Modeling missing binary outcome data in a successful web‐based smokeless tobacco cessation program
Bibliographic record
Abstract
AIM: To examine various methods to impute missing binary outcome from a web-based tobacco cessation intervention. DESIGN: The ChewFree randomized controlled trial used a two-arm design to compare tobacco abstinence at both the 3- and 6-month follow-up for participants randomized to either an enhanced web-based intervention condition or a basic information-only control condition. SETTING: Internet in the United States and Canada. PARTICIPANTS: Secondary analyses focused upon 2523 participants in the ChewFree trial. MEASUREMENTS: Point-prevalence tobacco abstinence measured at 3- and 6-month follow-up. FINDINGS: The results of this study confirmed the findings for the original ChewFree trial and highlighted the use of different missing-data approaches to achieve intent-to-treat analyses when confronted with substantial attrition. The use of different imputation methods yielded results that differed in both the size of the estimated treatment effect and the standard errors. CONCLUSIONS: The choice of imputation model used to analyze missing binary outcome data can affect substantially the size and statistical significance of the treatment effect. Without additional information about the missing cases, they can overestimate the effect of treatment. Multiple imputation methods are recommended, especially those that permit a sensitivity analysis of their impact.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".