Cutoff Value for Wilcoxon-Mann-Whitney Test by Minimum P-value: Application to COVID-19 Data
Bibliographic record
Abstract
Dependent and independent variables may appear uncorrelated when analyzed in full range in medical data. However, when an independent variable is divided by the cutoff value, the dependent and independent variables may become correlated in each group. Furthermore, researchers often convert independent variables of quantitative data into binary data by cutoff value and perform statistical analysis with the data. Therefore, it is important to select the optimum cutoff value since performing statistical analysis depends on the cutoff value. Our study determines the optimal cutoff value when the data of dependent and independent variables are quantitative. The piecewise linear regression analysis divides an independent variable into two by the cutoff value, and linear regression analysis is performed in each group. However, the piecewise linear regression analysis may not obtain the optimal cutoff value when data follow a non-normal distribution. Unfortunately, medical data often follows a non-normal distribution. We, therefore, performed theWilcoxon-Mann-Whitney (WMW) test with two-sided for all potential cutoff values and adopted the cutoff value that minimizes the P-value (called minimum P-value approach). Calculating the cutoff value using the minimum P-value approach is often used in the log-rank and chi-squared test but not the WMW test. First, using Monte Carlo simulations at various settings, we verified the performance of the cutoff value for the WMW test by the minimum P-value approach. Then, COVID-19 data were analyzed to demonstrate the practical applicability of the cutoff value.
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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 | 0.016 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".