Simulating results from trials of sigmoidoscopy screening using the OncoSim microsimulation model
Bibliographic record
Abstract
Projection of the effect of cancer screening interventions are frequently conducted using complex simulation models. It is important that such models demonstrate their ability to replicate observational results on the effect of screening. We present results using the OncoSim-CRC microsimulation model to replicate results from four randomized trials (RCTs) of sigmoidoscopy screening for colorectal cancer (CRC). The published results of four RCTs of sigmoidoscopy were reviewed. Two key outcomes were identified: the intention-to-treat hazard ratios (HR) for CRC incidence and CRC mortality for the screening versus control arms. Each RCT study arm was simulated within OncoSim-CRC using the study specific entry criteria, follow-up and observed participation and compliance rates. The ratio of predicted cases (deaths) between intervention arm and control arm was used to estimate the HRs. The RCTs differed in the implementation of sigmoidoscopy screening and only one (PLCO) used more than one cycle. All four RCTs found significant reductions, HR <1, in CRC incidence (range 0.77–0.82) and three for CRC mortality (range 0.69–0.78). The four study cohorts were successfully simulated to match the age and sex structure and length of follow-up of the study cohorts. Each OncoSim-CRC trial-specific predicted reduction fell within the confidence intervals for the observed HR for CRC incidence and CRC mortality for the corresponding trial. The predicted ranges of HRs for incidence was 0.74–0.82 and for mortality was 0.66–0.76 for the four trials. OncoSim-CRC predicted reductions in CRC incidence and mortality agreed well with observed in RCTs of sigmoidoscopy screening.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".