Biostatistics (2024/2025: period 1)

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Course aim

After completing this course you are able to:
  1. Understand, apply, and explain all methodological and statistical concepts used in class
  2. Explain the function and rationale of commonly used analysis techniques, including descriptive measures, t-tests, factorial Anova, Repeated measures Anova, Manova, measures for correlation, OLS regression, binary logistic regression, ROC curves, Chi-squared tests for goodness of fit and independence, life tables, and Cox proportional hazard regression.
  3. Explain, for a given research question, which choices regarding research design and analysis techniques may be made and why these are appropriate
  4. Conduct the procedures and analyses mentioned under 2 using SPSS in a series of computer lab sessions.
  5. For all procedures and analyses mentioned under 2, (a) interpret SPSS output and (b) report the findings following the APA format
  6. Explain selected methodological and statistical concepts by providing examples and short explanations in everyday language
  7. Reflect on the relevance of this biostatistics course and its components in the student’s curriculum

Relationship between assessment and learning goals:
In this course, the final course grade is based on seven elements: four written exams, lab reports, a personal electronic textbook, and a reflection document.
  1. Written in-class exam 1: this tests your knowledge of, and ability to apply the methodological and statistical terminology covered in the first few weeks of the course (course goal 1).
  2. Written in-class exam 2: this tests your knowledge of, and ability to interpret SPSS output for analysis techniques (course goals 1, 2, 3, and 5a, in particular descriptive measures, t-tests, factorial Anova, Repeated measures Anova, Manova)
  3. Written in-class exam 3: this tests your knowledge of, and ability to interpret SPSS output for analysis techniques (course goals 1, 2, 3, and 5a, in particular measures for correlation, OLS regression, binary logistic regression, ROC curves)
  4. Written in-class exam 4: this tests your knowledge of, and ability to interpret SPSS output for analysis techniques (course goals 1, 2, 3, and 5a, in particular Chi-squared tests for goodness of it and independence, life tables, and Cox proportional hazard regression)
  5. Lab reports. After each lab session, you complete a lab report following a prescribed format (course goals 4 and 5b)
  6. Personal Electronic Textbook (PET). In your PET, you can demonstrate your ability to explain methodological and statistical concepts using everyday language (course goal 6).
  7. Reflection document. After each lab session, you will write a short piece of text to reflect on the relevance of the techniques dealt with in your curriculum (course goal 7). Together, these constitute the reflection document.

Course content

Please note: The content of this course overlaps with UCACCMET22/23. Do not take both.

The content of the course is partly devoted to the understanding of the fundamentals of descriptive and inferential statistics (concepts, rationale of analyses and their assumptions), and partly to the application of techniques on data sets provided by the instructor. We will start with a definition of basic concepts relevant to all statistical tests, eg chance and odds, randomness, data levels, and probability distributions. Systematic errors and random errors will be discussed in relation to their impact on the reliability and validity of data. Concepts that will be explained in relation to statistical estimation and decision theory include the sampling distribution, standard error, test statistics, chosen (alpha) and observed (p-value) significance level, type I and type II error, the power of a test, confidence intervals, and effect size measures. We will cover a few research designs that are widely used in applied science research and relate these to different types of samples.

The actual tests and analysis methods include tests for group differences (t-tests, factorial Anova, Repeated measures Anova, Manova), and tests for relations between variables, such as Chi-square tests for goodness of fit and homogeneity / independence, OLS multiple linear regression models, binary logistic regression and ROC curves, life tables and Cox proportional hazards regression.

In the lab sessions, you will be given data sets that will have to be checked and summarized using appropriate descriptive statistical techniques. Data transformations will be applied where needed. Next to these descriptive statistics, you will test specific hypotheses on the given data sets and report on their findings in lab reports.

Format
The course is based on a mixture of lectures and guided computer lab sessions.

In the lectures, you are introduced to the fundamental concepts, assumptions, and rationale of statistical analyses. Each class session, you have to complete assigned entries in a Personal Electronic Textbook (PET). Each entry contains the definition of a concept, its source, and an illustrative example provided by you. The PET serves as a guide for homework and studies, as well as a quick reference guide for future use. At the end of the course, you submit your PET for evaluation.

In six computer lab sessions, you are familiarized with statistical analysis software (SPSS), and conduct analyses that were previously explained in theory. You may work individually or in pairs. Each lab session results in a lab report, which includes a description of the data, relevant analysis output (graphs and tables, assumptions checks), plus a description of results in APA format. You will also write a short reflective text after each lab session.

Knowledge of relevant concepts and theories and the ability to interpret output of analyses is tested with four in-class written exams.

Instructional formats

UCU course

Examination

Written in-class exam 1

Required | Weight 15% | ECTS 1.13

Written in-class exam 2

Required | Weight 20% | ECTS 1.5

Written in-class exam 3

Required | Weight 20% | ECTS 1.5

Written in-class exam 4

Required | Weight 20% | ECTS 1.5

Lab report

Required | Weight 10% | ECTS 0.75

Reflection document

Required | Weight 5% | ECTS 0.38

Personal Electronic Textbook (PET)

Required | Weight 10% | ECTS 0.75

*midterm FEEDBACK*

Not required

Entry requirements and preknowledge

Languages

  • English

Course Iterations

Related studies

Exams

There is no timetable available of the exams

Required Materials

  • READER
    Additional reading material will be provided by the instructor
  • BOEK
    Pezzullo, J.C. Biostatistics for Dummies. Wiley. ISBN-13: 978-1118553985 Available as paperback or E-book, either is fine.

Recommended Materials

No information available on the recommended literature

Remarks

Does not count as science course. Counts towards HUM methodology.

Coördinator

dr. G. de Krom G.deKrom@uu.nl

Lecturers

dr. G. de Krom G.deKrom@uu.nl

Enrolment

Attention: this course is not open to students from other faculties, so subsidiary students can't enroll for this course.

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