Intervention science in maternal and child health
Language of instruction
This course gives a comprehensive introduction to major methodological and conceptual approaches in the field of intervention science in maternal and child health.
Both established and emerging methods will be discussed and explored through lectures, group work and assignments. The course offers a unique opportunity for senior/postgraduate students to be acquainted with a diverse set of approaches to research design, analysis and interpretation with focus on Intervention science in maternal and child health.
The course lectures and presentations are given in English by national and international scientists. Course lecturers are selected based on their expertise in their fields.
Course theme Spring 2022
Previous topics and main topic for the next semester¿s course can be found at: https://www.uib.no/en/cismac
At the end of the week participants should be able to:
- Use the sufficient cause model, counterfactual susceptibility type model, and a causal graph to assist with the design or analysis of an epidemiologic study.
- Calculate adjusted measures of effect and select those that, when collapsible, correspond to the no-confounding condition. Use the adjusted measures of effect to estimate the direction and magnitude of confounding.
- Distinguish effect measure modification, interdependence, and statistical interaction from one another as separate - but related - concepts of interaction.
- Identify the likely magnitude and direction of bias due to misclassification of exposure, outcomes, confounders and modifiers.
- Weigh the advantages and disadvantages of significance testing.
- Compare the advantages and disadvantages of frequentist and Bayesian approaches to analysis of a single study, to evidence, and to changing your mind.
2 ECTS credits
Course registration and deadlines
PhD students at UiB will be given priority, but the course is open for students and researchers from other international academic institutions outside of UiB as well.
Registration will close when course is full.
Master's degree or equivalent education level is required, with the exception of students on the Medical Student Research Programme.
Master's degree or equivalent education level in medicine, biology, dentistry, statistics, mathematics, social and health sciences is required. Masters students that hold adequate competence in epidemiology will be considered as course participants.
Part of training component
Part of training component in the PhD program at the Faculty of Medicine. Recommended for candidates associated with CISMAC's graduate school.
All course activities are mandatory.
Form of assessment
All teaching activities are mandatory. No exam. Approved compulsory requirements are only valid for the semester they are approved.
Pass / Fail
Who may participate
PhD students. Recommended for candidates affiliated to the CISMAC Research School
Supplementary course information
Spring 2022:Advanced Epidemiology
Introductory and intermediate courses in epidemiological methods teach students the concepts needed to begin a career conducting valid epidemiological research; however these courses typically only briefly cover the causal models that should underlie the design of valid epidemiological studies. We will use these models as a jumping-off point to begin rethinking what we have already learned and to go further in our understanding of basic concepts of measures of effect, confounding, misclassification and selection bias. From there we will begin to question the implications of various sources of bias in our studies and we will work through novel methods and approaches for doing more than simply speculating about these biases. We will then finish by exploring the basic statistics used in epidemiological research and we will correct misunderstandings about what these statistics can tell us.
Throughout the course we will focus on the core concepts of validity (lack of systematic error) and statistical precision (lack of random error) and will will further develop our understanding of these central concepts. We will emphasize the development of skills that every doctoral level epidemiologist should have, skills that are both practical and marketable. Note that this course is not offered for any credit. It is a course designed to help doctoral level and advanced master¿s students advance their skills.
The reading list will be adapted and adjusted prior to each semester to reflect expected workload equivalent of 2 ECT.
For Spring 2022
There are no required readings for the course, but the following are recommended:
Ioannidis JP. Why most published research findings are false. PLoS Med 2005;2(8):e124.
Greenland S and Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol 1986;15:412¿418
Greenland S, Pearl J, and Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37¿48.
Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000;11(5):561-570.
Rothman KJ. Modern Epidemiology, 1st Edition. Chapter 15 - Interaction between Causes. Little, Brown, and Company, Boston, MA: 1986. pp 311¿326.
Jurek AM, Greenland S, Maldonado G et al. Proper interpretation of non-differential misclassification effects: expectations vs observations. Int J Epidemiol 2005;34(3):680-687.
Greenland S. Randomization, Statistics, and Causal Inference. Epidemiology 1990;1:421¿429.
Goodman S N. Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of Internal Medicine 1999;130(12): 995-1004. Paper available at http://www.acponline.org.
Poole C. Low P-Values or Narrow Confidence Intervals: Which Are More Durable? Epidemiology 2001;12(3): 291-294.
Type of assessment: Participated
- Withdrawal deadline