The effect of nitrous oxide on intra-ocular pressure in healthy adults.

Many anaesthetic agents affect intra-ocular pressure, yet little is known about nitrous oxide and intra-ocular pressure. This study assessed the effect of nitrous oxide on intra-ocular pressure in 20 healthy adult volunteers. The intra-ocular pressure was measured at baseline, while breathing a 70:30 mix of nitrous oxide and oxygen for 12 min, and then while breathing room air for 15 min. A linear mixed effects model was used to assess change in intra-ocular pressure over time. There was no significant difference in intra-ocular pressure between baseline and during or after nitrous oxide inhalation. Several differences in intra-ocular pressure were noted between internal time-points: pressure increased by 2.4 mmHg between 3 and 6 min of breathing nitrous oxide (p=0.01); it increased by 1.4 mmHg between 3 and 9 min of breathing nitrous oxide (p=0.046); and it decreased by 2.2 mmHg between 6 min of breathing nitrous oxide and 15 min of breathing room air (p=0.035). This study indicates that nitrous oxide inhalation does not significantly change intra-ocular pressure from baseline values in a population of healthy adults.

Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series.

Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset.