SCOUT · Supporting Causal Conclusions from Observational Survival Studies
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-10-01 → 2020-09-30
- EU contribution
- €166,157
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Supporting Causal Conclusions from Observational Survival Studies
This Marie Skłodowska Curie Action (MSCA) titled ‘Supporting Causal Conclusions from Observational Survival Studies” (SCOUT) falls within the field of biostatistics. Broadly speaking, the challenges of a biostatistician either concern (1)prediction: quantifying how likely it is for an individual with certain characteristics (age, sex, blood lipids) to develop a certain disease (coronary heart disease) in, say, 10 years or (2) explanation: quantifying how much a certain intervention (e.g. lowering cholesterol levels with a defined statin prescription) would change the risk of developing the disease or (3) description: describing empirical associations in a dataset. As hinted in the title of this MSAC a major focus lay on explanation, also often denoted as ‘causal inference’. Particularly, we were not just interested in quantifying the effect of an intervention, but rather in decomposing the total effect into direct and indirect components (e.g. how much of the effect of the statin use is explained through a reduction in cholesterol and, are there other, perhaps more direct, effects of the intervention). In clinical studies such analyses can help to elucidate an underlying mechanism, to alter and further improve its components, or even to shift its target. Another key interest lay in the comparison of different models for time-to-event data. Such data occur in clinical or epidemiological research when one is interested in the time from an intervention to the occurrence of a particular event, but due to several constrains cannot observe the event in every individual. The most popular model to analyse such data is Cox’s proportional hazards model, mainly due its seemingly easy-to-report outcome measure. However, for causal inference an alternative model, Aalen’s additive hazards model, gained relevance, while the literature on its performance in prediction tasks is sparse. Therefore, we investigated gains and difficulties of these models in clinical case studies, archetypical for prediction and causal inference tasks. The work was carried out at the Section for Clinical Biometrics at the Medical University of Vienna (MUW) under the supervision of Assoc. Prof. Georg Heinze and led to a vibrant two-way transfer of knowledge between myself, my supervisor and the entire Prognosis Research Group.
Data: CORDIS, © European Union
Project objective
The focus of my proposal is on statistical mediation analysis in medical studies with patient survival as outcome. Mediation analysis can help to elucidate the underlying mechanisms and ultimately to alter and further improve components of an intervention or shift the target of an intervention. Specifically, in aim 1 we utilize penalized likelihood techniques in an innovative way to improve mediation analysis with sparse data, e.g. rare side effects of drugs. Aim 2 will focus on the Aalen additive hazards model, an alternative to the Cox proportional hazards model, in particular when causal inference and mediation is of primary interest. We will consider a series of case studies comparing Aalen additive hazards model with the Cox proportional hazards model regarding interpretability of effect estimates, ease of application, applicability of assumptions, ease of description in an interdisciplinary context and public perception. By means of analytical reasoning and empirical studies we fill important gaps in knowledge, while the interdisciplinary working environment and intersectoral seminars will ensure that the achieved advances reach the relevant stakeholders. During the secondment at the Novartis Statistical Methodology Group in Basel, I will get in touch with mediation analysis for interventions in clinical trials and its connections to the estimands framework, which is currently being considered a promising alternative to the intention-to-treat paradigm as first principle in evaluating effects of interventions. The suggested action will combine my supervisor's and my scientific expertise and international experience at its best and thus guarantees mutual growth. Interaction with a highly active research group, participation in the institutional programme for soft skill development and involvement in project administration will further contribute to my development towards independence and are important prerequisites for various career paths.
Original text from CORDIS.
Participants
- MEDIZINISCHE UNIVERSITAET WIEN · WienCoordinatorAustria
Links
Data: CORDIS, © European Union