Introduction to Clinical and Translational Research: Bias in Clinical/Translational Research; Analyzing Data and Interpreting the Results - Session 4

In the first section, lectures cover potential sources of bias that can occur at all stages of research, and how to mitigate their impact on findings. The second section focuses on analysis of data in biomedical research, its presentation, statistical testing and conclusions. These lectures prepare students for in-class sessions.

About this Resource

In "Bias in Clinical/Translational Research," a lecturer discusses the elements of bias that can impact both validity and value of clinical investigations. The lecturer reviews potential sources of bias that can occur even at the earliest stages of study design, in participant selection and throughout data collection, and in how data is interpreted or reported. Topics covered include information bias, measurement errors, selection bias, identification of confounding variables, and the quality assurance practices to help researchers validate data and adjust for error.

In "Analyzing Data and Interpreting the Results," a lecturer further discusses the process of data analysis in biomedical research, through the process of developing a research question, a hypothesis, and a study design.  The lecturer discusses best approaches to coding and presenting data, statistical summaries and analysis, and various types of charts and other data display methods. Hypothesis testing and synthesis of results are also covered.

Course Syllabus/Topics

Bias in Clinical/Translational Research

  • Understanding Bias
  • Internal Validity of Research Findings
  • Causes of Bias
  • Information and Selection Bias
  • Confounding Variables

Analyzing Data and Interpreting the Results

  • The Process of Data Analysis
  • Study Databases/REDCap
  • Summary Statistics
  • The Language of Probability Distribution
  • Common Hypotheses and their Tests
  • Synthesizing Results

Recommended background

Please complete the following prior to attending the short course, "Clinical Data for Translational Science Research; Bioinformatics and High-Density Data, Session 3."

  • Pre-test on Clinical Translational Research
  • View the two-part video resource
  • Participant Break-out Session Guide
  • Read the articles as mentioned in Session 1

Suggested Readings

  1. Liu R1, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, Sherlock G, Lewicki J, Shedden K, Clarke MF. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med. 2007 Jan 18;356(3):217-26.
  2. Slamon DJ1, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, Fleming T, Eiermann W, Wolter J, Pegram M, Baselga J, Norton L. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001 Mar 15;344(11):783-92.
  3. Pritchard KI, Shepherd LE, O'Malley FP, Andrulis IL, Tu D, Bramwell VH, Levine MN; National Cancer Institute of Canada Clinical Trials Group. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med. 2006 May 18;354(20):2103-11.
  4. Stark A1, Kucera G, Lu M, Claud S, Griggs J. Influence of health insurance status on inclusion of HER-2/neu testing in the diagnostic workup of breast cancer patients. Int J Qual Health Care. 2004 Dec;16(6):517-21.
  5. Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics. 1946 Jun;2(3): 47-53
  6. Sheidan E,Wright J, Small N, Corry PC, Oddie S, Whibley C, Petherick ES, Malik T, Pawson N, McKinney PA, Parslow RC. Risk factors for congenital anomaly in a multiethnic birth cohort: an analysis of the Born in Bradford study. Lancet. 2013 Oct;382(9901): 1350-1359.


Yes. Participants who complete the course receive a Certificate. Participants must watch the videos and attend the in-person session.

No. In addition to K Scholars, this course is open to faculty, clinicians, community health workers, fellows, post-docs, TL1 and F trainees, as well as medical, OT/PT, pharmacy and other students who intend to conduct clinical and translational research.


Jonathan Samet, MD, MS
Former Director, SC CTSI Workforce Development; Currently, Dean and Professor Colorado School of Public Health, University of Colorado, Denver

NIH Funding Acknowledgment: Important - All publications resulting from the utilization of SC CTSI resources are required to credit the SC CTSI grant by including the NIH funding acknowledgment and must comply with the NIH Public Access Policy.