Statistical methods for two-sequence three-period cross-over designs with incomplete data

Clinical decision support tools useful for identifying sepsis risk

PURPOSE

Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms.

METHODS

This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity.

RESULTS

Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups.

CONCLUSION

While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

Analysis of cross-over studies with missing data

This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented.

Average bioequivalence evaluation: general methods for pilot trials

In clinical development of a bioequivalent (BE) drug product, a two-step strategy is commonly adopted. In the first step, a pilot BE trial is conducted to evaluate the acceptability of the test drug product as a candidate for further evaluation in a subsequent pivotal BE trial. In the second step, a full-scale pivotal BE trial is conducted to formally establish bioequivalence. The objective and criterion of a pilot BE trial are different from those of a pivotal BE trial. In practice, however, a pilot BE trial is often inappropriately designed and analyzed based on the criterion for a pivotal BE trial. One main reason is the lack of well-established design and analysis methods for a pilot BE trial. To close this gap in practice, this study proposes a Pilot Acceptance Range method specfically constructed for analyzing a pilot BE trial within the framework of a two-step strategy. For designing a crossover pilot BE trial, this paper derives the power function and provides an easy-to-use method for determining the sample size.

Bioequivalence trials with the incomplete 3 x 3 crossover design

In bioequivalence trials, one often considers two or more generic products with the original one. The 3 x 3 crossover design can be adopted to evaluate the two generic candidates with a brand name drug, rather than conducting two separate 2 x 2 crossover trials. Dropouts, however, are more likely to occur due to various administrative reasons when we consider a higher order crossover design. A modified method, which was originally given by Chow and Shao (1997), is extended to compare two generic products with a reference in the incomplete 3 x 3 crossover design. A simulation study and discussion are also presented.

Average bioequivalence for two-sequence two-period crossover design with incomplete data

A statistical procedure for analyzing a two-sequence two-period crossover design in bioequivalence trials is discussed when some observations at the second period are missing. The maximum likelihood estimators of the parameters in the average bioequivalence model are obtained under missing at random and a modified two one-sided test is proposed. The performances of the discussed test are compared using Monte Carlo simulations.