A new protocol for evaluating putative causes for multiple variables in a spatial setting, illustrated by its application to European cancer rates
Bibliographic record
Abstract
We introduce a statistical protocol for analyzing spatially varying data, including putative explanatory variables. The procedures comprise preliminary spatial autocorrelation analysis (from an earlier study), path analysis, clustering of the resulting set of path diagrams, ordination of these diagrams, and confirmatory tests against extrinsic information. To illustrate the application of these methods, we present incidence and mortality rates of 31 organ- and sex-specific cancers in Europe; these rates vary markedly with geography and type of cancer. Additionally, we investigated three factors (ethnohistory, genetics, and geography) putatively affecting these rates. The five variables were correlated separately for the 31 cancers over European reporting stations. We analyzed the correlations by path analysis, k-means clustering, and nonmetric multidimensional scaling; coefficients of the 31 path diagrams modeling the correlations vary substantially. To simplify interpretation, we grouped the diagrams into five clusters, for which we describe the differential effects of the three putative causes on incidence and mortality. When scaled, the path coefficients intergrade without marked gaps between clusters. Ethnic differences make for differences in cancer rates, even when the populations tested are ancient and complex mixtures. Path analysis usefully decomposes a structural model involving effects and putative causes, and estimates the magnitude of the model's components. Smooth intergradation of the path coefficients suggests the putative causes are the results of multiple forces. Despite this continuity of the path diagrams of the 31 cancers, clustering offers a useful segmentation of the continuum. Etiological and other extrinsic information on the cancers map significantly into the five clusters, demonstrating their epidemiological relevance.
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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 | 0.095 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".