Meg and Soslow: redo the analyses: 1) use percent change of BMI in repeated measure analysis adjusting for baseline 2) use BMI Z score for Cox model
Stacy Killen: Gerber grant. Check RedCap variables with Research question
Sarah Jaser: Analysis according to the hypothesis she sent over.
Arnold: Propensity matching for BiPAP project. Wait for complete the info for "attending"
On hold Items
Completed Items
Amelia Keaton - manuscript: adding 2011-2012 data
Lindsay Roofe: remove mechnism_given==3 patients and rerun the analysis, change to row percent, try with/wo "social worker involved" in the model
Shuplock: Dex manuscript review, change the independent test to paired test after matching
Report sent: Meg McKane & Jon Soslow: assessing association between BMI and outcome of interest
Fleming: manuscript review
Fleming - additional analysis with more data
Mary Romano - Comparing two time points of data on the outcome of interest. Lost Kelly's old code need to start over.
Shopluck: Dex manuscript with new data
Jonathan Soslow: Analysis of "Obesity leads to underestimation of Ventricular Volumes and abnormal myocardial strain in repaired tetralogy of fallot as measured by Crdiac MRI" . Wait for low BMI category and will add spline of age and age*BMI in the model
John Williams: manuscript revisions
Kannankeril and Shuplock:working on the CIs of the difference on the outcomes before and after matching
Agarwal: Manuscript revision
Cary Fu: grant re-submission. Working on analysis plan and sample size calculation for grant proposal.
Kannankeril and Shuplock: Data analysis of "The effect of Peri-operative Eexmedetomidine on Arrhythmias after Surgery for congenital heart disease". Report was sent to investigator
Jill Simmons: questions for manuscript
James Gay and Charles Phillips: Analysis was done and already met with Charles to go over the results.
Abstract for Don Arnold: Spearman correlation coefficient and 95% CI at 3 time points between PEP and other outcomes
Redo the analysis for Linsay Roofe using the updated data (PEM variable was re-evaluated)
Roofe, Arnold, report was sent to investigator. Abstract was submitted.
Michael O'connor and Don Arnold: Inter-rater Reliability of Two Acute Asthma Severity Scores. Statistical Analysis Plan was sent to investigator.
Quick calculation of Weighted Kappa for Johnathan Soslow
Manuscript review for Dr. Simmons's "Prevalence of 25-Hydroxyvitamin D Deficiency in a Pediatric Population with Type 1 Diabetes Mellitus Compared to a Control Population"
Manuscript review for Dr. Agarwal's "Risk factors and outcomes associated with excessive bleeding in pediatric cardiac surgery”
Geoffrey Fleming - Aim: Assess if there is any improvement in the self-reported score on 3 areas and test whether the change is associated with the factor of interest. Report was sent to investigator
Agarwal-analysis for the blood products with bleeding. Done, report was sent to investigator
Rosen - check manuscript addition. Done, report was sent to investigator
Don Arnold - Analysis was done, report was sent to investigator. Aim: correlate PEP with other measurement of ASTHMA severity
Jill Simmons: Analysis was done and has been sent to investigator.
John Williams: Completed by Ben, MX was not added to the access of Redcap.Manuscript resubmission, wants to compare titers and positive rates for MPV vs. RSV viruses in prospective cohort of children.
Geoffrey Fleming - write AP
Dr. Mary Romano & Dr. Carrie Lind - passed to Kelly, Dec 2012
SAP has been approved by PI's
Waiting for PI to send six months of data through Red Cap
Had to stop the analysis because I would need the post-prompt data which is not ready, I thought it was part of initial data loaded but is not.(08/07/2012)
PI's are including another variable on RedCap analysis will start soon.
Dr. Killen report is also done, Kelly and I kind of disagreed with the method used, I wanted to use Fisher Exact test and she wanted Chi-square test.
Dr. Thomsen
SAP has been sent to them waiting for approval from PI.
PI has approved and analysis should start any time soon with dummy data set from PI.
Analysis is in progress using dummy data set, as of 11/05/2012
Original data set has just been received and has been run.
Statistical analysis report is ready 11/05/2012.
Analysis report sent to Ben for review.
Dr. Donald Arnold
Meeting with Ben possibly sometime this week to discuss SAP (Meeting date to be scheduled).
SAP partially done, sent to Ben for comments.
Need to update comments from Ben on SAP
SAP is done sent to Ben for comments (07.23.12)
SAP has been modified again and sent to Ben for comments (07.25.2012)
SAP sent to PI, we don't have the data yet. (07/31/2012).
We met to discuss SAP and SAP has been updated and sent to PI's.(09/07/2012)
Waiting to hear from them
Received data today : Monday 10/15/2012.
