Pre-hospital qSOFA as a predictor of sepsis and mortality.

Background: The quick sequential organ failure assessment score (qSOFA) has been proposed as a simple tool to identify patients with sepsis who are at risk for poor outcomes. Its utility in the pre-hospital setting has not been fully elucidated.

Methods: This is a retrospective observational study of adult patients arriving by ambulance in September 2016 to an academic emergency department in Fresno, California.

Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial.

Objectives: We reanalyzed data from a previous randomized crossover design that administered high or low doses of intravenous immunoglobulin (IgG) to 12 patients with hypogammaglobulinaemia over 12 time points, with crossover after time 6. The objective was to see if results corresponded when analyzed as a set of single-case experimental designs vs. as a usual randomized controlled trial (RCT).

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them.

Abstract: A Meta-Analysis of the Autocorrelation in Single Case Designs.

Single case design (SCD) experiments in the behavioral sciences utilize just one participant from whom data is collected over time. This design permits causal inferences to be made regarding various intervention effects, often in clinical or educational settings, and is especially valuable when between-participant designs are not feasible or when interest lies in the effects of an individualized treatment. Regression techniques are the most common quantitative practice for analyzing time series data and provide parameter estimates for both treatment and trend effects.