Susanne Strohmaier

Susanne Strohmaier works as associate professor at the Department of Epidemiology at the Medical University of Vienna. Her main research interests include development and application of methods for causal inference with time-to-event outcomes as well as meta-scientific topics.

Title of keynote presentation:

Causal inference in practice – One (?) estimand, many (!) analytical decisions

Abstract:

For many medical research questions, randomization is unethical or infeasible and decisions have to be informed by results based on observational – often routinely collected – data. Such data have enormous potential to inform stakeholders including health policy makers, health professionals and the general public about the impact of their decisions on public as well as individual health. However, impact evaluations depend not only on the quality of the underlying data source, but crucially also the choices made for the (statistical) analysis approach. These choices begin with translating the scientific objectives into well-defined target estimands. Even if a primary research question can be identified thus, conceptualizing a corresponding statistical analysis plan (SAP) targeting the estimand of choice necessitates numerous follow-up decisions ranging from data cleaning, missing data handling to the main analysis model, among many others. This can lead to many sensible paths of analysis where each path is scientifically justifiable, yet may yield different results. This variation in results is often perceived to reflect purely individual researchersʹ opinions or biases rather than an inherent feature of complex analyses and may have contributed to the evident skepticism toward science in the general public.
Current research within our group focuses on methods to quantify this variation of effects due to the multiplicity of analysis strategies. Inspired by existing research we suggest an approach that implies developing a preferred SAP, as well as, a meta-SAP comprising sensible alternative decision at each step of an SAP. Ultimately, we want to present the preferred analysis results in context of a distribution of plausible estimates to demonstrate how and to what extent analytical decision affect the resulting estimates.
Based on real world data applications involving causal questions in time-to-event settings of varying complexity, I will highlight the importance of evaluating the robustness of results to alternative design and analysis decisions and reflect on conceptional challenges as well as practical hurdles. After all, acknowledging and transparently communicating the influence of analytical decisions is essential to strengthening the credibility of evidence based on observational data.