software- Pros/Cons Comparison===
| + | The following sub-sections represent a blend of model-based and model-free scientific inference, forecasting and validity. |
| | | | |
| − | ===Quality Control=== | + | ===[[SMHS_LinearModeling_StatsSoftware|Statistical Software]]=== |
| − | '''Questions:
| + | This section briefly describes the pros and cons of different statistical software platforms. |
| − | *''' Is the data what it’s supposed to (does it represent the study cohort/population)?
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| − | *''' How to inspect the quality of the data?
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| | | | |
| − | Data Quality Control (QC) and Quality Assurance (QA) represent important components of all modeling, analytics and visualization that precede all subsequent data processing steps. QC and QA may be performed manually or automatically. Statistical quality control involves quantitative methods for monitoring and controlling a process or data derived from observing a natural phenomenon. For example, is there evidence in the plots below of a change in the mean of these processes?
| + | ===[[SMHS_LinearModeling_QC|Quality Control]]=== |
| | + | Discussion of data Quality Control (QC) and Quality Assurance (QA) which represent important components of data-driven modeling, analytics and visualization. |
| | | | |
| − | # simulate data with base value of 100 w/ normally distributed error
| + | ===[[SMHS_LinearModeling_MLR |Multiple Linear Regression]]=== |
| − | # install.packages("qcc")
| + | Review and demonstration of computing and visualizing the regression-model coefficients (effect-sizes), (fixed-effect) linear model assumptions, examination of residual plots, and independence. |
| − | library(qcc)
| |
| − | demo.data.1 <- rep(100, 1000) + rnorm(1000, mean=0, sd=2)
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| − | qcc(demo.data.1, type="xbar.one", center=100, add.stats=FALSE,
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| − | title="Simulation 1", xlab="Index")
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| − | | |
| − | [[Image:SMHS_LinearModeling_Fig1.png|500px]] | |
| − | | |
| − | | |
| − | attach(data_1)
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| − | # to ensure all variables are accessible within R, e.g., using age instead of data_1$\$$age
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| − | # i2 maximum number of drinks (standard units) consumed per day (in the past 30 days range 0–184) see also i1
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| − | # treat randomization group (0=usual care, 1=HELP clinic)
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| − | # pcs SF-36 Physical Component Score (range 14-75)
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| − | # mcs SF-36 Mental Component Score(range 7-62)
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| − | # cesd Center for Epidemiologic Studies Depression scale (range 0–60)
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| − | # indtot Inventory of Drug Use Con-sequences (InDUC) total score (range 4–45)
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| − | # pss_fr perceived social supports (friends, range 0–14) see also dayslink
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| − | # drugrisk Risk-Assessment Battery(RAB) drug risk score (range0–21)
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| − | # satreat any BSAS substance abuse treatment at baseline (0=no,1=yes)
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| − | | |
| − | ===Fragment of the data===
| |
| − | <center>
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| − | {| class="wikitable" style="text-align:center; " border="1"
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| − | |-
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| − | ! ID ||i2 ||age ||treat ||homeless ||pcs ||mcs ||cesd ||indtot ||pss_fr ||drugrisk ||sexrisk ||satreat ||female ||substance ||racegrp
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| − | |-
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| − | | 1 ||0 ||25 ||0 ||0 ||49 ||7 ||46 ||37 ||0 ||1 ||6 ||0 ||0 ||cocaine ||black
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| − | |-
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| − | | 2 ||18 ||31 ||0 ||0 ||48 ||34 ||17 ||48 ||0 ||0 ||11 ||0 ||0 ||alcohol ||white
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| − | |-
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| − | | 3 ||39 ||36 ||0 ||0 ||76 ||9 ||33 ||41 ||12 ||19 ||4 ||0 ||0 ||heroin ||black
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| − | |-
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| − | | … || || || || || || || || || || || || || || ||
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| − | |-
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| − | | 100 ||81 ||22 ||0 ||0 ||37 ||17 ||19 ||30 ||3 ||0 ||10 ||0 ||0 ||alcohol ||other
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| − | |}
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| − | </center>
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| − | | |
| − | ===Testing section===
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| − | | |
| − | summary(data_1)
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| − |
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| − | x.norm <- rnorm(n=200, m=10, sd=20)
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| − | hist(x.norm, main="N(10,20) Histogram")
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| − | hist(x.norm, main="N(10,20) Histogram")
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| − | mean(data_1$\$$age)
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| − | sd(data_1$\$$age)
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| − | | |
| − | | |
| − | Simulate new data to match the properties/characteristics of observed data
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| − | | |
| − | * i2 [0: 184]
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| − | * age m=34,sd=12
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| − | * treat {0,1}
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| − | * homeless {0,1}
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| − | * pcs 14-75
