P-value and Bayesian Analysis in Randomized-controlled Trials in Child health Research Published Over Ten Years, 2007 to 2017: A Review Protocol
Bibliographic record
Abstract
Abstract Background: There is an unresolved debate about the reliability of the interpretation of P-value. Some investigators have suggested that an alternative Bayesian method is preferred in conducting health research. As randomized-controlled trials (RCTs) are important in generating research evidence, we decided to investigate the extent, if any, the inferential statistical framework in published RCTs in child health research have changed over 10 years. We aim to examine the change in P-value and Bayesian analysis in RCTs in child health research papers published from 2007 to 2017. Methods: We will search the Cochrane Central Register of Controlled Trials (Wiley) to identify relevant citations. We will leverage a pre-existing sample of child health RCTs published in 2007 (n=300) used in our previous study of reporting quality of pediatric RCTs. Using the same strategy and study selection methods, we will identify a comparable random sample of child health RCTs published in 2017 (n=300). Eligible studies will include RCTs in health research among individuals aged 21 years and below. One reviewer will select studies for inclusion and extract the data and another reviewer will verify these. Disagreements will be resolved by a discussion between reviewers or by involving another reviewer. We will perform a descriptive analysis of 2007 and 2017 samples and analyze the results using both the frequentist and Bayesian methods. Discussion: We will present specific characteristics of the clinical trials from 2007 and 2017 in tabular and graphical forms. We will report the difference in the proportion of P-value and Bayesian analysis between 2007 and 2017 to assess the 10-year change. Clustering around P-values of significance, if observed, will be reported. This review has the potential to help inform the need for a change in the methodological approach from the null hypothesis significance test to Bayesian methods.Systematic review registration: Open Science Framework https://osf.io/aj2df
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.238 | 0.447 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".