Comparing sleep studies in terms of the Apnea-Hypopnea Index using dedicated Shiny web application

Marcel M{\l}y\'nczak; Tulio A. Valdez; Wojciech Kukwa

REVIEW 3 major objections 5 minor 36 references

Comparing sleep studies in terms of the Apnea-Hypopnea Index using dedicated Shiny web application

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Correlation alone is insufficient to validate AHI-measuring devices; the paper's eMAE metric weights errors near clinical thresholds.

desk verdict A useful methods-and-software paper for sleep-device validation, but the new eMAE metric's weights are arbitrary and the headline comparison is an artifact of that choice. read the letter →

arxiv 1908.00960 v4 pith:5N4ICANA submitted 2019-08-02 stat.AP

Marcel M{l}yńczak , Tulio A. Valdez , Wojciech Kukwa This is my paper · ORCID

classification stat.AP

keywords Apnea-HypopneaIndexcorrelationcoefficientBland-AltmananalysisclinicalsignificancerankingfunctionShinywebapplicationmeanabsoluteerrordevicevalidation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper contends that validation studies for sleep apnea devices lean too heavily on the correlation coefficient between a device's Apnea-Hypopnea Index (AHI) and the reference polysomnography value, because high correlation can hide systematic offsets and clinically harmful misclassification. To address that, it assembles a toolbox of quantitative agreement measures (regression intercept and slope, Spearman's rho, Lin's concordance correlation coefficient, Bland-Altman analysis, mean absolute error) and qualitative classification measures (accuracy, Cohen's kappa, multi-class ROC AUC), and it argues that both kinds should be reported. Its new contribution is a ranking function that multiplies each absolute AHI error by a weight depending on where the reference value sits relative to clinical severity thresholds, and the resulting extended mean absolute error (eMAE) is proposed as a single clinically weighted error score. The paper also provides a Shiny web application that computes all of these statistics from two columns of AHI values, which is why a clinician or device developer could adopt the approach without writing code.

What carries the argument

The load-bearing object is the ranking function $B$ used inside the extended mean absolute error, $\mathrm{eMAE} = \frac{1}{n}\sum_{i=1}^{n} A(e_i)\, B(\mathrm{ref}_i)\, |\mathrm{res}_i - \mathrm{ref}_i|$. $B$ is built from the clinical thresholds of the AHI severity scale: it assigns weight 1.5 at each threshold ('hotspot', e.g., 5, 15, 30 for adults), weight 0.5 at the midpoint of each subrange, and weight 0.5 at twice the highest threshold, then interpolates between these points with a cubic, sinusoidal, or linear curve, with cubic as the default and the most restrictive. The companion factor $A$ is 0.5 when both measurements fall in the same severity subrange and 1.0 otherwise. The mechanism does the work of the paper's argument: it makes the error metric penalize boundary-crossing errors more heavily and within-subrange differences less heavily, so the eMAE's ranking of devices is intended to track clinical mismanagement rather than raw numerical distance.

What would settle it

A decisive test would use paired device-and-reference AHI data together with the actual clinical decisions those values produced (e.g., CPAP prescribed or not, treatment escalation). Compute both MAE and eMAE for each candidate device, then compare which metric better ranks devices by the observed rate of clinically harmful disagreement (a decision that would change if the reference value were used). If eMAE does not outperform plain MAE in predicting or ranking mismanagement, or if the arbitrary 1.5/0.5 weights can be varied without changing device rankings, the claim that eMAE captures clinical significance would fail.

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Extended reading notes

Core claim

The paper's central claim is that correlation alone is not sufficient to establish that a device measures AHI reliably; agreement should be judged both statistically and clinically. It defines clinical significance through the established severity subranges (normal/mild/moderate/severe; thresholds 5, 15, 30 for adults, and 1, 5, 10 for children), so two values that fall in the same subrange are clinically concordant even if numerically far apart. To give raw-error metrics clinical meaning, it introduces the eMAE, in which each absolute difference is weighted by $B(\mathrm{ref}_i)$, a ranking function equal to 1.5 at each threshold, 0.5 at subrange midpoints, and 0.5 beyond twice the highest threshold, with cubic (default), sinusoidal, or linear interpolation between anchors; an extra factor $A(e_i)$ halves the weight when both measurements are in the same subrange. Applied to two published datasets, eMAE separates a weak portable monitor (MAE 20.21, eMAE 11.90) from a stronger tracheal-sound method (MAE 5.91, eMAE 2.76) more sharply than MAE alone, and the qualitative metrics (accuracy 57.7% versus 84.2%, kappa 0.32 versus 0.76, multi-class AUC 0.733 versus 0.939) reveal clinical consequences that correlation coefficients alone would not show.