PI has made significant changes to request , refer to PI's email 10/16/2012 and Ben's email: 10/21/2012
Analysis in progress
Updates have been done sent to Ben for review last week.
* Dr. Bremer wants us to do further analysis on two extra variables, data is in the dropbox folder.
Baseline values for this new data are all zeros so there is not need to adjust for baseline
This should be completely done by close of today : 11/12/12
* Dr Abramo wants us to reproduce the Statistical Analysis report on NIRS/DKA hypertonic Solution study with new data.
Data is ready in Dropbox folder (Old data 31/new data 30)
Note every base excess variable should be negative it appears on data as positive
Remember each patient record is a sheet and read in one at a time in turns.
Updated and sent to Ben for review.
Dr. Abramo want Mean, SD, for variables in study , would give him mean, min, max, median for variables but some of these variables have issues and would still need further cleaning. Would be ready today. 05/11/2012
Meeting with Ben/Rebecca/Dr. Abramo at 2pm wed(08/15/20/12)
SAP has been sent to Ben for review.(08/16/12)
SAP has been sent back to me for update
Updated SAP sent to Ben for approval (08/21/2012)
Has been sent to PIs, and its been approved
Data is received (08/27/2012)(Deadline in Dec 2012)
Statistical analysis in progress now...10/05/2012
Analysis passed on to Mario
*Dr. Yaa Kumah
Would need to schedule a meeting with her again to modify the analysis plan.
Meeting at Ben's office 07/20/2012 Friday 3pm with Yaa Kuma.
SAP is complete sent to Ben for comments, (07.23.12).
Yaa is out of office, auto response email says, until 07/30/2012. (She has to confirm the data for this project).
SAP has been sent to Yaa (07.25.2012)
Waiting for data
PI wants to meet with me tomorrow ( 08/01/2012: 3pm) to make minor changes to statistical analysis plan.
Meeting :PI said data has two different variables that measure cell phone use: Mobile phone use and diabetes Mobile phone use each with scale 1- 27.
Research question : 1.Wants to know what extra explanation does each predictor contribute to the variation in the response variable in each full Model: (a) Adherence = prob.solv + barriers + MobilePhoneUse, (b) A1C =Adherence+prob.solv + barriers +MobilePhoneUse, (c) A1C =Adherence+prob.solv + barriers+Dia.MobilePhoneUse, (d) Adherence=prob.solv + barriers +Dia.MobilePhoneUse.
2. Which of the independent variables has a greater effect in predicting the response variable, in the event that the independent variables have different units of scale?
Analysis Plan (Main Points) : 1. Fit a linear regression model ; A1C = prob.solv + barriers + MobilePhoneUse +Dia.MobilePhoneUse, and set contrast option in R to generate Type III Sums of Squares. eg. options(contrasts=c(unordered="contr.sum", ordered="contr.poly")), Anova(lm(Y ~ x1+x2+x3 , data=dat1, type=3)) or use SAS . Type III SS test the Null Hypothesis: for a chosen B = 0 , when all other predictors are in the model. (comments:Type III SS have received a lot of criticisms I said, but PI insisted she wants that) .
2. Standardized all variables ( Y and X) so they all have a std of 1, and fit a similar model as above, and compare the standardized coefficients for the biggest in value.
PI wants to better put the data in shape before sending it (data not ready).
SAP is rewritten after meeting, has been sent to Ben for approval.
Data still not ready.
Data received (08/16/12) Essentially columns I-M :(HbA1C | adherence | Barriers | problem solving |mobile phone use ).
In progress ..........over now
Analysis report sent to Ben (09/14/2012)
Dr. Amanda Berger
Meeting at 2:30pm at MCN 2220 to work on Abstract submission. 08/09/12
SAP and part of the analysis has been done to meet abstract deadline, to be continued. (08/13/12)
Meeting with Dr. Amanda Berger and Dr. Franguol 2pm (08/14/2012)
Meeting to be schedule before the end of August to continue with project above.
Meeting on Monday 2:30pm (08/20/12) . Ben, me and PI's Dr. Berger , Dr Frangoul.
SAP is done and sent to Ben for approval.(08/21/2012)
SAP is sent to PI and its been approved. ( 08/23/2012)
Statistical Analysis Report is Ready (08/27/2012).- Will send to Ben today.
Sent back to me updates done and sent to Ben (08/31/2012)
Statistical Analysis Report sent to PI (09/05/2012)
Dr. Drew Bremer
Completed SAP, sent to Ben for approval.
Need to update comments from Ben on SAP.
Would meet Ben on last method on SAP for clarification, time not set yet.
SAP has been modified again and sent to Ben for comments (07.25.2012)
Statistical Analysis is in progress
Statistical Analysis Report is done and ready ( 08/03/2012).
Update of Statistical Report in progress (now).
