Statistical Misinterpretations and Clinical Practice Guidelines: any examples?

topical areas health services research
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“It seems to me that such cognitive/epistemological errors are far more insidious than technical errors in p-value interpretation, and much more capable of insinuating themselves into CPGs.”

This @davidcnorrismd post changed my understanding of “Threshold Science” which I have studied for over a decade.

I have long considered Threshold Science (The use of guessed threshold sets as gold standards or endpoints for RCT) a type of “pathological science”, after Langmuir, (wherein the “pathology” of the science is induced by an early undetected technical error in methodology.) Yet it never quite fit. Kuhn’s teachings also failed as the behavior he describes applies to both good science and pathological science.

I see now that Threshold Science comprises a different type of pathological science induced by a fundamental cognitive error rather than a technical error
The distinction between Langmuir’s Type I pathological science (fundamental methodological error) and Type II (fundamental cognitive error) is very important. Trusting, dutiful and particulatly the best trained in the discipline will be not be able to detect the error of Type II Pathological Science whereas they may be be able to detect the error of Type I.

Type II pathological science is probagated by text and mentorship. Indeed, since all in the disipline are unknowingly faithful to the pathologic teaching, its practice is mandated to receive funding and to publish in the top journals.

An entire scientific discipline (eg sleep apnea science), may be based on Type II pathological science. They may recognize that the science is not working but they will be complely confused as they cannot understand why.

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Here is another good example, mentioned in another data methods thread:

Blockquote
We find that in all cases the provision of HHC increased the probability of readmission of the treated patients. This casts doubt on the appropriateness of the 30-day readmission rate as an indicator of hospital performance and a criterion for hospital reimbursement, as it is currently used for Medicare patients.

Alecos Papadopoulos & Roland B. Stark (2021) Does Home Health Care Increase the Probability of 30-Day Hospital Readmissions? Interpreting Coefficient Sign Reversals, or Their Absence, in Binary Logistic Regression Analysis, The American Statistician, 75:2, 173-184, DOI: 10.1080/00031305.2019.1704873 link

It is a shame this paper is paywalled, but here is a brief blog post by one of the authors:

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Another good example found on Dr. Harrell’s blog – not sure how I missed it,

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I think I’ve found something worth studying more closely:

This resource is widely used for hospital and health insurance quality assessment. It will be educational to look at the guidelines and their sources, and examine them from a decision theoretic perspective.

An early article, discussing the relevance, is here:

Off Topic: I think I finally found the Senn commentary on the FDA 2 trial rule alluded to by @pmbrown above. I should organize these a bit better.

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it’s also covered in his book - “12.2.8 The two-trials rule” (2nd edition). I guess that’s what was in my mind

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This was posted by Frank on Twitter:

Blockquote
Additional motivation to identify and treat sepsis cases lies in the fact that sepsis serves as a system-level quality measure, with hospitals judged by both the by the federal Department of Health and Human Services and the CDC on their sepsis rates. Complicating efforts to reduce sepsis is how difficult it can be to diagnose—both accurately and quickly.

Given what I’ve learned about sepsis from @llynn, this failure of the prediction model isn’t surprising.

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No one should be surprised by this. It is, however, tragic on quite a massive scale.

Quote from the article
"He added that Epic isn’t wrong in their analysis. We differ in our definition of the onset and timing of sepsis. In our view, their definition of sepsis based on billing codes alone is imprecise and not the one that is clinically meaningful to a health system or to patients.”

This is the point I have repeated made in this forum. The analyses (the statistics) are perceived as correct but the definition (measurement) is wrong.

That makes little sense in the real world (for example the world of study design by statistician or bridge design by an engineer). In both those cases the measurement is part of the math. It is a continous function.

If, when designing a bridge, the engineer does not consider the structural math of the concrete to be used, she is a fool. When the bridge collapses its not mitigating that her math was correct because her math should be defined by knowledge of the structural math defined by the specified concrete mix. It is a continuous function.

Analysis of measurement is part of the design. Every engineer a bridge is a continuous function. Its strength depends on the weakest math of the function.

So too a study design.

