Recovering the raw data behind a non-parametric survival curve
Bibliographic record
Abstract
BACKGROUND: Researchers often wish to carry out additional calculations or analyses using the survival data from one or more studies of other authors. When it is not possible to obtain the raw data directly, reconstruction techniques provide a valuable alternative. Several authors have proposed methods/tools for extracting data from such curves using a digitizing software. Instead of using a digitizer to read in the coordinates from a raster image, we propose directly reading in the lines of the PostScript file of a vector image. METHODS: Using examples, and a formal error analysis, we illustrate the extent to which, with what accuracy and precision, and in what circumstances, this information can be recovered from the various electronic formats in which such curves are published. We focus on the additional precision, and elimination of observer variation, achieved by using vector-based formats rendered by PostScript, rather than the lower resolution image-based formats that have been analyzed up to now. We provide some R code to process these. RESULTS: If the raster-based images are available, one can reliably recover much of the original information that seems to be 'hidden' beneath published survival curves. If the original images can be obtained as a PostScript file, the data recovered from it can then be either input into these tools or processed directly. We found that the PostScript used by Stata discloses considerably more of the data hidden behind survival curves than that generated by other statistical packages. CONCLUSIONS: When it is not possible to obtain the raw data from the authors, reconstruction techniques are a valuable alternative. Compared with previous approaches, one advantage of ours is that there is no observer variation: there is no need to repeat the digitization process, since the extraction is completely replicable.
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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 | 0.048 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".