Updates done and sent to Ben for approval (08/23/2012)
Sent back to me for updates, updates are done left with executive summary with referencing .(08/31/2012)
Updates are completely done sent to Ben for approval. (09/04/2012).
Statistical Analysis Report sent to PI ( 09/05/2012).
Dr. Jeff Lomenick
Meeting to discuss the results of Analysis Report on Thursday 2pm, 07.26.12, at Ben's Office.
Need to update Statistical Analysis report with comments from last meeting.
Updating SA report : in progress
Updated reports are done and ready for both TSH and Free.T4 ( 08/07/2012).
Report has been updated and sent to PI, PI is OK with it.
Dr. Hemant Agarwal.
This project will be handled by Yan Hu
SAP is ready in Wenli's Bulk Files.
Deadline first or second week in August.
* Dr. Jeff Lomenick
Currently cleaning Data
Meeting with Ben before 11:30am on Wed June 27, 2012 to discuss SAP
Analysis is completed sent to Ben for comments.
Analysis id done with.
Old and Dead Meetings
* Dr. Frangoul and Dr. Amanda Berger : Meeting set for Friday 14th, 2:30pm at Ben's Office .
Dr. Abramo : Meeting is set for September 11th 4pm at Bens Office :Confirmed
Dr. Stacy Killen : Meeting Thursday : Sept 06. 4pm. at Ben's Office...status : over
Dr. Michael Glenn O'Connor: Meeting on Friday , Sept. 7th --12:45pm to 1:30 pm over
* Meeting on Tuesday Oct 2, at 3pm at Ben's office : Bill Herman, Ben, Evan Crossfield, Steve
Dr. Ettinger
Rerun the analysis report using 5 level acuity level
Dr. Yaa Kumah (Steve)
Research questions (grant)
To determine the sample size of exploring the association between A1C values and general phone use, social net work and diabetes related phone use
To doing the sample size justification of exploring the association between barrier questions, problem solving questions, and adherence questions and general phone use, social net work and diabetes related phone use
Research questions (Analysis 96 patients)
To explore the association between barrier questions, problem solving questions, and adherence questions and general phone use, social net work and diabetes related phone use
Issues: Outcomes are in 5 point likert scale and she'd like to look at 50 questions as outcome and 20 questions as predictors, not enough sample size and Type I error inflation.
Analysis plan
Using the summary scores for each questionnaire, fit a linear regression model on Adherence, as a function of barrier, problem solving questions, mobile use, A1C.
Using the summary scores for each questionnaire, fit a linear regression model on A1C, as a function of barrier, problem solving questions, mobile use, Adherence.
Using the summary scores for each questionnaire, fit a linear regression model on Adherence, as a function of barrier, problem solving questions, diabetic mobile use, A1C.
Using the summary scores for each questionnaire, fit a linear regression model on A1C, as a function of barrier, problem solving questions, diabetic mobile use, Adherence.
Dr. Yaa Kumah-Crystal (the same analysis but using the new data to adjust for more covariates)
Analysis plan
Fit a linear regression model on A1C values as a function of regiment status (Pre/Post), age, gender, status*age, status*gender with a sandwich estimator accounting for repeated measures [age being categorized into 3 interval]
Provide predicted plot of A1C vs. time for each age group
Provide predicted plot of A1C vs. time for each gender group
Perform wilcoxon signed rank test to weight and height before and after regimen chang, paired t test if normality assumption is satisfied.
Perform wilcoxon signed rank test to compare # of hospitalizations before and after regimen chang, paired t test if normality assumption is satisfied.
Dr. Agarwal
Review manuscript
Revisit the cpb data to determine whether cpb during time can predict cardiac or non-cardiac complications, LOI, LPICU, and Death, with adjustment for other confounders.
Prepare the analysis plan.
Katherine Kudyba
Provide descriptive stats for 15 pcr results
Use the new time window and rerun the report
Include the p values in Table 1
Dr. Jonathan Soslow
Fit a linear regression on average T1 time, with the predictor of group, adjusting for age
Assess the relationship of region and average t1 time (possibly variance) at each region of MRI image with the outcome--"LVEF", adjusting for age, medication, etc.
Write the analysis plan
Dr. Shoemaker
Provide the plot in R
Dr. Puzanovova
Provide two figures at baseline
Dr. Tom Abramo
Perform paired t test for the baseline and three post treatment time points(5,10,15)
Conduct the linear regression to determine the correlation between the changes pCO2, PH, and GCS and the delta changes in outcome
Fit the linear mixed effects model to explore the time trend in NIR readings.