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What in the world do billing codes have to do with the prediction of a clinical syndrome? That should be defined purely on anatomical or physiological (and maybe psychosocial) criteria.

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It’s even much worse than that makes it seem. I don’t think the original analysis had a proper time zero with the use of only true baselines as predictors.

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Clinical research with the application of statistics to billing codes as a surrogate for physiologic data was popular a few years ago. They call it clinical research using “administrative data”. In medtwitter I called it #Billing CodeScience" (BCS) and I decried its caprecious derivation and use. In fact, i beleive my twitter based critique of this pathological science brought it down.

To make matters worse, billing coding for sepsis was very poor. Undeterred, to compensate, the “measurement” for sepsis was based on the combination of two billing codes (one for infection and a second for organ failure). This was called the “Angus Method”. Nevermind that the two codes might not have been related.

As I predicted on twitter, billing code science failed and was recently abandoned but BCS left some residual in its wake. It appears that this algorithm may have been one of them.

BCS was, however, very widely used by the top brass with the seminal BCS research published in JAMA for example.

I have been trying to seek help from this group pointing out that mainstream statiticians should not dutifully apply math to a capricious nonreproducible measurements.

“Billing code science” (the use of billing code combinations as surrogates for physiologic signals) shows that the phrase “It is a single function” should be taught at every level.

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“When the desirable is not available, the available becomes desirable.”

— Nigerian proverb

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I can’t remember where I found it (probably one of Frank’s reference lists), but this paper on risk prediction as preferable to diagnosis (classification) is really where clinical care needs to go, especially with regulators insisting on health systems that get payments engage in financial risk taking for the care provided.

That, in turn, implies that quality assurance and improvement would be involved in the development and implementation of risk prediction (and reduction) models, instead of checklists based on arbitrary dichotomies.

Addendum: A related article on risk adjusted payments for primary care. These are the types of models doctors, nurses, and other professionals on the front lines need. A critical point – educating these people on when the model is not applicable will be critical if outcomes are to be improved.

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" Only two studies (n = 1298) measured referrals to domestic violence support services following clinical identification. We detected no evidence of an effect on referrals (OR 2.24, 95% CI 0.64 to 7.86, low quality evidence)."

I think this also maybe an example for wrong interpretation of confidence intervals. Interestingly, this is a cochrane meta-analysis

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“administrative data” now euphemistically called “real world data” may supplement drug approval claims

“Real-world data (RWD) forms the basis for real-world evidence (RWE) and can be extracted from a broad range of sources such as patient registries, health care databases, claims databases, patient networks, social media, and patient-generated data from wearables” Trial designs using real-world data: The changing landscape of the regulatory approval process

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Billing codes were shown to be poor surrogates for physiologic data in sepsis and therefore BCS was abandoned. Consider how meaningless the statistics were in sepsis BCS. All those resources wasted.

However, sepsis is difficult to define objectively even with physiologic data. Obviously administative data has utility but the role of the statistician, IMHO, will be to define its place as a reproducible measurement, relevant the hypothesis under test, before dutifully applying statistical processing.

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I’m somewhat surprised that anyone took this proposal seriously. It seems like a category error to me; like asking about the chemical properties of oil or metals by studying futures prices.

I bet they used “machine learning” to derive it, however.

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not completely abandoned - i just did such analysis. Im no longer involved, but i left a bunch of completed estimates with people who will try to publish them. Issue noted here re reproducible What is a fake measurement tool and how are they used in RCT - #29 by pmbrown Not sure what can be done

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There was formal teaching to use BCS in sepsis so no one can blame anyone that performed this type of research. BCS for sepsis emerged during the heady era of “big data”.

It probably works in some better defined fields. Admonitions, and the article below, precipitated the decline of sepsis billing code science.

QUOTE FROM THE CONCLUSION
“…in contrast to claims-based analyses, neither the incidence of sepsis nor the combined outcome of death or discharge to hospice changed significantly between 2009-2014. The findings also suggest that EHR-based clinical data provide more objective estimates than claims-based data for sepsis surveillance.”

The last sentence states well that which I predicted on twitter.

Of course, in retrospect, BCS for sepsis should have been tested against EHR clinical data before it was promulgated as a sepsis research method by experts.

