Abstract

Variable adherence to prescribed therapies for sleep disorders is commonplace. This study was designed to integrate three available statistical technologies (instrumental variables, residual inclusion, and shrinkage) to allow sleep investigators to employ data on variable adherence in the estimation of the causal effect of treatment as received on clinical outcomes.Using data from the Apnea Positive Pressure Long-term Efficacy Study (APPLES), regression adjustment for observed and unobserved confounders was applied to two primary neurocognitive outcomes, plus two measures of sleepiness. We demonstrate how to obtain estimates of reduced uncertainty for the causal effect of treatment as received for continuous positive airway pressure (CPAP) within clinical subpopulations (defined by baseline disease severity) of sleep apnea patients.Following six months of treatment, statistically significant improvements caused by device adherence were detected for subjective sleepiness in mild, moderate, and severe disease, objective sleepiness in severe disease, and attention and psychomotor function in moderate disease. Some evidence for worsening of learning and memory due to increased adherence in moderate disease was also detected. Application to APPLES illustrates that this method can yield bias corrections for unobserved confounders that are substantial-revealing new clinical findings. Use of this fully general method throughout sleep research could sharpen understanding of the true efficacy of pharmacotherapies, medical devices, and behavioral interventions. Extensive technical appendices are provided to facilitate application of this general method. Clinicaltrials.gov identifier NCT00051363.

View details for PubMedID 28899524

View details for PubMedCentralID PMC5609486