Dr. Jessica Hebert Mouledoux
Create demographic table
Create fisher exact results table
Dr. Don Arnold
Bayesian model averaging method to select variables in the dataset
Figure out confidence intervals for the parameters
Include FEV1 just for sensitivity test
Dr. Revi and Dr. Phillips
Sample size justification for matched cased control study to explore the differences in number of repeats in estrogen receptor
Analysis plan to compare case control group. t test on 150 patients in each group
Dr. Mary Romano
Send an email to attend the clinic
Dr. Bondi
Add one more time point
Describe time
Set up an meeting to discuss * Dr. Ettinger
Provide publication quality plot
Dr. Bondi
Longitudinal study on diabetic patients with and without intervention
Provide descriptive stats
Provide histograms of A1C and number of observations
Create a table showing the number of observations
Fit linear regression model using main effects only, with cubic spline function and GEE sandwich estimator
Provide contrast table for the differences between two time points
Provide Predicted plot vs time
Fit linear regression model using main effects and one interaction term, with cubic spline function and GEE sandwich estimator
Provide contrast table for the differences between two time points
Provide Predicted plot vs time by three age level
Dr. Jim Gay
Comparison of sample of chart reviews (n=200) vs. all readmissions for manuscript of readmission preventability
Provide CIs for the comparisons between reviewed and un-reviewed patients on a list of predictors
Give separate document for descriptives of variables in analysis
Dr. Ettinger
Review the draft of the manuscript
Dr. Natasha Halasa (Grant proposal)
Read about sample size calculation for non-inferiority study
Review reponses from Natasha
Write power calculation part in the grant
Dr Halasa
Clean up the new dataset
Rerun the analysis being restricted in the different time range
Dr. Arnold
Bootstrap CIs in Table 1
Dr. Arnold
Review research letter of cPRAM
Sample size calculation
Katherine Kudyba and Dr. Halasa
Send Katie figures file
Describe 4 patients with ROS but died
List x-ray reasons for abnormal patients
Dr. Jim Gay
Conduct comparisons between A vs. C+D and B vs. C+D
Dr. Natasha Halasa
Create a graph of raw counts of bacteria across month
Rerun the analysis for Lindsey Lawrence using the complete datset
Dr. Jim Gay (hospitalization dataset)
Provide histograms for 4 continuous outcomes
Fit ols() model for LOS, charges, relative weight and age regressed on group (A, B, and D)
Use sandwich estimator to adjust for repeated measures
Fit ordinal logistic regression model for CRG group regressed on group, with sandwich estimator to adjust for repeated measures.
Dr. Ashley Shoemaker
Rerun the analysis with the new data
Katherine Kudyba and Dr. Halasa
Add two variables in the comparison list
Group those patient with both ROS and other diagnosis into ROS=N
Use categorical version of variables
Dr. Puzanovova
Replace all log variables in Table 15
Remove all the w-variables in Table 15
Provide descriptive tables for baseline psychological variables, with CIs and P values
Provide correlation coefficients among all 12 variables
Dr. Creech
Manuscript review
Dr. Ettinger
Provide three graphs with publication quality
Dr. Puzanovova
Add rmssd as the outcome in the secondary report
Conduct comparisons between IBS and healthy patient groups in those outcomes of interest at baseline.
Explore the correlation between physiological factors( Heart rate) and psychological factors (severity ?)
Dr. Jim Gay
Perform Kendall's concordance W to assess the inter-rater reliability
Perform GEE Poisson model on the consensus ratings without covariates
Perform GEE Poisson model on the consensus ratings regressed on a set of risk factors
Dr. Shoemaker
Fitting linear regression model adjusted by robcov()
Provide predicted means plot
Dr. Ettinger
Fit a logistic regression model on the binary outcome--admission Y/N with individual acuity level and interaction term
See how the regression model fitting results look like to decide whether need to remove average Acuity from the current model
Create a descriptive for demographic variables
Dr. Don Arnold(PRAM)
Doing research on CI of R square (bootstrap)
Doing comparisons among three scores
Standardize the cPRAM and mPRAM score to 12 points scale
Provide two plots for PRAM vs. cPRAM or mPRAM separately, with R square (different colors) on the same panel
Dr. Ettinger
Calculate the average time to admission for patients with bed requests
Provide the graph of predicted rate versus across DOW
Katherine Kudyba (waiting on the list of variables of interest)
Summary table between two groups with or without positive ROS
Summary table between two groups with or without positive virus
Summary table between two groups by age groups (<28 days versus >28 days and < 6 months)
Dr. Agarwal
Review manuscript
Dr. Jim Gay
Write analysis plan
Doing research on how to assess agreement among ordinal ratings
Dr. Ettinger
Merge the admission and visit dataset
Cluster patients into half hour interval
Count # of bed requests, # of visits, take average of acuity level
Fit a negative or poisson model on the number of bed requests, offset by number of visits, adjusted by day, month and acuity level
Provide a summary table of estimated rates
Provide a contrast test on shift intervals vs. all other intervals
Provide a predicted plot of rate across intervals
Dr. Revi
Review manuscript
Dr. Revi Mathew
Rerun the analysis on subset data
Add one variable in the model
Dr. Arnorld
Double check normalization
Add spearman correlation test
Dr. Jim Gay
Make a graph in R
Dr. Don Arnold (cPRAM)
Remove duplicate records
Provide histograms of two outcomes
Verify whether standardized variables are correct
Fit linear restricted cubic spline model on tow outcomes on each predictor (6 models)
Provide ANOVA table, summary table and predicted plots
Provide Bland Altman plots to assess the agreement of three standardized measurements
Calculate ICC with CI
Fit linear restricted cubic models with baseline measurement of FEV and PRAM
Provide ANOVA table, summary table and predicted plots
Replicate the analysis on Rios
Dr.Warolin
Created a summary variable
Provide descriptive stats for overall and by x ray status
Provide histogram of outcomes, considering log transformation if needed
Perform negative binomial regression model on the number of episodes at each position as a function of reflux status
Perform t test on three continuous outcome. Be cautious about the normality of three outcomes.