When research methods are found to be flawed that should be promulgated. It should not require a pubmed search to learn that a well established tool is no longer considered valid.

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Here is another example of apparent adverse guideline effect induced by misinterpretation or incorrect application of statistics.

The initial practice guidelines for COVID-19 were based on the estabished guidelines for treatment of Adult Respiratory Distress Syndrome (ARDS) (as due to influenza).

This statistically misinterpreted study shows why there was confidence in this approach.

Note the raw mortality is not significantly different but encouragingly the “SOFA score-adjusted mortality of H1N1 patients was significantly higher than that of COVID-19 patients, with a rate ratio of 2.009 (95% CI, 1.563-2.583; P < .001).”

In march 2020, not to worry!

We now know this was misleading and that modification of the guidelines for COVID-19 were required but this was delayed. Many were probably compelled by the SOFA adjusted P value here.

The problem is that SOFA is a “fake” (guessed) one-size-fits-all measurement tool for RCT for reasons I have noted in this forum.

Looking deeper, why would adjusting for SOFA be so misleading?

SOFA is a summation score derived from ordinal thresholds of 6 signals.

https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.mdcalc.com/sequential-organ-failure-assessment-sofa-score&ved=2ahUKEwjskMueh7nxAhWDFlkFHaooDjkQFjACegQIGhAC&usg=AOvVaw2H1jBD1fkPUCqZHwK6xXqJ&cshid=1624839636768

Of these 6 signals the one most likely to be perturbed in COVID19 is the PaO2/FIO2. But the PaO2/FIO2 is a volatile signal and affected by reversible things like low PEEP or mucous pluging. In many cases PaO2/FIO2 is readily correctable. Therefore the timing of the PaO2/FIO2 (early or late in the care) greatly influences the mortality prediction. Here we see influenza (H1N1) cases presented with lower PaO2/FIO2.

The “take home point” of the editor published in March 2020 in the prestigious journal Chest was misleading.

"Interpretation:

Compared with H1N1, patients with COVID-19-induced ARDS had lower severity of illness scores at presentation and lower SOFA score adjusted mortality."

The article also suggests (without data presented) that corticosteroids were not beneficial and suggested they may be harmful. (Later corticosteroids became the standard of care.)

This is how adjustment with a guessed (1996) traditional score (which has reached standard use by PI & statiscians) can be misleading.

Yet SOFA was originally guessed for use with sepsis and we have discussed its limitations elsewhere in this forum. Clearly adjustments like this can produce misleading results and adversly effect public policy and medical guidelines.

This is a form of “Threshold Science”. Threshold science is not the use of thresholds in science as this is a compromise often made. Rather “Threshold Science” is a specific subset of science. It is a strange science which emerged in the late 1970s and 80s and uses of guessed threshold sets like SOFA as gold standards (criteria), independent variables, adjustments, or outputs in RCT.

Here is an example of the harm threshold science can render.

If anyone would like to learn more about this unique pathologic science please message me.

I have been researching threshold science for over a decade and have a robust archive to share. This would be a powerful overarching and nascent topic for publication.

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i find this intriguing and wich i had more detail:

Orphazyme Arimoclomol Needs Better Evidence In NPC

"The FDA questioned the clinical severity scale that served as the primary endpoint for the pivotal trial of Orphazyme A/S’s Miplyffa (arimoclomol) in a complete response letter announced 18 June.

The arimoclomol NDA for treatment of Niemann-Pick disease type C (NPC), a rare lysosomal storage disease, rests on a single Phase II/III using the 5-domain NPC Clinical Severity Scale (NPCCSS) to determine disease progression for a primary endpoint.

In the CRL, the FDA requested “additional qualitative and quantitative evidence to further substantiate the validity and interpretation” of the scale, “in particular, the swallow domain,” the company reported. The NPCCSS evaluates the five domains considered most clinically relevant: ambulation, swallow, cognition, speech and fine motor skills. "

https://pink.pharmaintelligence.informa.com/PS144544/Keeping-Track-US-FDA-Issues-Alzheimers-Breakthrough-Designations-Arimoclomol-CRL-Rinvoq-Delays

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