Load-bearing premise

The load-bearing premise is that the hand-set weights—1.5 at the severity thresholds, 0.5 at subrange midpoints, and 0.5 for points inside the same subrange—genuinely reflect how much an AHI error matters for patient management, a connection the paper asserts rather than demonstrates, since it states that the chosen values may be different but were set arbitrarily.

Editorial extensions

If this is right

Sleep-device validation studies that report only Pearson correlation should be considered incomplete; the paper's framework implies that accuracy, Cohen's kappa, and multi-class AUC should be reported alongside quantitative agreement metrics.
A device can show a statistically non-significant median difference (Wilcoxon p = 0.95) while still misclassifying roughly 42% of patients, so statistical and clinical significance must be assessed together rather than interchangeably.
The eMAE, especially with the default cubic ranking function, provides a single number that penalizes boundary-crossing errors more than plain MAE, making the relative gap between a weak and a strong device appear larger (2.42x for MAE versus 3.31x for cubic eMAE in the paper's two example datasets).
Because thresholds are adjustable in the Shiny application, the same framework transfers to pediatric AHI thresholds (1, 5, 10) and to any device comparison where clinically defined subranges exist.
When reporting eMAE, the chosen interpolation shape (cubic, sinusoidal, or linear) should be stated, since it changes the value and the shape's restrictiveness.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The eMAE's weights are arbitrary by the authors' own statement, so the obvious next step is empirical calibration: regress actual treatment decisions on AHI errors at various locations to estimate a data-driven weighting function, then compare it with the hand-set 1.5/0.5 weights.
The same ranking-function construction could be applied to any ordinal clinical scale with treatment thresholds (e.g., hypertension stages, hemoglobin A1c categories, tumor grading), giving a general family of clinically weighted error metrics.
The ratio eMAE/MAE could be read as a boundary-concentration index: a high ratio indicates errors cluster near thresholds, which is exactly the situation where average error understates clinical risk.
A sensitivity analysis over the three interpolation shapes, as the app already permits, would show whether device rankings are stable or an artifact of the chosen interpolation.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a useful methods-and-software paper with an honest weakness at its core. The critique of correlation as the primary validation tool for AHI devices is sound and well anchored, and the Shiny app is genuinely useful as a way to get clinicians and statisticians looking at multiple agreement metrics instead of one r value. That part deserves credit. The paper will likely change how some sleep labs report device validation, and that would be an improvement.

The genuinely new piece is the ranking-function-weighted eMAE. I agree with the reader and the stress-test note: the weights (1.5 at hotspots, 0.5 at midpoints) are arbitrary, and the authors say so explicitly in Section 2.2. That means the Table 2 result — eMAE amplifying the difference between datasets from 2.42x to 3.31x — is a construction artifact, not evidence that eMAE captures clinically important errors. The paper presents no sensitivity analysis, no anchor to clinical outcomes, and no justification for why a 1-unit error near AHI 5 should contribute six times more than the same error near AHI 10. The claim in the abstract that the approach is reliable for pediatric studies is also unsupported: no pediatric data are shown.

Other soft spots are minor but real: Tables 2 and 3 have no uncertainty measures, so the reader cannot tell whether the reported differences are meaningful. The Wilcoxon p-value of 0.04 is brushed off as "probably due to higher N," which is hand-wavy. And the app is hosted but the source code is not shipped, so the reproducibility is limited to the deployed version.

None of this sinks the paper. The multi-metric proposal is sensible, the qualitative analysis (accuracy, Kappa, multiclass AUC) is a clear improvement over correlation-only reporting, and the two worked examples are helpful. The problem is that the paper oversells eMAE as a clinically grounded innovation when it is a transparently hand-tuned heuristic. A revision that adds a sensitivity analysis over weights, reports confidence intervals or bootstrap results, drops the unsupported pediatric claim, and releases the source code would make this a solid contribution. As it stands, the recommendation to report eMAE with the 1.5/0.5 defaults is not yet supported.

This is a paper for sleep researchers and device developers, not for statisticians looking for a new estimator. I would send it to reviewers who understand both clinical sleep medicine and measurement agreement. With a moderate revision it could be worth publishing; without it, the eMAE part should not be presented as established.

Referee Report

3 major / 5 minor

Summary. The paper argues that the Pearson correlation coefficient, which is frequently used to validate AHI-measuring devices, is insufficient because it does not reflect clinical significance. The authors review a range of quantitative and qualitative alternatives, including Bland-Altman plots, linear-model parameters, Spearman's rho, Lin's concordance correlation coefficient, MAE, accuracy, sensitivity/specificity, Cohen's Kappa, and multi-class AUC. As a new contribution, they propose an 'extended mean absolute error' (eMAE) that weights AHI errors by a ranking function emphasizing values near clinical thresholds (5, 15, 30 for adults), and they implement all methods in a Shiny web application. The paper demonstrates the approach on two example datasets and reports that eMAE stresses errors more than plain MAE, especially for the less concordant dataset.