Figure out the prior distribution for BMA method
Dr.Puzanovova (07/26/11)
Generate two pdf, one with coding flipped, and word count removed;
The other one with coding flipped, and word count removed, baseline interval included in the outcome and 5 contrast comparison for outcomes mean RRI, SD RRI, and HF
Provide 5 contrasts at each interval and across all intervals, respectively
Replicate it on original scale for log transformed outcomes
Dr Agarwal
Through "Bayesian model averaging" to select predictors
Fit proportional odds logistic model to compare group differences
Review the manuscript
Dr. Creech Buddy
Review manuscript
Dr Jim Gay
Review manuscript
Fill in the stats
Check the numbers
Improve figures
Dr.Puzanovova and Dr. Walker
Provide descriptive tables
Provide histograms
Provide a graph of raw means across time points
For baseline data, fit a linear regression model with restricted cubic spine function to assess the group effect
Provide Anova table, summary table, table of broken down effects, table of predicted values, dot chart of predicted value
For longitudinal data, fit a linear regression model with restricted cubic spine function, including 3-way and 2-way interactions.
Provide Anova table, summary table, table of broken down effects, table of predicted values, line graph of predicted value
Figure out how to get effects averaging out another variable in summary table
Replace summary table stats with these averaging estimates
Dr. Revi
Provide data
Do comparison
Ben Deschner
Review abstract
Dr Joycy Granger
Fit cox regression model
Review manuscript
Dr Mathew Revi
rerun the analysis with data with new metrix
Compare the outcome with old report
Dr. Ben Deschner
Provide spaghetti plot by group
Label the data points by group in predicted plot
Add interaction term into the model
Add VSD into the propensity score model
Estimate outcome change at three time points
Help Natalia Jimenez with her numbers (manuscript)
Dr. Locklair
Provide trajectory plot of asthma score over time
Perform wilcoxon test between initial measurement and last measurement
Dr. Natasha Halasa
Restrict the date in the defined range
Rerun the analysis with the new data
Dr. Locklair
Reshape the data set from wide to long
Transform outcomes in log scale
Provide histograms and spaghetti plot for three outcomes
Calculate ICC
Fit linear model with restricted cubic spline, adjusting for repeated measures
Provide table of estimates, ANOVA tables and predicted means plot
Dr. Ben Deschner
Provide descriptive stats
Calculate propensity score
Fit a linear regression model with sandwich estimator for longitudinal outcomes
Fit cox proportional hazards model for time to event outcome
Revi
redo analysis with new data (note new cutoffs for positive vs. negative test results) and send ID #'s
Fit cox model in the report
Sara Rippel, manuscript preparation (waiting on data), Clinical outcomes of former pediatric patients with histologic reflux esophagitis
Has about 10 outcomes, mostly dichotomous, a couple continuous (depression scales), wants to compare 3 groups (GER, Normal Endoscopy, Controls)
Provide 2*2 tables and histograms
For binary outcomes, use logistic regression
Perform logistic regression models for ordinal outcomes
Perform linear regression model with continuous outcomes
In Subgroup analysis, we will fit logistic regression to calculate propensity scores
Fit a second logistic regression model to incorporate both group and propensity scores
Dr. Yaa Kumah-Crystal (wait on the data)
Add a table of predicted values at -400,0,400
Add more predictors in the model
Provide predicted plots
Dr. Sara Horst
Perform logistic regression with restricted cubic spline on each continuous variable
Provide predicted probability plots for each continuous variable
Provide table of estimates and anova table
If depression is not significant, remove Abd symptom score from the model and rerun it. only table needed.
