Predictors, Outcomes, and Statistical Solutions of Missing Cases in Web-Based Psychotherapy: Methodological Replication and Elaboration Study
Bibliographic record
Abstract
BACKGROUND: Missing cases present a challenge to our ability to evaluate the effects of web-based psychotherapy trials. As missing cases are often lost to follow-up, less is known about their characteristics, their likely clinical outcomes, or the likely effect of the treatment being trialed. OBJECTIVE: The aim of this study is to explore the characteristics of missing cases, their likely treatment outcomes, and the ability of different statistical models to approximate missing posttreatment data. METHODS: A sample of internet-delivered cognitive behavioral therapy participants in routine care (n=6701, with 36.26% missing cases at posttreatment) was used to identify predictors of dropping out of treatment and predictors that moderated clinical outcomes, such as symptoms of psychological distress, anxiety, and depression. These variables were then incorporated into a range of statistical models that approximated replacement outcomes for missing cases, and the results were compared using sensitivity and cross-validation analyses. RESULTS: =60.8%) and the rate of symptom change. Low treatment adherence, in particular, was associated with increased odds of presenting as missing cases during posttreatment assessment (eg, odds ratio 161.1:1) and, at the same time, attenuated the rate of symptom change across anxiety (up to 28% of the total symptom with 48% reduction effect), depression (up to 41% of the total with 48% symptom reduction effect), and psychological distress symptom outcomes (up to 52% of the total with 37% symptom reduction effect) at the end of the 8-week window. Reflecting this pattern of results, statistical replacement methods that overlooked the features of treatment adherence and baseline severity underestimated missing case symptom outcomes by as much as 39% at posttreatment. CONCLUSIONS: The treatment outcomes of the cases that were missing at posttreatment were distinct from those of the remaining observed sample. Thus, overlooking the features of missing cases is likely to result in an inaccurate estimate of the effect of treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.575 | 0.795 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".