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Record W4224922604 · doi:10.1111/cea.14144

Individual‐patient data and aggregate evidence syntheses and the future of allergy‐immunology research

2022· letter· en· W4224922604 on OpenAlexaff
Derek K. Chu

Bibliographic record

VenueClinical & Experimental Allergy · 2022
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpact
Fundersnot available
KeywordsPsychological interventionAtopic dermatitisMeta-analysisAggregate dataSystematic reviewMedicineAggregate (composite)Raw dataMEDLINEPsychologyComputer scienceDermatologyPathologyPsychiatry

Abstract

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Systematic summaries of the available evidence are a fundamental component in achieving optimal health outcomes.1 Traditional evidence hierarchies place systematic reviews and meta-analyses at their pinnacle. Meta-analyses (MA) can be subdivided into two analytic approaches: those that primarily combine existing published data using the values reported in individual studies, called ‘aggregate data meta-analysis’, where individual trials are a kind of unit of analysis; and those that seek to combine the raw study data from multiple studies, called ‘individual patient data [IPD] meta-analysis’, where the unit of analysis is individual participants that are clustered within individual studies. IPD meta-analyses have been claimed to be the ‘gold standard’ of evidence synthesis. What are the merits of IPD MA and why are investigators not doing more of them? In this issue, Van Vogt, Cro and colleagues, representing the Skincare interventions for the prevention of atopic dermatitis (SCiPAD) collaboration leadership, report a comparison of aggregate MA vs IPD MA of skin care interventions, primarily moisturizers (emollients), vs standard care for the prevention of atopic dermatitis and IgE-mediated food allergy in infants.2 Smartly planned, excellently done, spectacularly interpreted and impactfully informative, they report similar effect estimates using both analytic approaches, and the IPD approach better addressed the between-study heterogeneity, allowed more sophisticated statistical analyses and could reduce research waste. Given these advantages, should IPD MA be the new norm of evidence synthesis? Table 1 details some high-level considerations for reviewers considering embarking on an aggregate MA versus IPD MA. Other factors and explanations are detailed elsewhere.3-5 Probably, the most relevant factor will be the added resource implications required with IPD MA over aggregate MA. The added time (likely a year or more), staff and costs (thousands of pounds) are all likely large enough barriers to dissuade most investigators. Guideline developers are likely to fund multiple questions using aggregate MA methods rather than focusing on their time and money, likely on fewer or only one question, for an IPD MA. Most IPD MAs are, therefore, likely to be the academic pursuit of select highly invested investigators. Such pursuits can be transformative. This is borne out by exceptional examples in other fields of medicine such as fluid resuscitation in sepsis,6 or the role of corticosteroids and IL-6 inhibitors for severe COVID-19.7 New funding mechanisms and dedicating portions of clinical trial budgets are clearly needed to reduce barriers to conducting IPD MAs and thereby allowing them to routinely inform optimal patient care. Beyond funding, trialist teams must pair with expert systematic reviewers and methodologists since IPD MA blends elements of both. 0.5 FTE statistician,2 1.0 FTE Project manager,2 2nd reviewer £60,000+ per year (estimated from Vogt et al2 and Imperial College human resources websites) Collaborator meetings Travel beyond that expected for aggregate MA Considering the value of IPD MA in reducing research waste highlights a big problem in the allergy-immunology field. Registered 5 years ago, the SCiPAD IPD MA was prospectively planned and benefitted from a large group of academic investigators that were independently conducting investigator-initiated clinical trials to answer a common question, were open to collaboration and had strong evidence synthesis methodologic support. Most studies in the field of allergy and immunology, however, are not clinical trials (i.e. on average, less robust data collection standardization, oversight and storage); some investigators may be unwilling to share (e.g. perception of competition, politics, ego, intellectual property concerns and no funding); most studies are industry-sponsored rather than investigator-initiated (i.e. industry typically owns the study participant data, not the investigator); obtaining IPD from industry for meta-analysis typically faces multiple barriers (e.g. no easy point of contact; scrutiny by company staff and lawyers; investigators often necessarily supplying entire protocol with no guarantee of confidentiality or receiving data) and is often neither timely nor successful; the previous poster child of evidence-based medicine, the systematic review, faces erosion by an explosion of duplicitous, methodologically weak, highly conflicted and uncredible systematic reviews. To achieve optimal population health, and to appropriately honour the patients and participants of research studies, evidence ecosystems must actively seek open science and a culture of sharing.8 The SCiPAD investigators’ success is emblematic of what the allergy-immunology field can do and what it needs to do more of. The authors declare the non-financial (intellectual) role as Chair, Evidence in Allergy Group, McMaster University, and developed GRADE guidelines. DC drafted the manuscript, reviewed it, and approved it.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.129
metaresearch head score (Gemma)0.060
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1290.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0090.012
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0170.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.837
GPT teacher head0.598
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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