RESUMEN
PURPOSE: Although the validity of the Epworth Sleepiness Scale (ESS) as an effectiveness measure for sleep apnea treatments such as continuous positive airway pressure (CPAP) has been supported by multiple studies, some researchers continue to challenge it. They suggest that in addition to its impact on relieving patients' daytime sleepiness, CPAP also alters the internal standards patients use to evaluate their sleepiness (i.e., response shift; RS), confounding the meaning of the difference in the ESS scores. We believe an issue yet to be addressed in this debate is that all existing evidence of RS has been obtained through the then-test approach, a retrospective method sensitive to various cognitive mechanisms. Thus, in the current study, we re-examined this issue using the structural equation modeling (SEM) approach, a method that can be directly applied to randomized clinical trial (RCT) data without retrospective measures. METHODS: With the ESS data from two independent RCTs, we conducted cross-sectional and longitudinal measure invariance tests in SEM to examine whether CPAP would lead to RS. RESULTS: The ESS demonstrated cross-sectional and longitudinal scalar invariance against CPAP treatments. Its factorial pattern, loadings, and thresholds were invariant between the treatment and control groups and pre- and post-treatment, supporting the comparability of the observed mean ESS scores across time and groups. CONCLUSION: Our results support the validity of the average difference scores of the ESS for quantifying the effectiveness of CPAP on group-level daytime sleepiness in RCTs with relatively large sample sizes.
RESUMEN
Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random.