Randomized clinical trials of cardiovascular disease in obstructive sleep apnea: understanding and overcoming bias.

Pack AI, Magalang UJ, Singh B, Kuna ST, Keenan BT, Maislin G. Randomized clinical trials of cardiovascular disease in obstructive sleep apnea: understanding and overcoming bias. Sleep. 2021 Feb 12; 44(2).

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Abstract:

Three recent randomized control trials (RCTs) found that treatment of obstructive sleep apnea (OSA) with continuous positive airway pressure (CPAP) did not reduce rates of future cardiovascular events. This article discusses the biases in these RCTs that may explain their negative results, and how to overcome these biases in future studies. First, sample selection bias affected each RCT. The subjects recruited were not patients typically presenting for treatment of OSA. In particular, subjects with excessive sleepiness were excluded due to ethical concerns. As recent data indicate that the excessively sleepy OSA subtype has increased cardiovascular risk, subjects most likely to benefit from treatment were excluded. Second, RCTs had low adherence to therapy. Reported adherence is lower than found clinically, suggesting it is in part related to selection bias. Each RCT showed a CPAP benefit consistent with epidemiological studies when restricting to adherent patients, but was underpowered. Future studies need to include sleepy individuals and maximize adherence. Since it is unethical and impractical to randomize very sleepy subjects to no therapy, alternative designs are required. Observational designs using propensity scores, which are accepted by FDA for studies of medical devices, provide an opportunity. The design needs to ensure covariate balance, including measures assessing healthy user and healthy adherer biases, between regular users of CPAP and non-users. Sensitivity analyses can evaluate the robustness of results to unmeasured confounding, thereby improving confidence in conclusions. Thus, these designs can robustly assess the cardiovascular benefit of CPAP in real-world patients, overcoming biases in RCTs.