Making clinical trials more relevant: improving and validating the PRECIS tool for matching trial design decisions to trial purpose
Bibliographic record
Abstract
BACKGROUND: If you want to know which of two or more healthcare interventions is most effective, the randomised controlled trial is the design of choice. Randomisation, however, does not itself promote the applicability of the results to situations other than the one in which the trial was done. A tool published in 2009, PRECIS (PRagmatic Explanatory Continuum Indicator Summaries) aimed to help trialists design trials that produced results matched to the aim of the trial, be that supporting clinical decision-making, or increasing knowledge of how an intervention works. Though generally positive, groups evaluating the tool have also found weaknesses, mainly that its inter-rater reliability is not clear, that it needs a scoring system and that some new domains might be needed. The aim of the study is to: Produce an improved and validated version of the PRECIS tool. Use this tool to compare the internal validity of, and effect estimates from, a set of explanatory and pragmatic trials matched by intervention. METHODS: The study has four phases. Phase 1 involves brainstorming and a two-round Delphi survey of authors who cited PRECIS. In Phase 2, the Delphi results will then be discussed and alternative versions of PRECIS-2 developed and user-tested by experienced trialists. Phase 3 will evaluate the validity and reliability of the most promising PRECIS-2 candidate using a sample of 15 to 20 trials rated by 15 international trialists. We will assess inter-rater reliability, and raters' subjective global ratings of pragmatism compared to PRECIS-2 to assess convergent and face validity. Phase 4, to determine if pragmatic trials sacrifice internal validity in order to achieve applicability, will compare the internal validity and effect estimates of matched explanatory and pragmatic trials of the same intervention, condition and participants. Effect sizes for the trials will then be compared in a meta-regression. The Cochrane Risk of Bias scores will be compared with the PRECIS-2 scores of pragmatism. DISCUSSION: We have concrete suggestions for improving PRECIS and a growing list of enthusiastic individuals interested in contributing to this work. By early 2014 we expect to have a validated PRECIS-2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.822 | 0.917 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".