Characteristics, consent patterns, and challenges of randomized trials using the Trials within Cohorts (TwiCs) design - A scoping review
Bibliographic record
Abstract
OBJECTIVES: Trials within Cohorts (TwiCs) is a pragmatic design approach that may overcome frequent challenges of traditional randomized trials such as slow recruitment, burdensome consent procedures, or limited external validity. This scoping review aims to identify all randomized controlled trials using the TwiCs design and to summarize their design characteristics, ways to obtain informed consent, output, reported challenges and mitigation strategies. STUDY DESIGN AND SETTING: Systematic search of Medline, Embase, Cochrane, trial registries and citation tracking up to December 2022. TwiCs were defined as randomized trials embedded in a cohort with postrandomization consent for the intervention group and no specific postrandomization consent for the usual care control group. Information from identified TwiCs was extracted in duplicate from protocols, publications, and registry entries. We analyzed the information descriptively and qualitatively to highlight methodological challenges and solutions related to nonuptake of interventions and informed consent procedure. RESULTS: We identified a total of 46 TwiCs conducted between 2005 and 2022 in 14 different countries by a handful of research groups. The most common medical fields were oncology (11/46; 24%), infectious diseases (8/46; 17%), and mental health (7/46; 15%). A typical TwiCs was investigator-initiated (46/46; 100%), publicly funded (36/46; 78%), and recruited outpatients (27/46; 59%). Excluding eight pilot trials, only 16/38 (42%) TwiCs adjusted their calculated sample size for nonuptake of the intervention, anticipating a median nonuptake of 25% (interquartile range 10%-32%) in the experimental arm. Seventeen TwiCs (45%) planned analyses to adjust effect estimates for nonuptake. Regarding informed consent, we observed three patterns: 1) three separate consents for cohort participation, randomization, and intervention (17/46; 37%); 2) combined consent for cohort participation and randomization and a separate intervention consent (10/46; 22%); and 3) consent only for cohort participation and intervention (randomization consent not mentioned; 19/46; 41%). CONCLUSION: Existing TwiCs are globally scattered across a few research groups covering a wide range of medical fields and interventions. Despite the potential advantages, the number of TwiCs remains small. The variability in consent procedures and the possibility of substantial nonuptake of the intervention warrants further research to guide the planning, implementation, and analysis of TwiCs.
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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 | 0.785 | 0.915 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.026 | 0.040 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".