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Record W4210823726 · doi:10.1371/journal.pmed.1003896

Patient-reported outcomes and target effect sizes in pragmatic randomized trials in ClinicalTrials.gov: A cross-sectional analysis

2022· review· en· W4210823726 on OpenAlexafffund
Shelley Vanderhout, Dean Fergusson, Jonathan Cook, Monica Taljaard

Bibliographic record

VenuePLoS Medicine · 2022
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsClinical trialSample size determinationMedicineRandomized controlled trialMEDLINEWilcoxon signed-rank testFamily medicineAlternative medicineMedical physicsStatisticsInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Use of patient-reported outcomes (PROs) and patient and public engagement are critical ingredients of pragmatic trials, which are intended to be patient centered. Engagement of patients and members of the public in selecting the primary trial outcome and determining the target difference can better ensure that the trial is designed to inform the decisions of those who ultimately stand to benefit. However, to the best of our knowledge, the use and reporting of PROs and patient and public engagement in pragmatic trials have not been described. The objectives of this study were to review a sample of pragmatic trials to describe (1) the prevalence of reporting patient and public engagement; (2) the prevalence and types of PROs used; (3) how its use varies across trial characteristics; and (4) how sample sizes and target differences are determined for trials with primary PROs. METHODS AND FINDINGS: This was a methodological review of primary reports of pragmatic trials. We used a published electronic search filter in MEDLINE to identify pragmatic trials, published in English between January 1, 2014 and April 3, 2019; we identified the subset that were registered in ClinicalTrials.gov and explicitly labeled as pragmatic. Trial descriptors were downloaded from ClinicalTrials.gov; information about PROs and sample size calculations were extracted from the manuscript. Chi-squared, Cochran-Armitage, and Wilcoxon rank sum tests were used to examine associations between trial characteristics and use of PROs. Among 4,337 identified primary trial reports, 1,988 were registered in CT.gov, of which 415 were explicitly labeled as pragmatic. Use of patient and public engagement was identified in 39 (9.4%). PROs were measured in 235 (56.6%): 144 (34.7%) used PROs as primary outcomes and 91 (21.9%) as only secondary outcomes. Primary PROs were symptoms (64; 44%), health behaviors (36; 25.0%), quality of life (17; 11.8%), functional status (16; 11.1%), and patient experience (10; 6.9%). Trial characteristics with lower prevalence of use of PROs included being conducted exclusively in children or adults over age 65 years, cluster randomization, recruitment in low- and middle-income countries, and primary purpose of prevention; trials conducted in Europe had the highest prevalence of PROs. For the 144 trials with a primary PRO, 117 (81.3%) reported a sample size calculation for that outcome; of these, 71 (60.7%) justified the choice of target difference, most commonly, using estimates from pilot studies (31; 26.5%), standardized effect sizes (20; 17.1%), or evidence reviews (16; 13.7%); patient or stakeholder opinions were used to justify the target difference in 8 (6.8%). Limitations of this study are the need for trials to be registered in ClinicalTrials.gov, which may have reduced generalizability, and extracting information only from the primary trial report. CONCLUSIONS: In this study, we observed that pragmatic trials rarely report patient and public engagement and do not commonly use PROs as primary outcomes. When provided, target differences are often not justified and rarely informed by patients and stakeholders. Research funders, scientific journals, and institutions should support trialists to incorporate patient engagement to fulfill the mandate of pragmatic trials to be patient centered.

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)0.075
metaresearch head score (Gemma)0.077
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0750.077
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0200.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.463
GPT teacher head0.573
Teacher spread0.110 · 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 designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations24
Published2022
Admission routes2
Has abstractyes

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