Missing Values in Clinical Research [EP16]
5 full days, assignments due after course
Prof. G. Molenberghs, PhD, Dipl.-Stat. Nicole Erler,
Erasmus MC, Rotterdam NL
The Introduction to R course of NIHES* and Using R for Statistics in Medical Research (BST02), previously part of Courses for the Quantitative Researcher (SC17), Biostatistical Methods II: Classical Regression Models (EP03), and Repeated Measurments (CE08), or equivalent knowledge.
*If you have been registered for the EP16 course, you will be automatically enrolled in the Introduction to R course, a short online course.
Detailed information about this course:
Missing data frequently occur in clinical trials as well as observational studies. An important source for missing data are patients who leave the study prematurely, so-called dropouts. Alternatively, intermittent missing data might occur as well.
When patients are evaluated only once under treatment, then the presence of dropouts makes it hard to comply with the intention-to-treat (ITT) principle. However, when repeated measurements are taken then one can make use of the observed portion of the data to retrieve information on dropouts. Generally, commonly used methods to analyse incomplete data include complete-case (CC) analysis and, in longitudinal studies, an analysis using the last observation carried forward (LOCF). However, these methods rest on strong and unverifiable assumptions about the missing mechanism. Over the last decades, a number of analysis methods have been suggested, providing a valid estimate for, e.g., the treatment effect under less restrictive assumptions.
The assumptions regarding the dropout mechanism have been classified by Rubin and co-workers as: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).
In the first part of the course we will review various repeated measurements models and indicate under which missing data mechanism they will provide valid estimates of the treatment effect. Finally, since it is impossible to verify that the dropout mechanism is MAR we argue that, to evaluate the robustness of the conclusion, a sensitivity analysis thereby varying the assumption on the dropout mechanism should become a standard procedure when analyzing the results of a clinical trial.
The second part of the course focuses on multiple imputation (MI), specifically the fully conditional specification (FCS, MICE), which is often considered the gold standard to handle missing data. We will discuss in detail what MI(CE) does, which assumptions need to be met in order for it to perform well, and alternative imputation approaches for settings where MICE is not optimal. The theoretic considerations will be accompanied by demonstrations and short practical sessions in R, and a workflow for doing MI using the R package mice will be proposed.
Examination for this course consists of two assignments.
Gain insight in various repeated measurements models and under which missing data mechanism they will provide valid estimates of the treatment effect.
Learn how to perform (multiple) imputation for cross-sectional and longitudinal data in R.
Reduction on fees
PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)
50% reduction on our fees for participating institutes of NIHES
This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.
University Medical Center Rotterdam/Erasmus University Rotterdam:
- Department of Epidemiology
- Department of Public Health
- Department of Psychiatry, Section of Medical Psychology and Psychotherapy
- Department of Child and Adolescent Psychiatry/Psychology
- Department of Medical Informatics
- Department of General Practice
- Erasmus School of Health Policy & Management
- Generation R
50% reduction on our fees for NIHES affiliated partners
This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:
University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:
- General Paediatrics
- Plastic and Reconstructive Surgery
- Rehabilitation Medicine
UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.
25% reduction on our fees for partner institutes of NIHES
For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above).
25% reduction for all (international) PhD students
Please upload proof of enrollment as a PhD student in the application form and state the name of your tutor on the application form (remarks field).
25% reduction on our course fees (maximum 3 EC points per academic year) for all NIHES alumni
Please state that you are a NIHES alumnus/alumna and the academic year you graduated (remarks field). Please note that the reduction on fees for alumni does not apply to the Erasmus Summer Programme.