Loading in 2 Seconds...
Loading in 2 Seconds...
A Unique Summer Experience Without Traditional Course Work: Balancing an Internship and Additional Research. Janelle Noel, M.S. KUMC Biostatistics Ph.D. Graduate Student. Outline. Process of Obtaining an Internship The Internship Life as an Intern Daily Schedule Expectations
Janelle Noel, M.S.
KUMC Biostatistics Ph.D. Graduate Student
Step 1: Tell the necessary people
Step 2: Get a game plan!
Step 3: Organize your materials
Step 4: Continue to have open communication with your future boss/company until your start date
CEO: Mark Wietecha, M.S., M.B.A
Mission Statements: “We are committed to improving access to quality care, reducing costs and keeping the unique needs of children at the forefront of health care reform implementation.”
Title: Analyst Intern
Company Branch: Statistical Solutions
Jay Berry, M.D., M.P.H.
Matt Hall, Ph. D.
Troy Richardson, Ph. D.
Duration: 12 weeks
Day 1: Orientation
Week 1: Compliance, IT, Exploring datasets, and learning the ICD-9 coding system
Week 2: PI in-person visit
Week 3 :
Weeks 11/12: Documenting/Summarizing progress and verifying codes
programming, literature reviews, conference calls, weekly meetings, projects, learning their corporate culture, making caffeinated coffee, eating fruit & nuts, and introducing myself to 100+ people
Primary Aim:Adapt a publicly available, comprehensive diagnosis classification scheme developed by AHRQ to count the number of chronic conditions, name each chronic condition, and describe the combinations of chronic conditions endured for each CMCC.
Data:Healthcare Costs and Utilization Kid’s Inpatient Database 2009 (HCUP KID) and Medicaid data from Truven Health Analytics (2009-2012)
Exclusion Criteria:Normal newborns and only one chronic condition
Objective:Determine if a trend exists year to year regarding the percentage of discharges and length of stays in children’s hospitals (CH) using two different definitions
Data: HCUP KID years 2000-2012
Method:Cochran—Armitage Trend Test
Title: Prediction of Medical Expenditures (ME) in Children
Objective: To predict the expected medical expenditures and health care utilization (HCU) in medically complex children using CCC/CCI/CCS in future years.
Data: Medicaid data from Truven Health Analytics and
Exclusion Criteria: Records that contains missing values and patients 17 years old
Study Design/Method: Fit a two-stage regression model to predict ME and HCU in children.
Stage 1: Logistic Regression/Stage 2: Linear Regression
Two part genomics project
Part I: Determine differentially expressed (DE) genes found among the different DE analysis methods
-pre and post treatment
Part II: Assess
2) impact of ignoring the paired design among samples (Summer ‘14)
Figure 1: Number of Differentially Expressed Genes (Statistical Framework)
Table 1: Number of Common Differentially Expressed When Methods Overlap
Figure 2: Number of Differentially Expressed Genes (Method’s Statistical Theory)
Bayesian Methods N=2609
Frequentist Methods N=4543
*Excludes EBseq from Venn Diagram
What happens when you let two grown men decorate your office?
Every office needs at least one running joke…