Power analysis
Calculate sample size for two outcomes using chi-square test for two proportions
Calculate effect size using t test
Dr. Bremer (monkey data)
Manuscript review
Dr.Revi Mathew (11/12/10)
Manuscript review
Patrick Drayna (fellow) and Dr. Don Arnold
Provide estimates and CIs for differences in predicted means between baseline and follow-up time points
Perform linear mixed effects model and provide table of estimates
Provide a predicted means plot for lme model
Add dose, age and gender to restricted cubic spline model
Calculate the standard deviation of differences between baseline and follow-up
Calculate the standard deviation within each time point
Calculate the correlation between baseline and follow-up
Dr. Bremer (monkey data)
Provides methods and summary section
Provide estimates and CI for each outcome
Provide predicted plots for each outcome, log and original scale
Dr. Arnold (Asthma patients)
Provide descriptive stats and histograms of outcomes
Test for correlation among predictors
Perform logistic and linear regression models on three outcomes (Delta or 2hr adjusted for baseline value)
Check whether sample size can support the model, if not, need to choose which one to use
Patrick Drayna (fellow), Don Arnold PAS abstract/manuscript requiring repeated measures.
Restructure data so that it's tall, not wide
Provide histograms of Isop by time to check normality assumptions
If normality satisfied, fit linear model with spline for time: y = time + dose, use Robcov for repeated measures
Dr. Bremer and Dr. Mathew
Hochberg correction for all the p value
Add two columns in the table, the new alpha level and test result
Take out all the variables with small sample size
Create a table for Spearman test to show the corrected p value and results
Dr. Bremer (grant: pilot data)
Provide histograms for original and log scale variables
Provide sample size calculation using paired t test between 12 months and baseline
Fit linear mixed effect model on each outcome for the pilot data
Provide estimates and CI for multiple comparison
Provide predicted plots for each outcome
Dr. Yaa Kumah-Crystal
Reshape the dataset from wide to long
Provide descriptive stats and histograms
Perform linear mixed effect model to compare pre vs. post A1C values
Perform linear model with spline on time variable
Provide linear graph based on the model
Dr. Sarah Horst
Perform logistic regression with restricted cubic spline
Provide predicted probability plot based on the model output
Dr. Matthew Locklair(12/2/10)
Combine individual csv file to one dataset
Seek for matches in two datasets
Provide descriptive stats and histograms of matched records
Perform correlation test for each pair
Transform three outcomes
Perform linear mixed model to test for association for athma data
Dr. Agarwal
Provide forest plot and boxplot for each table
Adjust scale of each plot
Perform proportional odds ratio test and logistic regression on morbidity after surgery, adjusted for bypass time and bleeding status
Dr. James Gay (manuscript) (11/10/10)
Review stats in manuscript
Dr. Buddy Creech (11/12/10)
Provide figures and tables for manuscript
Dr.Revi Mathew (11/12/10)
Provide descriptive tables and histograms
Compare case and control groups with Wilcoxon signed rank test
Perform spearman correlation test
Dr.Arnold (11/15/10)
Provide spaghetti plots and median plots for two outcome groups
Provide boxplots
Dr.Claudia Florez (11/12/10)
Provide boxplots for groups comparisons
Dr. Natasha Halasa (11/12/10)
Provide comparisons for the variables of interest between 6 groups
Dr. James Gay (manuscript)
Provide histograms and 2*2 tables with updated data
Perform Wilcoxon rank sum test for 0-7 days vs. 8-15 days and for for acute vs. chronic
Provide stats for two groups
Dr. Buddy Creech (10/18/10)
Provide a matrix of Spearman correlations
Perform GEE logistic regression model on children colonization
Evaluate effect of maternal colonization at birth, discharge, two months and 4 months through contrast matrix
Perform ANOVA table to give overall p value
Provide table of predicted probability for each combination of maternal colonization and time
Refit the model with the predictor Mcol at enrollment
Repeat both model with Vandy data only (be careful of the sample size
Dr. Mathew Revi
Provide descriptive stats for subgroups of patients
Provide contingency tables for two tests
Dr. Puzanovova
Provide means graph of four groups for each outcome
Dr. Shari Barkin and Dr. Rachel Biber (Manuscript revision)
Add a table of contents * Delete Sections 2.1 and 2.2
For sections 2.4 and 2.5, use BMI z score (variable \bmizscore") instead of BMI percentile.
Perform restricted cubic splines for the predictor instead of polynomials
Give summary tables along with p-values and R-squared
Provide corresponding graph for the continuous predictor of interest.
