Add event ASCIE (acute silent cerebral ischemic event) in the descriptive report and stats report.
Provide descriptive stats for the person year from "date of informed consent" to "the date of their last well visit height, weight and blood pressure recorded"
Provide frequency and percentage of patients seen within one year and two year by site and overall (20 sites in total)
Wait for the demographic data to be cleaned by WashU and rerun the analysis then
Find out how many patients have RPMRI? and how many patients are tested positive or negative
Choose three predictors of most interest (e.g., age, gender, age times gender) and just test the linear relationship
Stratified the survival curves by blood pressure levels as Pegelow's paper
Update the analysis plan.
Background reading
Reading background paper
Export data from redcap and generate descriptive stats
Get familiar with the dataset
Statistical analysis
Work on the analysis plan
Reshape the dataset from wide to long (Note: also including spirometry data, but the visit date has a lot of missing and messy input)
One more issues on the well visit date --- they are ordered in decreasing date (from latest to oldest)
Check the date of spirometry data
Completed Items
Include the interaction of gender with all other predictors
Fit a marginal model on blood pressure with age, gender and their interaction.
Provide predicted means plot of blood pressure vs. age by gender
Provide correlation matrix for all the predictors used in the cox PH model
Provide a dataset containing predicted FEV and FEV%, actual FEV, with age, height, weight, bmi
Provide p values for table 2-5 in report 10
Use black equations for other group
Figure out the tick marks in the survival curves
Provide HR estimate for the comparison of two groups in the KM plot
Report the missing values in event date and outliers in height, age in the spirometry dataset.
Create FEV% and FVC variables in the spirometry dataset.
Perform log rank test for comparisons of positive MRI group versus negative MRI group.
Provide survival curves for the endpoint--with any type of event.
Provide boxplot with labeled outliers
Provide the total number of patient years
Provide number of patients in the model
Recode the missing in the stroke event as no and rerun the descriptive tables and KM plot.
Add a legend in the KM plot annotating the confidence intervals.
Check why there are mismatches in weight, height and bmi.
Check 211 missing records in history of silent new infarct
Provide number of records par patient
Look at the missing values in age in the demographic dataset
Categorize age into intervals and provide descriptive stats for each variable within the interval
Total patient years (overall, by those with and without events)
Boxplot
Frequencies for 3 types of events (silent infarct, CIA, over stroke)
Give Dionna first 25 and last 25 patients IDs in blood pressure, age, height, weight, hemoglobin.
Remove 9 patients with TIA/stroke from the analysis
Provide descriptive stats for all the variables in the model
Provide KM curves for the non-missing data
Determine the type of events, included in the table 1
Total patient years, by with events and without events