Statistical interpretation of Medical Research Results

Name: Statistical interpretation of Medical Research Results
Rating: 3.9 (8 reviews)

Statistics, biostatistics, clinical trials, data analysis in medicine.

Created byBioStat Statistics

Last updated 10/2023

English

What you'll learn

Basic statistical measures used in medical research
How to interpret simple descriptive characteristics
How to interpret the results of statistical models: simple linear regression, linear regression with repeated measurements, logistic regression
What survival analysis is and how to interpret the results: Kaplan-Meier estimators and Cox proportional hazards model
What EBM indicators are and how to interpret them

Course content

1 section • 5 lectures • 1h 48m total length

Introduction24:25
The first lesson is introductory and focuses on basic descriptive characteristics commonly found in medical publications. Through several practical examples, you will see how the measures discussed in this lesson can be presented by researchers and what to pay special attention to during such presentations.
Evidence-based medicine17:52
The second lesson delves into evidence-based medicine. It is somewhat more practical than the other lessons because during it, participants have the opportunity to independently calculate all indicators and compare their results with the correct solutions.
Statistical Significance and Clinical Significance29:01
The third lesson covers theoretical aspects related to statistical and clinical significance. Topics related to confidence intervals and tests of proportion significance are also explored.
Statistical models14:45
The fourth lesson discusses statistical model topics, starting with simple linear models, moving on to mixed-effects models, and ending with logistic regression.
Survival analysis22:15
In the fifth lesson, we focus on survival analysis, including the Kaplan-Meier estimator, comparing survival curves, and Cox proportional hazards models.

Requirements

Familiarity with the use of a Windows-based computer program.

Description

This course aims to familiarize the learner with the interpretation of statistical measures and methods used in medical publications. It covers topics ranging from simple descriptive characteristics like mean and median to EBM indicators, statistical tests, especially tests of proportion significance, and statistical models (linear and logistic). Each lesson provides practical examples of using these measures and tools in published medical articles.

What will you learn?

Who this course is designed for:

This course is intended primarily for individuals who have contact with medical publications, especially:

• Professionals involved in clinical research

• Publishing doctors

• Doctoral students in medical fields

• Individuals planning a career related to the interpretation of medical research, such as employees of medical units affiliated with the Polish Academy of Sciences or other laboratories.

About us

The course is conducted by Aneta Piechaczek, PhD. Statistician and econometrician. In the years 2017-2021, an employee of the Laboratory of Applications of Mathematics in Economics AGH UST. PhD in the field of Management and Quality Science, developing in her dissertation the issues of more advanced statistical analyses in socio-economic sciences. In 2021, she was nominated for the Didactics Laurel of AGH university. Author and co-author of several scientific publications related to data analysis and statistics. Statistician of the BioStat Research and Development Center.

BioStat Research and Development Center is among the commercial scientific institutions with the status of a Research and Development Center (Centrum Badawczo-Rozwojowe, CBR) registered by the Ministry of Entrepreneurship and Technology.

Who this course is for:

Professionals involved in clinical research
Publishing doctors
Doctoral students in medical fields
Individuals planning a career related to the interpretation of medical research, such as employees of medical units affiliated with the Polish Academy of Sciences or other laboratories.