Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Upper airway dynamic imaging during tidal breathing in awake and asleep subjects with obstructive sleep apnea and healthy controls.

We used magnetic resonance imaging (MRI) to quantify change in upper airway dimension during tidal breathing in subjects with obstructive sleep apnea (OSA, N = 7) and BMI-matched healthy controls (N = 7) during both wakefulness and natural sleep. Dynamic MR images of the upper airway were obtained on a 1.5 T MR scanner in contiguous 7.5 mm-thick axial slices from the hard palate to the epiglottis along with synchronous MRI-compatible electroencephalogram and nasal/oral flow measurements. The physiologic data were retrospectively scored to identify sleep state, and synchronized with dynamic MR images. For each image, the upper airway was characterized by its area, and linear dimensions (lateral and anterior-posterior). The dynamic behavior of the upper airway was assessed by the maximum change in these parameters over the tidal breath. Mean upper airway caliber was obtained by averaging data over the tidal breath. There was no major difference in the upper airway structure between OSA and controls except for a narrower airway at the low-retropalatal/high-retroglossal level in OSA than in controls. Changes in upper airway size over the tidal breath ((maximum - minimum)/mean) were significantly larger in the OSA than in the control group in the low retropalatal/high retroglossal region during both wakefulness and sleep. In the four OSA subjects who experienced obstructive apneas during MR imaging, the site of airway collapse during sleep corresponded to the region of the upper airway where changes in caliber during awake tidal breathing were the greatest. These observations suggest a potential role for dynamic OSA imaging during wakefulness

Trazodone Effects on Obstructive Sleep Apnea and Non-REM Arousal Threshold.

RationaleA low respiratory arousal threshold is a physiological trait involved in obstructive sleep apnea (OSA) pathogenesis. Trazodone may increase arousal threshold without compromising upper airway muscles, which should improve OSA.ObjectivesWe aimed to examine how trazodone alters OSA severity and arousal threshold. We hypothesized that trazodone would increase the arousal threshold and improve the apnea/hypopnea index (AHI) in selected patients with OSA.MethodsSubjects were studied on two separate nights in a randomized crossover design. Fifteen unselected subjects with OSA (AHI ≥ 10/h) underwent a standard polysomnogram plus an epiglottic catheter to measure the arousal threshold. Subjects were studied after receiving trazodone (100 mg) and placebo, with 1 week between conditions. The arousal threshold was calculated as the nadir pressure before electrocortical arousal from approximately 20 spontaneous respiratory events selected randomly.Measurements and main resultsCompared with placebo, trazodone resulted in a significant reduction in AHI (38.7 vs. 28.5 events/h, P = 0.041), without worsening oxygen saturation or respiratory event duration. Trazodone was not associated with a significant change in the non-REM arousal threshold (-20.3 vs. -19.3 cm H2O, P = 0.51) compared with placebo. In subgroup analysis, responders to trazodone spent less time in N1 sleep (20.1% placebo vs. 9.0% trazodone, P = 0.052) and had an accompanying reduction in arousal index, whereas nonresponders were not observed to have a change in sleep parameters.ConclusionsThese findings suggest that trazodone could be effective therapy for patients with OSA without worsening hypoxemia. Future studies should focus on underlying mechanisms and combination therapies to eliminate OSA. Clinical trial registered with www.clinicaltrials.gov (NCT 01817907)

Trazodone Effects on Obstructive Sleep Apnea and Non-REM Arousal Threshold.

