Completed Items

• 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
• 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
  • Data exported from RedCap (07/31/2012)
  • Statistical Analysis in progress
  • 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.

• 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

• • 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

• • 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.

• • 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.

• 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

• 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
• Dr. DeBaun
  • Table 1 of demographics (see previous manuscript) with 7 columns
  • Give Dionna first 25 and last 25 patients IDs in blood pressure, age, height, weight, hemoglobin.
  • Frequencies for 3 types of events (silent infarct, CIA, over stroke)
  • Total patient years (overall, by those with and without events)
• Dr. Shoe maker
  • Provide graphs
  • Review manuscript
  • Bootstrap CI
• Dr. DeBaun
  • 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 R²
• 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
    • Have one figure for each outcome
    • Outcomes (y-axis): hospital charges, age, ISS, GCS, ICU, Hosp days,
  • Have another plot for year(x-axis) vs. total number of accidents(y-axis)
  • Formal test for trend over time
  • Perform test of counts vs. time
  • Edit report
• Abramo
  • Import data from Abramo study into one R data file
  • Create tapped variable
  • Remove observation at and after "tapped"
  • Describe left and right column data and plot histograms separately
  • Create new data set with just left and right means by patients'ID
• Barkin
  • Produce requested table - keep track of hours (12 hr)
  • Add "Obese group" to previous table (1hr)
• Hermant
  • Produce one table for Death vs. all predictors
  • Produce separate tables for predictors vs. three outcome variables
• Miranda Rainese
  • Describe data set
  • Summarize outcomes by predictor
  • Perform ordinal logistic regression on outcomes vs. the predictor
  • Edit the report