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| − | * mcs 7-62
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| − | * cesd 0–60
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| − | * indtot 4-45
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| − | * pss_fr 0-14
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| − | * drugrisk 0-21
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| − | * sexrisk
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| − | * satreat (0=no,1=yes)
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| − | * female (0=no,1=yes)
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| − | * racegrp (black, white, other)
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| − |
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| − | # Demographics variables
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| − | Sex <- ifelse(runif(NumSubj)<.5,0,1)
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| − | Weight <- as.integer(rnorm(NumSubj, 80,10))
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| − | Age <- as.integer(rnorm(NumSubj, 62,10))
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| − | | |
| − | # Diagnosis:
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| − | Dx <- c(rep("PD", 100), rep("HC", 100), rep("SWEDD", 82))
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| − | | |
| − | # Genetics
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| − | 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
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| − | 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
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| − | | |
| − | # Clinical # rpois(NumSubj, 15) + rpois(NumSubj, 6)
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| − | UPDRS_part_I <- c( ifelse(runif(100)<.7,0,1)+ifelse(runif(100)<.7,0,1),
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| − | ifelse(runif(100)<.6,0,1)+ ifelse(runif(100)<.6,0,1),
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| − | ifelse(runif(82)<.4,0,1)+ ifelse(runif(82)<.4,0,1) )
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| − | UPDRS_part_II <- c(sample.int(20, 100, replace=T), sample.int(14, 100, replace=T),
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| − | sample.int(18, 82, replace=T) )
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| − | UPDRS_part_III <- c(sample.int(30, 100, replace=T), sample.int(20, 100, replace=T),
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| − | sample.int(25, 82, replace=T) )
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| − | | |
| − | # Time: VisitTime – done automatically below in aggregator
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| − | | |
| − | # Data (putting all components together)
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| − | sim_PD_Data <- cbind(
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| − | rep(Cases, each= NumTime), # Cases
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| − | rep(L_caudate_ComputeArea, each= NumTime), # Imaging
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| − | rep(Sex, each= NumTime), # Demographics
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| − | rep(Weight, each= NumTime),
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| − | rep(Age, each= NumTime),
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| − | rep(Dx, each= NumTime), # Dx
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| − | rep(chr12_rs34637584_GT, each= NumTime), # Genetics
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| − | rep(chr17_rs11868035_GT, each= NumTime),
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| − | rep(UPDRS_part_I, each= NumTime), # Clinical
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| − | rep(UPDRS_part_II, each= NumTime),
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| − | rep(UPDRS_part_III, each= NumTime),
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| − | rep(c(0,6,12,18), NumSubj) # Time
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| − | )
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| − | | |
| − | # Assign the column names
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| − | colnames(sim_PD_Data) <- c(
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| − | "Cases",
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| − | "L_caudate_ComputeArea",
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| − | "Sex", "Weight", "Age",
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| − | "Dx", "chr12_rs34637584_GT", "chr17_rs11868035_GT",
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| − | "UPDRS_part_I", "UPDRS_part_II", "UPDRS_part_III",
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| − | "Time"
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| − | )
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| − | | |
| − | # some QC
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| − | summary(sim_PD_Data)
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| − | dim(sim_PD_Data)
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| − | head(sim_PD_Data)
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| − | | |
| − | | |
| − | .....
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| − | | |
| − | <center>[[Image:SMHS_DataSimulation_Fig1.png|500px]] </center>
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| − | | |
| − | | |
| − | ....
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| | | | |
| | + | ===[[SMHS_LinearModeling_LMM |Linear mixed effects analyses]]=== |
| | + | Scientific inference based on fixed and random effect models, assumptions, and mixed effects logistic regression. |
| | | | |
| | + | ===[[SMHS_LinearModeling_MachineLearning|Machine Learning Algorithms]]=== |
| | + | Data modeling, training , testing, forecasting, prediction, and simulation. |
| | | | |
| | <hr> | | <hr> |
| | * SOCR Home page: http://www.socr.umich.edu | | * SOCR Home page: http://www.socr.umich.edu |
| − |
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| | {{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_LinearModeling}} | | {{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_LinearModeling}} |