Significance. The paper makes a useful and largely correct critical point: correlation alone is an inadequate validation metric for AHI devices, and multi-metric agreement assessment, including qualitative classification metrics, is more clinically relevant. The Shiny application is a practical, accessible tool that could benefit clinicians and statisticians. However, the central novelty, the eMAE ranking function, is currently supported only by hand-set weights that the authors explicitly state were chosen arbitrarily, and the reported advantages of eMAE over MAE are consequences of that choice rather than demonstrated properties. The comparisons in Tables 2 and 3 also lack uncertainty quantification. If the authors add sensitivity analysis, uncertainty measures, and either external clinical anchoring or a clearly exploratory framing, the paper could become a solid methods-and-software contribution.

major comments (3)

[Section 2.2, Eq. (1)] The clinical meaning of eMAE rests entirely on the hand-set weights B(ref) and A(e): 1.5 at hotspots, 0.5 at midpoints, and 0.5 for within-subrange paired values. The paper states in Section 2.2 that 'the chosen values may be different, but we decided to set them arbitrarily.' Because eMAE is a weighted mean absolute error, the observed amplification (Table 2: eMAE cubic 3.31x versus MAE 2.42x) is a direct consequence of this arbitrary weight set, not evidence that eMAE captures clinically important errors. In particular, for a unit error at ref=5 the weight is about 1.5, while at ref=10 it is about 0.5 because the A(e)=0.5 factor applies within the same subrange, producing a six-fold difference that is not clinically motivated. The manuscript should include a sensitivity analysis over plausible weight choices and either anchor the default weights to external clinical criteria or explicitly present eMAE as an exploratory, user-weighted metric rather than a recommended diagnostic standard.
[Section 3, Tables 2 and 3] Tables 2 and 3 report point estimates for MAE/eMAE, accuracy, Cohen's Kappa, and multi-class AUC without any measure of uncertainty. The central comparison in Table 2 (e.g., 2.42x versus 3.31x relative differences) is used to conclude that eMAE stresses errors more than MAE, but with sample sizes of 71 and 304 these differences could plausibly fall within sampling variability. Bootstrap confidence intervals, standard errors, or other uncertainty measures should be provided for all reported metrics, and the comparisons between datasets should account for that uncertainty before drawing conclusions about eMAE's behavior.
[Sections 2.2 and 4] The claim that eMAE assesses clinical significance is not validated against any clinical outcome, treatment decision, or expert-based criterion. The paper demonstrates only that eMAE values differ from MAE values under the chosen weights; it does not show that eMAE better predicts clinical management or patient outcomes than ordinary MAE or simple accuracy. Without such an external anchor, the ranking function is a descriptive re-weighting of absolute error, and the stated objective of assessing 'clinical significance' alongside 'statistical significance' is not yet supported. The authors should either provide criterion validation linking eMAE to clinical endpoints or substantially soften the interpretation, presenting eMAE as one possible exploratory weighting scheme rather than a clinically validated metric.

minor comments (5)

[Section 2.2, Eq. (1)] The notation A(e) is used without defining e; the text refers to 'points (e) in the same subrange,' which is unclear. Define e_i = res_i - ref_i, or state explicitly that A depends on the error being within a single AHI subrange.
[References] References [8] and [31] are the same paper (Yalamanchali et al., 2013) and should be merged or cross-referenced to avoid duplication.
[Section 4, Discussion] The phrase 'lo-factors' should be written as 'LOF factors' or 'local outlier factors' to match the cited density-based local outlier method.
[Section 3, after Table 2] The statement that the cubic ranking function stresses errors 'little more than sinusoidal and linear, as the mean value of ranking function throughout the range is the lowest' is not self-evident from the figures; provide the mean weighting factors or a direct numerical justification.
[Figures 4-6] In the submitted PDF, Figures 4-6 have overlapping or repeated axis annotations and equations, making them hard to read; please regenerate the figures with clearer layout and smaller annotation fonts.

Circularity Check

1 steps flagged · score 4.0 of 10

The eMAE 'amplification' result is definitionally guaranteed by the hand-set weights; the paper's broader multi-metric proposal remains independent.

self definitional [Section 2.2 (Eq. 1) and Section 3 (Table 2 discussion)]
""Of course, the chosen values may be different, but we decided to set them arbitrarily." ... "Presented results show that eMAE enable stressing the errors more than regular MAE, and cubic ranking function little more than sinusoidal and linear, as the mean value of ranking function throughout the range is the lowest (the differences are the highest around hotspots).""