Dr.Puzanovova (second plan) (9/29/10)
Subset data with time intervals 1-4
Provide spaghetti plot for all patients
Calculate the difference between time interval 1 and 2, 2 and 3, 3 and 4
Fit linear model with baseline values and predictors
Provide table of parameters and anova output
Provide predicted means for differences for each combination
Perform interaction test on two way interaction term and refit the model if not significant
Provide another table of parameters if not significant
Subset data with time intervals 1-3
Perform interaction test on three way interaction term and refit the model if not significant
Provide table of parameters, ANOVA table and table of predicted means only for final model
Provide predicted means graphs for 4 groups vs. time intervals
Dr. Agarwal Hemant: bleeding study (9/17/10)
Provide descriptive stats
Perform logistic regression on bleeding outcome with predictors of interest
Provide predicted probability plot for continuous variables
Perform Wilcox Rank Sum test for the difference in the bleeding group vs. non-bleeding group on blood product transfusion outcomes
Provide descriptive stats for blood product use by bleeding status
Perform logistic regression on outcome bleeding with PICU variables
Provide predicted graph for delta time
Perform the Wilcox Rank Sum test and Fisher exact test to test for the differences of distribution in morbidity variables by bleeding status
Dr. Agarwal Hemant: CBP study (9/17/10)
Provide descriptive stats
Perform logistic regression on death with adjusting for severity of surgery, bypass time and their interaction
Provide predicted probability graph for bypass time
Provide descriptive table for bypass time by death
Perform proportional adds logistic regression model on ordinal outcomes
Dr. Agarwal Hemant: CBP study 3.5 years (9/17/10)
Provide descriptive stats
Perform proportional adds logistic regression model on ordinal outcome and test for the interaction term to decide the final model
For final model with interaction term, evaluate the effect of the continuous predictor at each level of rachs
Provide table of parameters for the final model
Perform logistic regression model on death
Provide table of parameters and predicted probability plot for bypass time
Dr. Mathew Revi (first plan 8/26/10)
Provide descriptive stats for overall data and subsets
Provide counts and proportions for two successive tests by resutls
Perform logistic regression on outcome "steroids treatment" to test the effect of initial GHST test and ACTH test 1cortisol value
Provide predicted probability graphs for these two continuous variables
Dichotomize the initial GHST test and ACTH test 1 result
Provide the 2*2 table and perform fisher exact test to test for the associate between binary predictors and outcome "steroids treatment"
Subset the dataset for whose deficiency status are available
Repeat the logistic regression on outcome deficiency status with initial GHST test and ACTH test 1 cortisol value
Repeat the Fisher exact test on outcome deficiency status
Provide predicted probability plots for initial GHST test and ACTH test 1 cortisol value
Dr.Abramo (pre tapped study) (9/10/10)
Updated the dataset with new files (including 4 new pts)
Rerun the pre analysis on the new dataset
Perform linear regression model on means and standard deviations with the same predictors
Dr.Abramo (pre and post tapped study) (9/10/10)
Updated the dataset with new files (including 4 new pts)
Rerun the pre and post analysis on the new dataset
Perform linear mixed effect model on left and right NIRS readings with tap status variables ( pre, during and post) and other predictors of interest
Split time period after getting tapped into 8 small intervals and refit the model with these interval dummy variables
Dr. Hemant (survey1)
Provide descriptive stats
Merge two dataset
Provide 2*2 tables for each survey questions for both pre and post surveys
Provide summary statistics and perform Wilcoxon test betweeen pre and post surveys
Repeat the tables and analysis for post and handoff surveys
Provide summary statistics for 4 Likert-scale questions
Perfrom Kruskal-Wallis test to compare 5 role groups
Dr.Abramo (post)
Provide 6*6 matrix plots for pre and post-tapped patients
Provide Bland Altman test for left and right measurement agreement
Linear mixed analysis for NIRS pre tap readings in comprehensive model
Dr.Puzanovova (second plan)
Provide ICC table and effective sample size for all the outcome variables on first 3 time points only
Fit a restricted cubic spline model for one outcome with interaction term and adjusted with robcov()
Provide ICC for time points 1 and 2, 2 and 3
Fit a linear mix model with the same predictors and outcome
Provide ICC table for all the outcome variables on first 3 time points only * Dr. Matthew Revi (08/26/10)
Provide descriptive stats
Label all the variables
Provide descriptive tables by variable and by tests
Clean data by switching rows and columns
Replace Metryrapone test variable in the descriptive tables
Provide descriptive stats for subgroup patients (151, 18, 13 and 34 separately)
Create a new outcome named "Deficiency"
Combine the two steroids treatment use (Maintenance and Stress)
Rerun the two models with two new outcome (continuous and dichotomized)
Provide predicted probability plots for each model
Dr. James Gay (hospitalization dataset) (8/13/10)
Lump together the APR-DRG MDC levels
Replace severity with MDCs in descriptive table and GEE model
Provide predicted probability tables to all categorical variables
Provide graphs to depict predicted probabilities
Dr. Abramo (8/11/10)
Rescale all the matrix plots
Bland Altman test for left and right measurement agreement
Provide comprehensive and individual plots
Dr. Dalabih
Provide a survival analysis graph
Convert the odds ratio in survival table
Provide analysis for time to mild status
Produce graphs for time to mild status * Dr. Abramo
Provide graphs of readings for each patient
Create a matrix of graphs of readings
Provide matrix graphs for 18 patients (128 pre-tap data)
Provide descriptive stats for standard deviations for 128 patients
Dr. Halasa Natasha
Provide 3 descriptive graphs * Dr.Abramo
Data manipulation to create tap marker
Create labels for during-tap
Making descriptive tables for pre and post tap data
Making histograms
ICC analysis
Study differences in distributions
Linear regression model analysis
Dr. James Gay (hospitalization dataset)
Create descriptive tables
Create outcome variables and fitting logistic regression GEE model
ANOVA for GEE
Predictive probability
Dr. Dalabih 7/19/10
Update all analyses with new data set
Adding race and gender to the model for infusion status
Dr.Granger(waiting for input) 7/15/10
Produce tables and figures for Etco2 manuscript
Dr. James Gay (patients dataset) 7/12/10
Provide descriptive statistics on overall data, and by group
Create summary table for age distributions
Create summary table for age distributions by 7 categories
Detect differences in age and gender distributions between Group C and other inpatients
Detect group difference in total number of hospital days and hospitalizations
Dr.Abramo
Check and update stats in the NIR paper
Check and update stats in ETco2 paper
Dr. Erin Alber
Review manuscript stats
Dr.Granger
Create table and figures for ATV manuscript
Look at the correlation of the new variable for Etoco2 project
Dr.Abramo
Clean the dataset and rerun the analysis
Create two types of time variables
Plot NIRS reading across time using both types of variables
Dr. Romano
Provide descriptive stats summary tables for questions with scores, single and multiple choices, respectively
Provide descriptive stats and histograms for survey results
Dr. Dalabih
Rerun the analysis with corrected data
Detect raw drug level effect
Dr.Puzanovova (first plan)
Describe variables of interest
Provide mean tables and plots for 13 primary outcome variables (one plot for each variable)
Provide mean tables and plots for each of 13 primary outcome variables under 4 subgroups (one plot for each variable)
Detect group effect and gender effect on primary outcomes using lme model
Produce interaction plots for variables of interest
Determine correlation among variables of interest with linear mixed model
Compare GEE packages in R
Run GEE model to test for association of 6 variables with outcomes
Dr.Abramo
Combine 130 csv files into one dataset
Rerun the analysis with new dataset
Dr. Dalabih
Describe time to mild status
Detect drug dose effect
Spearman correlation test on q2hs time and time to mild status
Granger
Survival analysis on association between outcomes and Etco2 values
Erin Albers
Rerun analysis without #83 and edit report
UNC.MP
Run descriptive stats
Dr. Dalabih
Run descriptive stats and provide summaries for variables of interest
Fit cox proportional hazards model with either binary or continuous predictor to assess drug influence
Erin Albers
Check normality assumptions
Run repeated measures ANOVA to determine condition effect
Pairewise comparison("Tukey" HSD)
Dr.Abramo
Combine 130 csv files into one dataset
Change variable labels
VCH readmission study
Run descriptive stats
Dr. Abramo
Remove one file from dataset
Burn a data cd
MPV project
Produce descriptive statistics tables
Use summary.formula to produce 2*2 table
Run Fisher exact test for subject with small expected cell count
Run univariate logistic regression on continuous variables
Barkin (12 hr)
Create format for gender, BMI category
Run regressions
Produce graphs
Produce demographic table (age, sex bmi percentile, WC, BIA)
Granger
Get ICC for Delta, forced and unforced data
Include FEV% to the descriptive table
Convert and format outcome variables
Descriptive Statistics on main variables in wide data set (delta's, Asthma score, %FEV, 10 outcome variables)
Make tall data set (1 row per observation)
Detect time effect in deltas
Detect baseline athma score effect
Detect FEV effect in deltas
Produce boxplots for non-forced,forced and deltas at various time points
Abramo
Update Abramo datasets with new csv file
Use Abramo data to get ICC using book formula and lme()
get CI using book formula
compare icc() and ICC.CI() function using both textbook data and Abramo data.
Based on the distribution of readings, calculate ICC and R˛
Mark Reiderer analysis
Describe scores for each item in questionnaire by residence group
Plot histograms of scores for each question by group
Describe three outcomes by year and group and perform Wilcoxon test
Plot histograms of outcomes by year and group
ATV (Granger)
Investigate predicted means using Mean() function of rms package
ATV (Granger)
Produce vertical boxplots by year for the outcomes of interest, with year (2000,2001,etc.) on the x-axis