Rationale: Continuous positive airway pressure (CPAP), the first line therapy for obstructive sleep apnea (OSA), is considered effective in reducing daytime sleepiness. Its efficacy relies on adequate adherence, often defined as >4 hours per night. However, this binary threshold may limit our understanding of the causal effect of CPAP adherence and daytime sleepiness, and a multilevel approach for CPAP adherence can be more appropriate. Objectives: In this study, we show how two causal inference methods can be applied on observational data for the estimation of the effect of different ranges of CPAP adherence on daytime sleepiness as measured by the Epworth Sleepiness Scale (ESS). Methods: Data were collected from a large prospective observational French cohort for patients with OSA. Four groups of CPAP adherence were considered (0-4, 4-6, 6-7, and 7-10 h per night). Multivariable regression, inverse-probability-of-treatment weighting (IPTW), and inverse propensity weighting with regression adjustment (IPW-RA) were used to assess the impact of CPAP adherence level on daytime sleepiness. Results: In this study, 9,244 patients with OSA treated by CPAP were included. The mean initial ESS score was 11 (±5.2), with a mean reduction of 4 points (±5.1). Overall, there was evidence of the causal effect of CPAP adherence on daytime sleepiness which was mainly observed between the lower CPAP adherence group (0-4 h) compared with the higher CPAP adherence group (7-10 h). There are no differences by considering higher level of CPAP adherence (>4 h). Conclusions: We showed that IPTW and IPW-RA can be easily implemented to answer questions regarding causal effects using observational data when randomized trials cannot be conducted. Both methods give a direct causal interpretation at the population level and allow the assessment of the appropriate consideration of measured confounders.
Keywords: causal inference; daytime sleepiness; inverse probability weight; sleep apnea.