CAUSALab at Unit of Epidemiology, IMM, Karolinska Institutet
The research group is devoted to understanding how to best utilize Swedish health data for causal inference. This includes developing, utilizing, and adapting cutting-edge frameworks and methodologies, with an interest in how they can be operationalized in the Swedish and Nordic context.
By combining sound methodology with register data, we can produce actionable causal inference with real world impact, ultimately providing clinicians, patients, and policy makers with evidence on the effectiveness and safety of treatments and interventions. We are currently working within the following broad areas.
Complementing randomized trials using observational registry data
Randomized trials are the best way to make sound causal inferences, but they cannot answer all questions. They are limited to selected populations that enroll in trials, have restricted lengths of follow up time, and are restricted in their ability to estimate treatment effects in subpopulations. Through the application and adaptation of methods for transportability and benchmarking, this body of work aims to understand how to best utilize observational data to complement randomized trials and ask additional questions that were not answered in the initial trial. We initially aim to extend results from two registry-based randomized trials that were embedded within the SWEDEHEART clinical registry: VALIDATE and TASTE, which estimated the comparative effect of two anticoagulant medications, bivalirudin and heparin, and the effect of thrombus aspiration, on the risk of death and myocardial infarction in individuals undergoing percutaneous coronary intervention following myocardial infarction.
Replicating randomized trial results using observational registry data
A fundamental problem of causal inference is the impossibility to determine when the observational data are sufficient to approximately emulate the target trial. Though several attempts have been made at comparing the results of randomized trials and observational studies, the results are hard to interpret because 1) the observational studies did not explicitly emulate the trials that were conducted, and 2) randomized and observational studies were conducted in different populations. We will use observational data from the SWEDEHEART clinical registry to emulate several registry-based randomized trials that were embedded within SWEDEHEART. This is a unique opportunity to conduct a systematic comparison of randomized and observational studies in the same population with an explicit emulation of the trials.
Estimating the effect of dietary interventions using observational data
Nutritional epidemiology uses observational data extensively as randomized experiments are expensive and often difficult to conduct. However, traditional methodological approaches in the field are often limited to the comparison of disease risk at different levels of a nutrients or at different dietary patterns, but cannot address well-defined, explicitly formulated causal questions that would have important public health relevance. Many of the “real-life” nutritional interventions involve substituting one nutritional factor with another while keeping overall energy intake unchanged. Furthermore, dietary interventions must sustain for a longer period to be effective, so factors that may influence adherence to these interventions should be considered during the analysis. The target trial framework provides a conceptual framework and causal analytical methods that allows us to address such research questions and emulate a hypothetical experiment using observational data and linked register data.
Anita Berglund, Assistant Professor of Epidemiology
Miguel Hernán, Principal Researcher, IMM, Karolinska Institutet and Kolokotrones Professor of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health
Anthony Matthews, Assistant Professor of Epidemiology and Causal Inference
Conor MacDonald, Postdoctoral Researcher
Jessica Young, Postdoctoral Researcher
Anna Humphreys, Doctoral Student
The Swedish Research Council (Vetenskapsrådet)
The Swedish Research Council for Health, Working Life and Welfare (FORTE)
The Strategic Research Area in Epidemiology and Biostatistics (SFOepi)
Research team leader: Anita Berglund - firstname.lastname@example.org