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Department of Medicine Research Curriculum for 2018-2019

A monthly didactic conference generally on the second Thursday of each month at noon (12 p.m.).

Location: Lecture Room A.  Hospital Basement
Intended Audience: Medical Residents and Fellows in the Department of Medicine.

Served with lunch provided by DOM.

The goal of this conference is to present topics that will help the participants to: 1) plan a clinical research project; 2) implement either observational or experimental clinical research; 3) analyze and interpret the data; and 4) present clinical research data.

1. September 13, 2018
Importance of scholarship, Introduction to research at Temple (Iris Lee)

2. October 11, 2018
Studies using Large Databases: How to use them well - Which to use, how to obtain, how to get the data (Meredith Briscoe)

3. November 8, 2018
Writing a Study Protocol: Aims & Hypothesis, Study Design, Statistical Analysis, Sample Size (Dan Rubin)

4. December 13, 2018
Using Epic for Research (Mark Weiner)

5. January 10, 2019
Using Redcap for Research Studies (Mark Weiner)

6. February 14, 2019
Epidemiological studiesSurvey design, Case Control studies, observational cross-sectional and cohort designs that would take advantage of EHR data (Resa Jones)

7. March 14, 2019
Ethical and IRB Issues in Biomedical Research

8. April 11, 2019
Writing an Abstract and Manuscript (Frank Friedenberg) 

9. May 9, 2019
Presenting Research Studies: Posters and Oral Presentations (Roberto Caricchio)

 

Resident Research Curriculum   

Introduction to Research at Temple, Dr. Iris Lee 

How to get started, Find a mentor and project, Resources for research       

Abstract/Case Report Writing, Dr. Iris Lee

Getting your Abstracts, Case Reports Submitted

Presenting Your Research, Dr. Iris Lee

Tips on Oral and Poster Presentations       

Presentation of Research Opportunities, DOM faculty

Resident Research Day

     

Bio-stats Lectures:  Dr. Peter Axelrod

 Lecture 1: Bias, confounding, and effect modification

Lecture 2: Cohort versus case-control studies; p values and confidence interval

Lecture 3: Logistic regression and survival analysis

Lecture 4: Meta-analysis