Bibliographic record
Abstract
Background and Objectives: Missing participant data are likely to bias the results of randomized control trials (RCTs) when the reason for missingness is associated with status on the outcome of interest. Unlike dichotomous MPD in RCTs, which have been thoroughly investigated, knowledge regarding continuous MPD in RCTs is much more limited. Our objectives were 1) using an adapted checklist, to assess the reporting quality of simulation studies comparing methods to deal with continuous MPD; 2) identify optimal methods proposed by biostatisticians and tested in simulations studies for continuous MPD in RCTs; 3) evaluate how authors report MPD, and how they plan and conduct analyses to deal with MPD in RCTs. Methods: We conducted two systematic surveys. The first identified methods papers published till 2015 January that compared statistical approaches to deal with continuous MPD in RCTs using at least one simulation. In this sample, we considered both the quality of reporting and the results. The second survey identified a representative sample of individual RCTs published in 2014 in core journals reporting the results of at least one continuous variable addressing a patient-important outcome. Results and conclusion: Our survey identified important limitations in reporting quality of simulation studies that compared statistical approaches to deal with continuous MPD, particularly in the reporting of simulation procedures. Only one of 60 studies reported the random number generator used and none reported starting seeds or failures during simulation. Less then half reported software used to perform simulation (41.7%) or analysis (48.3%), and only 4 (5%) reported justification of number of simulations. When facing continuous MPD in RCTs, results of simulation studies demonstrate that trialists seeking optimal approaches may choose robust regression or mixed models and avoid using last observation caring forward. Continuous MPD frequently occurs in RCTs and the extent is typically substantial (median greater than 10%). Methods sections in trial reports typically do not provide adequate detail on how they dealt with MPD in their primary analysis. Among methods actually implemented to deal with MPD, most authors use only available data, thus excluding MPD from the analysis. Seldom do investigators apply statistical approaches to impute or taking into account of MPD nor conduct sensitivity analysis to address the impact of it. A comprehensive knowledge synthesis summarizing current available statistical approaches and its relative merits, as well as the current used methods in RCTs provide clear implications on how the practise of using methods to handle continuous MPD should shift in individual RCTs. Trialists should use mixed models and robust regressions and avoid using last observation caring forward method.
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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 | 0.684 | 0.907 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".