Biased odds ratios from dichotomization of age
Bibliographic record
Abstract
Dichotomizing a continuous variable is known to result in the loss of information, lower statistical power, and lower reliability. In many epidemiological studies, age is a scaled (continuous) variable prior to statistical analyses; however, despite pleas from methodologists, researchers frequently dichotomize age in their data analysis without an appropriate rationale. Using simulated case-control data, we show that dichotomizing age generally will lead to a biased odds ratio (OR). When age was a confounder (potentially representing common causes of risks and outcomes), including age as a scaled variable (whether the age effect was linear or non-linear in the logit), provided satisfactory control, whereas when age was categorized, the estimated risk factor effect was biased. We also demonstrate that the further the cutpoint is from the median age, the greater the increase in the OR; thus, in cases where age dichotomization is warranted, researchers are cautioned not to allow the size of the empirical OR to influence their choice of cutpoint. Recommendations are made for analysing age in epidemiological data and interpretation of empirical findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".