Biostatistics for Researchers

Description

The statistical techniques covered are several t-tests, chi‐square tests, analysis of variance (ANOVA), (multiple) linear and logistic regression and survival analysis. The course ends with a group assignment in which a case study is analyzed using the newly acquired statistical techniques. Concrete examples and case studies are used to apply the theory to practical situations. 

The daily schedule of the course includes interactive lectures by highly experienced lecturers, alternated with computer practical sessions, from 9.30 to 17.00 hours. Examples from medical and biological research will be used in the exercises. If the schedule permits, participants are also welcome to ask the instructor for advice on how to incorporate the statistics covered in the course into their own data analyses. Datasets will be analyzed using the statistical software packages RStudio and/or SPSS. Participants should bring their own laptop computer with the necessary statistical software packages (R/RStudio and/or SPSS) installed (R/RStudio are freeware, for SPSS a campus license is available for most universities). No prior experience with the statistical software packages is required, though it is recommended. 

Please note that there are no graded activities included in this course. Therefore, we are not able to provide participants with a transcript of grades. You will obtain a certificate upon completion of this course.

Day-to-day Biostats for Researchers 2024.pdf

Target audience

Researchers seeking an introduction to biostatistics. 

We offer this course in small groups, to guarantee the attention needed for each participant and to create a learning environment that encourages the participants to be actively involved in the learning activities. Therefore, the maximum number of participants is 20, the minimum number is 12. At least one month before the start of the course, you will receive an invitation letter.

Aim of the course

At the end of the course, you:

• have knowledge of the role that statistics plays in academic research;
• have knowledge of basic statistical techniques that are used to analyse data, and know the conditions under which they are appropriate;
• have insight into which of these techniques is applicable in a particular situation;
• can apply these techniques by using statistical software (R and/or SPSS);
• are able to interpret the results from a statistical analysis;
• can report these results in the context of a research question.

Study load

To be able to follow this course successfully, you will take part in a full-time daily programme during two weeks.

Although active statistical knowledge is not a prerequisite, we assume some basic knowledge on statistics and mathematics acquired through, for example, courses in biostatistics in the bachelor programme or self-study.

The basic knowledge we assume are:

• the concepts of population and sample
• histogram, boxplot, frequency table, scatterplot
• mean, median
• variance, standard deviation, range, interquartile range, standard error of the mean
• probability, probability distributions (especially the normal distribution)

Tags

• descriptive statistics
• t-test
• analysis of variance
• ANOVA
• Linear regression
• Logistic regression
• Survival analysis
• statistics
• biostatistics
• R