This PhD position is funded by the Dutch Research Council (NWO). The summary of the project is:
It is generally hard to establish the true cause of something. Are outcomes after a certain treatment better because the treatment is so good, or because only patients with a better prognosis received it? Luckily, we can learn a surprising amount when a cut-off is used. Suppose for example that patients aged 65 or younger receive one treatment while older patients receive another. If the 64 year olds have better outcomes on average than the 66 year olds, we may ascribe this difference to the treatment. The researchers will use mathematical techniques to make optimal use of such convenient cut-offs.
In this PhD project, you will develop new statistical methods in the area of causal inference and apply them on real medical data, in particular on hip replacement data.
Causation is notoriously difficult to establish, as is instilled in many researchers with the saying `correlation does not imply causation!'. It is usually considered the prerogative of the randomized controlled trial to claim that a particular intervention causes better outcomes. However, due to ethical or practical considerations, there are many research questions and (patient) groups for which a trial cannot be carried out, leaving questions of the type 'Which treatment is the best choice for the patient?' unanswered.
Regression discontinuity designs
It is possible to draw causal conclusions outside of the setting of randomized controlled trials. An opportunity for causal inference presents itself when an intervention is assigned based on a cut-off, as is very common in medical decision-making.
Suppose for example that patients aged 65 or younger receive treatment A and patients older than 65 receive treatment B. On average, patients aged 64 will be similar to patients aged 66 in all potentially confounding aspects like BMI or smoking status. So if the outcomes for patients aged 64 are much better than those of patients aged 66, we may reasonably ascribe this difference to the intervention, and claim a causal effect. This is the core concept behind the regression discontinuity design.
In this PhD project, you will extend regression discontinuity methodology into several medically relevant directions and apply the new methods to medical data in collaboration with clinicians.
You will spend most of your time performing research. You are expected to publish your results in refereed journals, to develop publicly available software, and to finalize the research with a PhD thesis. In addition, you will present your results at local and international conferences, seminars and workshops, and you will contribute to the teaching activities of the department.
We are looking for a PhD student with the following experience and background:
Amsterdam UMC has an open culture. This means that we hope that everyone feels welcome in our organization and that we strive to offer equal opportunities to everyone. We therefore cordially invite all interested parties to respond to this vacancy.
The project is embedded in the Big Statistics group within the Department of Epidemiology and Data Science. You will work closely with Stéphanie van der Pas (website) and Mark van de Wiel (website) to develop the new methods. For the applications, you will work with orthopedic surgeons through the Dutch Arthroplasty Register (LROI).
Big Statistics is a lively research group that links Big Data to clinical response by novel, problem-specific statistical methods. We love data. You can read more about the group members and our projects on our website, and follow our activities on Twitter.
We would like to receive your application (motivation letter & CV) via the site accompanied by your grade list (Master's degree), (a draft of) your master thesis (if available) and one or two letters of recommendation (optional). Please upload these additional documents as one file with your CV.
During the publication period of this vacancy, an invitation for a first interview is already possible.
We would like to invite you to apply now if you are graduating this summer! The start date of this position is flexible, but can be no later than September 15 2022.
For more information about this position, you can contact dr. Stéphanie van der Pas via firstname.lastname@example.org
For more information about the application process please contact Tanja Hart, Recruitment adviser, via email@example.com or via 06-21603178.
A reference check, screening and hiring test may be part of the procedure. Read hier whether this also applies to you. If you join us, we ask for a VOG (Certificate of Good Conduct).