RationaleA low respiratory arousal threshold is a physiological trait involved in obstructive sleep apnea (OSA) pathogenesis. Trazodone may increase arousal threshold without compromising upper airway muscles, which should improve OSA.ObjectivesWe aimed to examine how trazodone alters OSA severity and arousal threshold. We hypothesized that trazodone would increase the arousal threshold and improve the apnea/hypopnea index (AHI) in selected patients with OSA.MethodsSubjects were studied on two separate nights in a randomized crossover design. Fifteen unselected subjects with OSA (AHI ≥ 10/h) underwent a standard polysomnogram plus an epiglottic catheter to measure the arousal threshold. Subjects were studied after receiving trazodone (100 mg) and placebo, with 1 week between conditions. The arousal threshold was calculated as the nadir pressure before electrocortical arousal from approximately 20 spontaneous respiratory events selected randomly.Measurements and main resultsCompared with placebo, trazodone resulted in a significant reduction in AHI (38.7 vs. 28.5 events/h, P = 0.041), without worsening oxygen saturation or respiratory event duration. Trazodone was not associated with a significant change in the non-REM arousal threshold (-20.3 vs. -19.3 cm H2O, P = 0.51) compared with placebo. In subgroup analysis, responders to trazodone spent less time in N1 sleep (20.1% placebo vs. 9.0% trazodone, P = 0.052) and had an accompanying reduction in arousal index, whereas nonresponders were not observed to have a change in sleep parameters.ConclusionsThese findings suggest that trazodone could be effective therapy for patients with OSA without worsening hypoxemia. Future studies should focus on underlying mechanisms and combination therapies to eliminate OSA. Clinical trial registered with www.clinicaltrials.gov (NCT 01817907)

Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy.

A possible precision-medicine approach to treating obstructive sleep apnoea (OSA) involves targeting ventilatory instability (elevated loop gain) using supplemental inspired oxygen in selected patients. Here we test whether elevated loop gain and three key endophenotypic traits (collapsibility, compensation and arousability), quantified using clinical polysomnography, can predict the effect of supplemental oxygen on OSA severity.36 patients (apnoea-hypopnoea index (AHI) >20 events·h-1) completed two overnight polysomnographic studies (single-blinded randomised-controlled crossover) on supplemental oxygen (40% inspired) versus sham (air). OSA traits were quantified from the air-night polysomnography. Responders were defined by a ≥50% reduction in AHI (supine non-rapid eye movement). Secondary outcomes included blood pressure and self-reported sleep quality.Nine of 36 patients (25%) responded to supplemental oxygen (ΔAHI=72±5%). Elevated loop gain was not a significant univariate predictor of responder/non-responder status (primary analysis). In post hoc analysis, a logistic regression model based on elevated loop gain and other traits (better collapsibility and compensation; cross-validated) had 83% accuracy (89% before cross-validation); predicted responders exhibited an improvement in OSA severity (ΔAHI 59±6% versus 12±7% in predicted non-responders, p=0.0001) plus lowered morning blood pressure and "better" self-reported sleep.Patients whose OSA responds to supplemental oxygen can be identified by measuring their endophenotypic traits using diagnostic polysomnography

Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy.

A possible precision-medicine approach to treating obstructive sleep apnoea (OSA) involves targeting ventilatory instability (elevated loop gain) using supplemental inspired oxygen in selected patients. Here we test whether elevated loop gain and three key endophenotypic traits (collapsibility, compensation and arousability), quantified using clinical polysomnography, can predict the effect of supplemental oxygen on OSA severity.36 patients (apnoea-hypopnoea index (AHI) >20 events·h-1) completed two overnight polysomnographic studies (single-blinded randomised-controlled crossover) on supplemental oxygen (40% inspired) versus sham (air). OSA traits were quantified from the air-night polysomnography. Responders were defined by a ≥50% reduction in AHI (supine non-rapid eye movement). Secondary outcomes included blood pressure and self-reported sleep quality.Nine of 36 patients (25%) responded to supplemental oxygen (ΔAHI=72±5%). Elevated loop gain was not a significant univariate predictor of responder/non-responder status (primary analysis). In post hoc analysis, a logistic regression model based on elevated loop gain and other traits (better collapsibility and compensation; cross-validated) had 83% accuracy (89% before cross-validation); predicted responders exhibited an improvement in OSA severity (ΔAHI 59±6% versus 12±7% in predicted non-responders, p=0.0001) plus lowered morning blood pressure and "better" self-reported sleep.Patients whose OSA responds to supplemental oxygen can be identified by measuring their endophenotypic traits using diagnostic polysomnography

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants