Reference no: EM132555466
The data consist of growth measurements for 11 girls and 16 boys at ages 8, 10, 12, and 14.
Part A
Based on the long form of this data set, considering the effect of gender, generate the following:
Individual response profile
Mean response profile
Boxplot response profile
Based on the wide form of this data set
Create the mean summary measure (MSM) of (y2 y3 y4)
Generate the boxplot for mean summary measure by gender
Perform an analysis of covariance
model MSM = y1 Gender
Repeat using y4 as a summary measure instead of MSM.
Fit a random intercept linear mixed effect model.
Produce a plot comparing the actual individual response profile to the predicted.
Repeat the above using a random slope and intercept model.
Give your conclusions about the above models.
Part B Data / Introduction
The purpose here is to understand the impact of different types of missing/dropout on the estimation results
The response is a continuous variable
Baseline measure at time 0
Repeated measures at time 1, 2, ..., 10
Two explanatory variables are associated with the response in the simulation model
Binary variable X
Continuous variable Z
Based on the originally simulated data, we generate the data set with different types of missing/dropout observations
Report
What have you observed/discovered from the analysis of different data sets?
Discuss briefly the impact of different types of missing/dropout on the estimation results
Part C
Based on the wide data form, explore the correlation among the responses at different time points
Based on the long data form, considering the effect of X, explore the following
Pooled individual response profiles
Mean response profiles
Boxplot response profiles
Build a GEE based GLM for the response vs. the following explanatory variables: X, Z, time
With independent working correlation structure
With AR working correlation structure
Build a GLIMMIX for the response vs. the following explanatory variables: X, Z, time
Consider a random effect for the Intercept
Summarize your results and indicate if the results are similar, or if one model is more appropriate than the other
Part D
Based on the wide data form, explore the association among the responses at different time points
Based on the long data form, considering the effect of X, explore the following
Pooled individual response profiles
Mean response profiles
Boxplot response profiles
Build a GEE based GLM for the response vs. the following explanatory variables: X, Z, time
With independent working correlation structure
With AR working correlation structure
Build a GLIMMIX for the response vs. the following explanatory variables: X, Z, time
Consider a random effect for the Intercept
Summarize your results and indicate if the results are similar, or if one model is more appropriate than the other.
Attachment:- Homework.rar