eMAE is defined in Eq. (1) as a weighted mean of absolute errors in which B(ref) is set to 1.5 at hotspots and 0.5 at midpoints, while A(e) is 0.5 for errors inside the same AHI subrange. Consequently, the property that eMAE gives greater relative emphasis to errors near thresholds is true by construction, not discovered from data. The Table 2 comparison (3.31x vs 2.42x relative difference) is an arithmetic consequence of these arbitrarily chosen weights applied to two illustrative datasets, and the sentence claiming eMAE 'stresses' errors more is a restatement of the definition.

full rationale

The paper's central critique of using correlation coefficients for AHI device validation is well grounded in standard statistical literature and is independent of the new eMAE metric. The qualitative analysis methods (accuracy, Kappa, multi-class AUC) and the other quantitative tools (Bland-Altman, Lin's CCC, etc.) are standard or externally referenced, so they do not introduce circularity. The only construction-dependent element is the presentation of eMAE's behavior as a 'result': since the weights in Eq. (1) are hand-set with maxima at clinical thresholds and minima at subrange midpoints, the observation that eMAE emphasizes threshold errors is guaranteed by the definition. The paper is transparent about the arbitrariness of the weights, so this is a disclosed tautology rather than a hidden fit, and it does not undermine the broader suggestion that agreement should be assessed with multiple complementary metrics. However, any claim that eMAE captures clinically important errors is not independently supported; it is an imposed operationalization. This warrants a moderate score of 4 rather than a higher one, because the paper does not present the eMAE behavior as a validated empirical prediction and the rest of the contribution stands on independent statistical reasoning.

Assumptions & free parameters 4 free parameters · 2 assumptions · 1 invented entities

The central claim rests mainly on two domain assumptions: PSG as ground truth and clinical-subrange equivalence. The proposed ranking function adds hand-chosen weights that are not externally calibrated. No new physical or statistical entity with independent evidence is introduced.

free parameters (4)

w_hotspot = 1.5
Default multiplier applied to absolute AHI differences at clinical thresholds (5, 15, 30 for adults); set arbitrarily in Section 2.2.
w_midpoint = 0.5
Default multiplier at midpoints of AHI subranges and beyond 60; set arbitrarily in Section 2.2.
A_same_subrange = 0.5
Multiplier for errors where test and reference values fall in the same clinical subrange, in formula (1); set arbitrarily in Section 2.2.
A_diff_subrange = 1.0
Multiplier for errors across different subranges; set arbitrarily in Section 2.2.

assumptions (2)

domain assumption Polysomnography-derived AHI is treated as the error-free reference, so all disagreement is attributed to the test device.
The entire device-validation framing assumes the PSG reference is ground truth; invoked throughout Sections 1 and 3.
domain assumption All values within one AHI subrange lead to the same clinical management, and the subrange boundaries (adult 5, 15, 30; pediatric 1, 5, 10) are clinically correct.
This underlies the qualitative analysis and the ranking function in Sections 1.3 and 2.2; it is a simplification of real treatment decisions and is not validated in the paper.

invented entities (1)

Ranking function B(ref)
purpose: Multiplies AHI differences in eMAE according to distance from clinical thresholds.
New construct introduced in Section 2.2; the weights are arbitrary and no external outcome data support the specific shape.

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Cite this review

Pith. "Pith review of Comparing sleep studies in terms of the Apnea-Hypopnea Index using dedicated Shiny web application." pith.science (2026). https://pith.science/paper/5N4ICANA

@misc{pith2026190800960,
  author       = {Pith},
  title        = {Pith review of: Comparing sleep studies in terms of the Apnea-Hypopnea Index using dedicated Shiny web application},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5N4ICANA}},
  note         = {Machine review of arXiv:1908.00960}
}

read the original abstract

The Apnea-Hypopnea Index (AHI) is one of the most-used parameters from the sleep study that allows assessing both the severity of obstructive sleep apnea and the reliability of new devices and methods. However, in many cases, it is compared with a reference only via a correlation coefficient, or this value is at least the most emphasized. In this paper, we discuss the limitations of such an approach and list several alternative quantitative and qualitative techniques, along with their interpretations. We propose the assessment of clinical significance along with the statistical one. Qualitative analysis can be used for this purpose, or we suggest using the ranking function which enables consideration of various AHI values with different weights. It can be reliable for both adult-related and pediatric sleep studies. The dedicated Shiny web application, written in R, was developed to enable quick analysis for both physicians and statisticians.

Figures

Figures reproduced from arXiv: 1908.00960 by the authors.

**Figure 2.** The home page of the Shiny web application, supplemental for the paper [17]. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** The graph is produced by: (1) taking into account hotspots (for adults 5, 15 and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The scatter-plots and basic statistics of the data points (in the Shiny web app, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: The Bland-Altman plots of the analyzed data points. a) On the left - the analysis [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: The modified Bland-Altman plots of the analyzed data points, along with the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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