Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial
Bibliographic record
Abstract
The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".