Double Sampling for Informatively Missing Data in Electronic Health Record-Based Comparative Effectiveness Research.

Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These solutions, however, are often unsatisfying in that they are not guaranteed to yield actionable conclusions.

Multi-Source Conformal Inference Under Distribution Shift.

Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning predictions, make it challenging to achieve valid inferences in multi-source environments. In this paper, we consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources.

Association of Preoperative Copeptin Levels with Risk of All-Cause Mortality in a Prospective Cohort of Adult Cardiac Surgery Patients.

We aimed to investigate the association of preoperative copeptin, a new cardiovascular biomarker, with short- and long-term mortality in a cohort of adult patients undergoing cardiac surgery, including its potential as a prognostic marker for clinical outcome. Preoperative blood samples of the Bern Perioperative Biobank, a prospective cohort of adults undergoing cardiac surgery during 2019, were analyzed. The primary and secondary outcome measures were 30-day and 1-year all-cause mortality.

Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes.

Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To address this gap, we 1) draw connections to the causal inference literature on sequentially randomized experiments, 2) propose a non-parametric framework for analyzing "open-loop" (static regime) optogenetics behavioral experiments, 3) derive extensions of history-restricted marginal structural models for dynamic treatment regimes with positivity violations for "closed-loop" designs, and 4) propose a taxonomy of identifiable causal effects that encompass a far richer collection of scientific questions compared to standard methods.